We are very proud and happy to present DataCleaner 3.5, which has just been released!

With the 3.x branch of DataCleaner we set forth on a mission to deliver monitoring, scheduling and management of your data quality directly in your browser. And now with the new release, we are building upon this platform to deliver an even richer feature set, a comfortable user experience and massive scalability through clustering and cloud computing.

To be more precise, these are the major stories that we've worked on for the DataCleaner 3.5 release:

Connectivity to Salesforce and SugarCRM

One of the most important sources of data is usually a company's CRM system. But it is also one of the more troublesome data sources if you look at the quality. For this reason we've made it easier to get the data out of these CRM systems and into DataCleaner! You can now use your Salesforce.com or your local SugarCRM system as if it was a regular database. Start by profiling the customer data to get an overview. But don't stop there - you can even use DataCleaner to also update your CRM data, once it is cleansed. More details are available in the brand new focus article about CRM data quality.

Wizards and other user experience improvements

The DataCleaner monitor is our main user interface going forward. So we want the experience to be at least as pleasant, flexible and rich as the desktop application. To meet this goal, we've made many user interface and user experience improvements, amongst others:

• Several wizards are now available for registering datastores; including file-upload to the server for CSV files, database connection entry, guided registration of Salesforce.com credentials and more.
• The job building wizards have also been extended with several enhanced features; Selection of value distribution and pattern finding fields in the Quick analysis wizard, a completely new wizard for creating EasyDQ based customer cleansing jobs and a new job wizard for firing Pentaho Data Integration jobs (read more below).
• You can now ad-hoc query any datastore directly in the web user interface. This makes it easy to get quick or sporadic insights into the data without setting up jobs or other managed approaches of processing the data.
• Once jobs or datastores are created, the user is guided to take action with the newly built object. For instance, you can very quickly run a job right after it's built, or query a datastore after it is registered.
• Administrators can now directly upload jobs to the repository, which is especially handy if you want to hand-edit the XML content of the job files.
• A lot of the technical cruft is now hidden away in favor of showing simple dialogs. For instance, when a job is triggered a large loading indicator is shown, and when finished the result will be shown. The advanced logging screen that was previously there can still be displayed upon clicking a link for additional details.

Distributed execution of jobs

To keep up with the massive amounts of data that many organizations are juggling with today, we had to take a critical look at how we process data in DataCleaner. Although DataCleaner is among the fastest data processing tools, it was previously limited to running on a single machine. For a long time we've been working on a major architecture change that enabled distribution of a DataCleaner job's workload over a cluster of machines. With this new approach to data processing, DataCleaner is truly fit for data quality on big data. More details are available in the documentation section.

Data visualization extension

Data profiling and data visualization do share some common interests - both are disciplines that help you understand the story that your data is telling. There are obviously also some differences, mainly being that data profiling is more targeted at identifying issues and exceptions rather than deriving or measuring business objectives. But confronted with visualization tools we've realized that sometimes there's a lot of profiling value in progressively visualizing data. For instance, a scatter plot can easily help you identify the numerical outliers of your datasets. This idea gave fuel to the idea of a visualization extension to DataCleaner. Therefore DataCleaner now also let's you do basic visualization tasks to aid you in your data quality analysis.

National identifiers extension

A very common issue in data quality projects is to validate national identifiers, such as social security numbers, EAN codes and more. In our commercial editions of DataCleaner, we now offer a wide range of validation components to check such identifiers.

Custom job engines

We've made the ultimate modularization of the DataCleaner monitoring system: The engine itself is a pluggable module. While we do encourage to use DataCleaner's engine as the primary vehicle for execution in DataCleaner monitor, it is not obligatory anymore. You can now schedule and monitor (both in terms of metric monitoring and history management) other types of jobs. For instance, you can provide your own piece of Java code and have it scheduled to run in DataCleaner monitor using the regular web user interface.

Pentaho job scheduling and execution

One major example of a pluggable job engine was introduced that we think deserves special attention: You can now invoke and monitor execution metrics of Pentaho Data Integration transformations. DataCleaner monitor by default ships with this job engine extension which connects to the Pentaho DI server ("Carte") and supervises the execution and result gathering of it. After execution you can track your Pentaho transformations in the timeline views of the monitoring dashboard, just like other metrics. For larger deployments of DataCleaner it may be convenient with dedicated ETL-style jobs in your data quality solution, and with this extension we provide an integration with a leading open source solution for just that. More details are available in the documentation section.

... And a whole lot more!

There's even a lot more to the 3.5 release than what is posted in these highlights. Take a look at the milestone page on the bugtracker for a more thorough listing of improvements made.

A non-functional aspect of DataCleaner is the reference documentation, which we've also done a lot to update. Additionally all the documentation pages now have a commenting feature, so that you can ask questions or provide feedback to the help that is in there. We'll be continuously providing more and more content in the documentation and on the website for you to get the best resources at your hands.

... Stay tuned for more!

On the front page of the DataCleaner website we'll be posting "feature focus" articles in the weeks to come. Please help us spread the word by promoting the release and the articles to your friends, colleagues and whom else might be interested.

We're happy to announce another release of DataCleaner - version 3.1.2. This version is a minor improvement and bugfix release.

So what's new? Here's the summary:

• We've added a web service in the monitoring application for getting a (list of) metric values. This makes the monitoring even more usable as a key infrastructure component, as a way to monitor data (quality) and expose the results to third party applications. Read more in the documentation.

• The 'Table lookup' component has been improved by adding join semantics as a configurable property. Using the join semantics you can tweak if you wish the lookup to work semantically like a LEFT JOIN or an INNER JOIN.
• The EasyDQ components have been upgraded, adding further configuration options and a richer deduplication result interface.
• Performance improvements have been a specific focus of this release. Improvements have been made in the engine of DataCleaner to further utilize a streaming processing approach in certain corner cases which was not covered previously.

For more details on the individual issues worked on, visit our milestone page.

The 3.1.2 release should be a drop-in replacement of other 3.x releases, so go download and upgrade now!

We've added two additional transformations to the EasyDQ additionals package, provided by Human Inference.

The two transformations are:

• Country standardization. This allows you to get direct access to EasyDQ's country standardizer and unify different spellings, formats and more of country names and codes.
• Similarity evaluator. This feature provides a low-level function for comparing two sets of values. For instance, if you've done a reference data lookup, you will often be interested in knowing if the result of the lookup matches the data that you already have. Using the similarity evaluator you can easily compare the incoming and resulting values and thereby make visible the improvements and changes you are doing to your data with these lookups.

The extension is available, like always, in the Extensions section of the website.

We have a nice little release for you today, which contains the usual maintenance fixes, but also some improvements and minor new features. DataCleaner 3.1.1 is ready for download as of now.

Let's dive into the news ...

• The date and time related analysis options have been expanded, adding distribution analyzers for week numbers, months and years. All analyzers related to date and time are now grouped within a submenu called "Date and time" under "Analyze".
• An optional "descriptive statistics" option has been added to the Number analyzer and the Date/time analyzer. This option adds additional metrics to the results of these analyzers, such as Median, Skewness, percentiles and Kurtosis. These metrics are optional since their memory footprint is somewhat larger than the existing metrics.
• The lines in the timeline charts of the monitoring web application now have small dots in them. This is especially useful for charts with few (or even only one) observations in them - to point out exactly where the observation points are.

• The query parser when invoking ad-hoc queries have also been substantially improved. Now queries can contain DISTINCT clauses, *-wildcards, subqueries and are fault-tolerant towards text-case issues.
• Two new transformers have been added for generating UUIDs and for generating timestamps.

For the full list of improvements, go to the milestone page on our bugtracker.

We hope you enjoy this release, and go get it immediately from the downloads page.

Human Inference is happy to announce that DataCleaner 3.1 has been released and that it is available for download now! With DataCleaner 3.1 we’ve really focused on usability and day-to-day requirements of both the DataCleaner desktop data profiling application, and the web application for continuous data quality monitoring. Features that we feel really aids the user to do what he wants to do. Here’s a summary of what has been done.

Metric formulas – elaborated Data Quality KPIs
It is now possible to build much more elaborate Data Quality KPIs in DataCleaner’s monitoring web application. The user interface allows you to build complex formulas in a spreadsheet-like formula style; using variables collected by DataCleaner jobs.

Metric formulas can combine any number of metrics, constants and operations, as long as it can be expressed in a mathematical equation.

For instance – measure the rate of duplicate records in percentage of the total record count. Or measure the amount of product codes that conform to a set of multiple string patterns.

Ad-hoc querying – of any datastore
With DataCleaner 3.1 you can now perform ad-hoc queries to any datastore! Queries can be expressed in plain SQL and will be applied to databases as well as files, NoSQL databases and more, providing a truly helpful query mechanism to extend into your discovery and data profiling experience.

The query option is also available through a web service to monitoring users with the ADMIN role. The query is provided as a HTTP parameter or POST body, and the result is provided as an XHTML table.

Value matcher – a new analysis option
Often times you have a firm idea on which values should be allowed and expected for a particular field. In DataCleaner there’s always been the Value Distribution analysis option which would help you assert your assumptions. In DataCleaner 3.1 though, you have a more precise offering – the Value matcher. This analysis option allows you to specify a set of expected values and then perform a value distribution like analysis, specifically to validate and identify unexpected values.


Copying, deleting and management of jobs
Management of jobs and results in the DataCleaner monitor application has been improved greatly. You can now click a job in the Scheduling page of the monitor, and find management options available for operations such as renaming, copying, deleting and more. Each operation respects the linkages to other artifacts in the monitor, such as analysis results, schedules and more. This means that management of the monitoring repository has become a lot easier and mature.


Manage data quality history
Sometimes you’re facing situations where you actually want to do monitoring with historic data! It might be that you have historic dumps or backups of databases, which you wish to show and tell the story of. You can now do the analysis of this historic data, upload it to the DataCleaner monitor, and using a new web service, set a historic data of that particular analysis result. This means that your timelines will properly plot the results using their intended date, but with the results that you’ve collected maybe at a later point in time.

Clustered scheduler support (EE only)
The scheduler of DataCleaner monitor has been externalized, so that it can be replaced by the means of simple configuration. In the Enterprise Edition (EE) of DataCleaner, we provide a clustered scheduler, providing the ability to load balance and distribute your executions across a cluster of machines.

Single-signon (SSO) using CAS (EE only)
In the Enterprise Edition (EE) of DataCleaner we now provide a single-signon option for the monitor application. Now DataCleaner can be an integrated part of your IT infrastructure, also security-wise.

... And a lot more
The above is just a summary. More than thirty issues have been resolved in this release. We have solved several requests coming from the forums and community, and we encourage everyone to use this medium as a vehicle for change. We’re very happy to make the development of DataCleaner be heavily influenced by the streams in the community.

• For a full list of changes in DataCleaner 3.1, go to the milestone report in our issue tracker.
• To download DataCleaner, go to the downloads page and get your copy now.
• To learn more, get the documentation, watch screenshots or the webcast demonistrations.

Neopost, the European leader and number two worldwide supplier of mailroom solutions, today announced that it has completed the acquisition of Human Inference.


With products and services marketed in 90 countries and subsidiaries in 29 countries, the Neopost Group has 5,900 employees all over the world, 1,300 sales representatives and 450 R&D engineers.

As the postal sector is undergoing major changes, Neopost is anticipating the needs of its customers by bringing new services and technological innovation to the market. Therefore, Neopost has been acquiring multiple companies; several components have been added to the mix, all relating to the topic of communications between people. Satori software, a US-based data quality vendor has been part of the mix for a while and GMC, a Swiss-based Customers Communications Management vendor has been acquired recently. For Neopost, Human Inference is a strategic acquisition helping them to create the portfolio that they need to bring future-proof solutions to the market and their current customers.

Neopost has chosen Human Inference for its strong expertise, its proven solutions and its splendid reputation. We will continue to operate independently, with an unchanged management team. Our core values will remain to be our guidelines. Our customers will be able to enjoy an even broader set of solutions, which we believe will be in perfect fit with our single customer view-strategy. In addition, Human Inference will be able to use the sales and distribution channels of Neopost, which will give us the opportunity to service new markets.

Human Inference CEO Winfried van Holland said: "We are very pleased to join Neopost. This offers us access to new markets and the support and relationships from a large organization. Our solutions fit perfectly in Neopost’s portfolio. This way Neopost customers, Human Inference customers, common customers and the DataCleaner community members will benefit from a broader range of solutions allowing them to reduce their risk, become more efficient and grow their profit by deploying a single customer view."

See here the press release on the Neopost website.

Who will post the best content for use in DataCleaner?

Human Inference is announcing a competition for the DataCleaner community. The goal is to provide the best contribution for our favourite open source data quality tool.

What kind of contributions?
Submitted content can be of many forms:

• Educational content like tutorials, videos etc.
• Regular Expressions for the RegexSwap.
• DataCleaner extensions for the ExtensionSwap.
• Reference data for inclusion in the tool.
• Use case descriptions – tell the community about your experiences.
• Third party tool integration.

Prize
We do cherish everything in the community being free. But we will also be giving a nice prize to the winner with the best submission. With the prize we want to encourage further creativity and technological discovery. So the winner will have the option of either a Android tablet of their own choice (for instance the new Google Nexus 7) or a Lego Mindstorms programmable and modular robot system.

We want to send a special thank you to the CUBRID affiliates program for helping in sponsoring the prizes.



In addition to winning a prize, all submissions will be reviewed and mentioned on the DataCleaner website.

Participating
Content must be submitted before Christmas (December 24) 2012. Post a comment on this discussion topic to tell the community where and how to retrieve your submitted content. We also encourage people to join our Google+ community hangouts where authors will be invited to present their contributions.

Submitted contributions (so far)
Here's a list of the submitted contributions in the contest so far:

• Pentaho Data Integration auto-profiling generator, by Alex Meadows.
• Groovy DataCleaner, by Kasper Sørensen (not applicable for the prize).
• Scala in DataCleaner, by Ankit Kumar.

Dear DataCleaner users and developers,

We have a new release for you today, version 3.0.3 of DataCleaner. Grab it before your neighbor at the download page.

The focus of this release has been stability, performance and convenience for monitoring repository maintenance. Thus, the new and improved list follows:

• We've added a service for renaming jobs in the monitoring repository. You can access this as a RESTful web service or interactively in the UI:

• A web service was added for changing the historic date of an analysis result in the monitoring repository. This is convenient if you have historic dumps of data that you wish to include in a timeline.
• The documentation has been updated with more elaborate descriptions of the web services available for repository navigation, job invocation and more.
• The login dialog in the desktop application had a low-level version conflict, which caused it to be unusable. This has been fixed.
• The web application has been made compatible with legacy JSF containers, making the range of applicable Java Webservers wider.
• Caching of configuration in the web application was greatly improving, leading to faster page load and job initialization times.

We hope you enjoy this release. It should be 100% backwards compatible with other 3.x releases, so we encourage everyone to upgrade.

We are happy to invite everyone to a new initiative: The DataCleaner community hangout. The community hangout is a chance for users and developers of DataCleaner to meet face-to-face online every once in a while.

The last couple of weeks we've been trying out the new concept with a limited amount of people, and we are now ready to make the invite to everyone with an interest!



The date of the next hangout is Tuesday the 6th of November at 10:00 CET. Please be aware of any timezone differences.

The hangouts are happening on Google+ on a semi-weekly basis. The frequency will be adjusted according to the interest in the community. To kick it off we will from the Human Inference side provide some presentations and discussion topics for the first couple of sessions. But the idea is also to engage users and friends to join the hangouts with their own input.

For the next hangout, project founder Kasper Sørensen will be demoing the new monitoring web application, and how it relates to the traditional desktop application.

For more information, go to our Google+ page and sign up to the next hangout.

It's friday afternoon and we have a little weekend gift to share with everyone. The last couple of weeks we've been working on a number of small but nice feature improvements and minor bugfixes in DataCleaner. These are now all available in DataCleaner version 3.0.2 - go grab it at the downloads page.

Here's a wrap-up of the work that we've done:

• When triggering a job in the monitoring web application, the panel auto-refreshes every second to get the latest state of the execution.
• File-based datastores (such as CSV or Excel spreadsheets) with absolute paths are now correctly resolved in the monitoring web application.
• The "Select from key/value map" transformer now supports nested select expressions like "Address.Street" or "orderlines[0].product.name".
• The table lookup mechanism have been optimized for performance, using prepared statements when running against JDBC databases.
• Administrators can now download file-based datastores directly from the "Datastores" page.
• Exception handling in the monitoring web application has been improved a bit, making the error messages more precise and intuitive.

We hope you enjoy the new version. It should be a drop-in replacement of previous DataCleaner 3 releases, so no need to wait, upgrade now.

If you're using DataCleaner and think it would be fun to meet up with team members from Human Inference who work on the product, as well as consultants and other users of it - join our new Google+ page from where we will start doing community hangouts and thereby invite you to share ideas, questions and good vibes.

Thank you to all for the positive attention about our recent DataCleaner 3 release. With this information we've been able to quickly and effectively identify a few minor improvements and have introduced these in a new release: Version 3.0.1.

The primary bugfix in this release was about restoring the mapping of columns and specific enumerable categorizations. For instance in the new Completeness analyzer, we found that after reloading a saved job, the mapping was not always correct.



Furthermore a few internal improvements have been made, making it easier to deploy the DataCleaner monitor web application in environments using the Spring Framework.

Last but not least, the visualization settings in the desktop application have been improved by automatically taking a look at the job being visualized and toggling displayed artifacts based on the screen size and amount of details needed to show it nicely.

DataCleaner 3.0.1 is available for download on our downloads page. We wish you good luck cleaning your data, and enjoy the software.

Dear friends, users, customers, developers, analysts, partners and more!

After an intense period of development and a long wait, it is our pleasure to finally announce that DataCleaner 3 is available. We at Human Inference invite you all to our celebration! Impatient to try it out? Go download it right now!

So what is all the fuzz about? Well, in all modesty, we think that with DataCleaner 3 we are redefining 'the premier open source data quality solution'. With DataCleaner 3 we've embraced a whole new functional area of data quality, namely data monitoring.

Traditionally, DataCleaner has its roots in data profiling. In the former years, we've added several related additional functions:- transformations, data cleansing, duplicate detection and more. With data monitoring we basically deliver all of the above, but in a continuous environment for analyzing, improving and reporting on your data. Furthermore, we will deliver these functions in a centralized web-based system.

So how will the users benefit from this new data monitoring environment? We've tried to answer this question using a series of images:

The monitoring web application is a fully fledged environment for data quality, covering several functional and non-functional areas:

• Display of timeline and trends of data quality metrics
• Centralized repository for managing and containing jobs, results, timelines etc.
• Scheduling and auditing of DataCleaner jobs
• Providing web services for invoking DataCleaner transformations
• Security and multi-tenancy
• Alerts and notifications when data quality metrics are out of their expected comfort zones.

Naturally, the traditional desktop application of DataCleaner continues to be the tool of choice for expert users and one-time data quality efforts. We've even enhanced the desktop experience quite substantially:

• There is a new Completeness analyzer which is very useful for simply identifying records that have incomplete fields.
• You can now export DataCleaner results to nice-looking HTML reports that you can give to your manager, or send to your XML parser!
• The new monitoring environment is also closely integrated with the desktop application. Thus, the desktop application now has the ability to publish jobs and results to the monitor repository, and to be used as an interactive editor for content already in the repository.
• New date-oriented transformations are now available: Date range filter, which allows you to subset datasets based on date ranges, and format date, which allows to format a date using a date mask.
• The Regex Parser (which was previously only available through the ExtensionSwap) has now been included in DataCleaner. This makes it very convenient to parse and standardize rich text fields using regular expressions.
• There's a new Text case transformer available. With this transformation you can easily convert between upper/lower case and proper capitalization of sentences and words.
• Two new search/replace transformations have been added: Plain search/replace and Regex search/replace.
• The user experience of the desktop application has been improved. We've added several in-application help messages, made the colors look brighter and clearer and improved the font handling.

More than 50 features and enhancements were implemented in this release, in addition to incorporating several hundreds of upstream improvements from dependent projects.

We hope you will enjoy everything that is new about DataCleaner 3. And do watch out for follow-up material in the coming weeks and months. We will be posting more and more online material and examples to demonstrate the wonderful new features that we are very proud of.

We are celebrating the plans to build a version 3.0 of DataCleaner, where we hope to be pushing the limits of what you can expect from your open source data quality applications. A few big themes for version 3.0 has already been decided:

• A data quality monitoring web application.
• A multi-tenant repository for data quality artifacts (jobs, profiling results, configurations, datastore definitions etc.)
• Being able to edit data (in the desktop application).
• Wizards to guide users through their first-time user experience with DataCleaner.

Go read Kasper Sørensen's blog post about the data quality monitoring application, which underlines the general direction and scope of the release!

Human Inference is arranging two sessions of free online training, for people wishing to learn about data profiling, data cleansing, deduplication and more ... And of course, how you do it all in DataCleaner.

The two sessions are (click a link to read more and to register):

• May 29th, free training for European and APAC timezones.
• June 7th, free training for US timezones.

We hope to see a lot of people join in on the training, which we hope will be a good and fun event, where you'll learn about data quality, data quality tools and get a chance to say hello to other community members.

DataCleaner 2.5.2 has just been released. The DataCleaner 2.5.2 release is a minor release, but does contain some significant feature improvements and enhancements. Here's a walkthrough of this release:

Apache CouchDB support

We've added support for the NoSQL database Apache CouchDB. DataCleaner supports both reading from, analyzing and writing to your CouchDB instances.



Update table writer

Following our previous efforts to bring ETLightweight-style features into DataCleaner, we've added a writer which updates records in a table. You can use this for example to insert or update records based on specific conditions.

Like the Insert into table writer, the new DataCleaner Update table writer is not restricted to SQL-based databases, but any datastore type which supports writing (currently relational databases, CSV files, Excel spreadsheets, MongoDB databases and MongoDB databases), but the semantics are the same as with a traditional UPDATE TABLE statement in SQL.

Drill-to-detail information saved in result files

When using the Save result feature of DataCleaner 2.5, some users experienced that their drill-to-detail information was lost. In DataCleaner 2.5.2 we now also persist this information, making your DQ archives much more valuable when investigating historic data incidents.

Improved EasyDQ error handling

The EasyDQ components have been improved in terms of error handling. If a momentary network issue occurs or another similar issue causes a few records to fail, the EasyDQ components will now gracefully recover and most importantly - your batch work will prevail even in spite of errors.

Table mapping for NoSQL datastores

Since CouchDB and MongoDB are not table based, but have a more dynamic structure we provide two approaches to working with them: The default, which is to let DataCleaner autodetect a table structure, and the advanced which allows you to manually specify your desired table structure. Previously the advanced option was only available through XML configuration, but now the user interface contains appropriate dialogs for doing this directly in the application.

We hope you enjoy the new 2.5.2 version of DataCleaner. Go get it now at the downloads page.

Today we announce an exciting new partnership with Pentaho, the leading open source Business Intelligence and Business Analytics stack! For the past years Human Inference, members of the DataCleaner community and Pentaho have been in close contact to design a new data quality package for the Pentaho Suite. DataCleaner plays a key part in this new solution.

DataCleaner’s integration in Pentaho is primarily focused on the open source ETL product, Pentaho Data Integration (aka Kettle). Pentaho and Human Inference will be running a joint webinar on May 10th to tell everyone about all the new features (register for the webinar here), but until then – here’s a summary!

Profile ETL steps using DataCleaner

When working with ETL you often find yourself asking what kinds of values to expect for a particular transformation. With the data quality package for Pentaho we offer a unique integration of profiling and ETL: Simply right click any step in your transformation, select ‘Profile’, and it will start up DataCleaner with the data available for profiling, which the step produces! Not only is this a great feature for Pentaho Data Integration, it is also a one-of-a-kind in the ETL space. We are very excited to see this great use of embedding DataCleaner into other applications.



Execute DataCleaner job

Another great feature in the Pentaho data quality package is that you now orchestrate and execute DataCleaner jobs using Pentaho Data Integration. This makes it significantly easier to manage scheduled executions, data quality monitoring and orchestration of multiple DataCleaner jobs. Mix and match DataCleaner’s DQ jobs with Kettle’s transformations and you’ve got the best of both worlds.



EasyDQ integration

Additionally, the data quality package for Pentaho contains the EasyDQ cleansing functions as ETL steps, similar to what you know from their DataCleaner counterparts.


Deduplication and merging via DataCleaner

In addition to embedding DataCleaner for profiling of steps, you can also start up DataCleaner when browsing databases in Pentaho Data Integration. This will create a database connection which is appropriate for more in-depth interactions with the Database. For example, you can use it to find duplicates in your source or destination databases.



For more information:

The press release from Pentaho:
Pentaho announces new Data Quality solution

Installation instructions and information from Pentaho:
Pentaho wiki: Human Inference

Example of using the DataCleaner profiler with Pentaho:
Pentaho wiki: Kettle Data Profiling with DataCleaner

Information about the EasyDQ functions for Pentaho:
EasyDQ Pentaho page

Today we've released DataCleaner 2.5.1. This is a maintenance release with only minor bugfixes and improvements. But nevertheless we encourage users to upgrade!

Here are the news in DataCleaner 2.5.1:

• A bug was fixed in the Table lookup transformation, which caused it to be unable to have multiple output columns.
• CSV file escape characters have been made configurable.
• A minor bug pertaining to empty strings in the Concatenator was fixed.
• Support for the Cubrid database was added.
• The converter transformations was adapted to be able to work on multiple fields, not just single fields.

For more information, please refer to the 2.5.1 milestone in the trac system.

We hope you enjoy the new version of DataCleaner!

Today we announce the general availability of DataCleaner 2.5! This release is the result of months of hard work by the core DataCleaner crew, the EasyDQ group and the community at large.

Let’s get straight to the “What’s new” question. There are plenty of major improvements in this release:

Saving results to disk
With DataCleaner 2.5 you can save, archive and share your analysis results. This is not only a time-saver for those who used to do manual exporting of analysis results, but it is also a means to improve your methodology around handling profiling results, sharing them with colleagues and for archiving historically profiles of your data.



Saving is implemented so that future versions and/or custom solutions can take advantage of the results and potentially use it for scheduled profiling, data quality monitoring and more.

Data structure transformers
With the rise of Big Data and NoSQL databases comes more advanced data structures. In next generation databases we see key/value pairs and list structures that are cumbersome to deal with in tools built for traditional relational data. To solve these issues DataCleaner 2.5 ships with a new set of “data structure” transformers, which allow you to easily wrap and unwrap structures, to be able to get to the parts that you want to analyze or process.



The data structure transformers also include parsers and writers for JSON data, which is one of the more common representations of NoSQL datastructures.

Filters and transformers are now all "Transformations"
Since DataCleaner 2.0 we’ve been pushing the idea of transformers and filters. The strength of these two types of components were evident from a technical perspective, but for the end-user the distinction has shown to be distracting from its main use-case: To process data in a flow of actions. Therefore DataCleaner 2.5 has consolidated these two terms, and made them available in a common metaphor for the user: Transformations. This means that the user will no longer have to look in multiple menus to find the component he is looking for.

New EasyDQ transformations: Merge duplicates and Due diligence check
The EasyDQ on-demand data quality platform team has also been busy. We present to you three new functions and an optional extension for the advanced users.



First is the Merge duplicates transformation. With this transformation you can turn your results from Duplicate detection into merged, golden records! The merge component is designed to handle a hierarchy of criteria when merging to make sure that critieria such as well-formedness, update date and manual overriding is taken into account.

Secondly we’ve introduced two services for Due diligence checks. These are transformations which will help you validate that the people you are engaging business with are not connected to sanction lists of terrorists, narcotics trafficking and other security threats.

These new features, as well as the other EasyDQ functions, are described in detail in the EasyDQ reference documentation.

Lastly, there's a new extension available, the EasyDQ essentials, which we recommend as a handy extra toolkit for those that want to go deep diving into the features of EasyDQ.

Defining datastore properties on the command line
One of the areas that have been heavily enforced in the later releases of DataCleaner is the command line interface. Using this interface you can set up DataCleaner to execute in all environments, in a scheduled or managed fashion. In DataCleaner 2.5 we’ve also made it possible to override datastore properties from the command line. Why? Because it allows you to reuse the same job on different datastore definitions. If you are for example scanning a directory for CSV files, and want to run a DataCleaner job on each file, this is a solution for you. Refer to the documentation for further explanation and examples.

Drill to detail information in value distribution results
The Value distribution analyzer now contains a drill to detail option, to make it possible to see the source records for each value in the distribution. This greatly helps usability when doing explorative data profiling.

Database-specific connection panels
The dialogs for setting up database connections have been enhanced with database-specific connection properties. This makes it a lot easier for the end-user to connect to a database without having to know the details of constructing a connection URL.



Database-specific configuration panels have been created for MySQL, PostgreSQL, Microsoft SQL Server and Oracle. Other database types are supported using the traditional way of connecting, as in previous versions of DataCleaner.

Execution and scheduling of DataCleaner jobs using Pentaho Data Integration
Pentaho Data Integration (PDI, aka. Kettle) is an open source ETL product that the EasyDQ and DataCleaner team has had a lot of interactions with. For the DataCleaner 2.5 release we are now announcing that in next version of Pentaho Data Integration you will be able to execute and schedule DataCleaner jobs using Pentaho’s infrastructure.



While this is not available, released software as of today, we are looking forward to telling you more about this in the near future!

For those still reading, we also did some minor improvements in DataCleaner 2.5:

• We’ve added some number transformations for generating IDs, incrementing numbers and more.
• Implemented a Date range filter, similar to the Number range and String range filters.
• Support for matching against Synonym catalogs in Reference data matcher (which is previously known as the Matching analyzer).
• Now all components have flow visualizations in their configuration panel. This feature helps retain the overview when working with large analysis jobs.
• The sample data (the ‘orderdb’ database) has been reworked to contain better examples of data quality issues.
• User experience improvements; more elegant dialog designs and trimming of window layout.

We hope you all enjoy the new release of DataCleaner 2.5. Please let us know what you think on the forums, or on our LinkedIn group, or on Google Plus, or on Blogger, or tweet it, or...

The EasyDQ on-demand data quality platform, which DataCleaner is integrated with, has released a patch for DataCleaner version 2.4.2. The patch includes a critical bugfix for the Inter-Dataset matching analyzer.

If you're using this functionality, please download the patch and place it in the lib/ folder of DataCleaner. This will automatically apply the fix and matching multiple datasets will be working again.

The patch has also been applied to the Java WebStart version of DataCleaner, so WebStart users will not need to do anything.

We've just released DataCleaner version 2.4.2, which is a bugfix and minor enhancements release. Please update to this latest version, which has a whole bunch of items fixed:

• Database connection can now specify if multiple connections can be made or not. This solves an issue related to databases that did not allow this, and a potential application halt if no more connections was available.

• There's now a separate distribution of DataCleaner specific for Mac OS. Using this version of DataCleaner you'll see a much nicer OS integration than previously.
• Performance of the engine has been improved by providing some job-level metrics as lazy loaded values. For instance, the estimated row count is now lazy loaded, so in situations where this metric is not needed (eg. the command line interface and embedded use of DataCleaner), it will not be calculated.
• The command line interface now has additional options to save the results of an analysis to a file, given a variety of output formats. Saved files can later be opened in the User Interface, allowing for a DIY data quality monitoring solution (see Kasper Sørensen's blog for more details).
• An issue with correct prefixing of table names in INSERT statements was fixed in the downstream dependencies for the "Insert into table" component.

For full details about all changes, check out the trac roadmap for DataCleaner 2.4.2, AnalyzerBeans 0.10 and MetaModel 2.2.1.

As our new years present to all of you, we have a new release of DataCleaner. DataCleaner 2.4.1 is largely a release of bugfixes and minor feature enhancements.

Here's an overview of the improvements we've made:

Feature enhancements:

• Batch loading features we're greatly improved when writing data to database tables. Expect to see many orders of magnitude improvements here.
• Writing to data has been more conveniently made available by adding the options to the window menu.
• You can now easily rename components of a job by double clicking their tabs.
• The Javascript transformer now has syntax coloring, so that your Javascripts are easier to inspect and modify.

Bugfixes:

• When reading from and writing to the same datastore (eg. the DataCleaner staging area) we've made sure that the table cache of that datastore is refreshed. Previously some scenarios allowed you to see an out-of-date view of the tables.
• A potential deadlock when starting up the application was solved. This deadlock was a consequence of an issue in the JVM, but we worked around it by synchronizing all calls to the particular API in Java.

The full list is also available on the DataCleaner 2.4.1 milestone in the roadmap.

The 2.4.1 release should work as a drop-in replacement of DataCleaner 2.4, so we encourage everyone to upgrade. Get it on the downloads page. Happy new year.

Merry christmas! Today we announce the release of DataCleaner 2.4, which marks a huge joint effort by the community and the team at Human Inference to bring together the best ideas of both open source and cloud-based Data Quality.

Here's what's new in DataCleaner 2.4:

EasyDataQuality integration

With DataCleaner 2.4 we've made an alliance with the newly launched EasyDQ.com service, which offers cloud-based Data Quality services. The services provided are:

• Duplicate detection (aka. Deduplication or Fuzzy matching of records), which is free to use for up to 500,000 values.
• Address data validation and cleansing. This allows you to check if addresses exist, if they are correctly formatted and even to suggest corrections in case you have mistakes.
• Name data validation and cleansing. With the Name service, EasyDQ does not only format your names consistently, but also checks for misspellings and interprets the name parts.
• Email and phone validation and cleansing. These services provide checking of email and phone data, making sure that email domains exist, that country codes are correct and much more.

No, these are not open source services, but they are offered at a reasonable price as well as a free starter package, and we thoroughly believe that the integration allows DataCleaner to become a much better tool for those who want it.

New analysis job components

Many of DataCleaner's users have reported that they use DataCleaner as a lightweight ETL tool. This is because we currently support basic reading, transformation and writing capabilities. With 2.4 we've added a few crucial components to add to this use-case where you want to do ad-hoc transformations, data quality checks and actually write the data back to your database.

• Table lookup which allows you to look up any number of values based on any number of conditions. The lookup component has an intelligent caching mechanism and is highly performant. (Docs).
• Insert into table is a new option when writing data. With this option we are making it possible for DataCleaner to not only produce new files, but also to insert records into existing databases. That makes it a much more flexible writing option.

MongoDB support! And a few more...

Another theme in DataCleaner 2.4 is support for the popular NoSQL database MongoDB. The support is offered both as a profiling service (eg. reading and analyzing data), but ALSO for writing data to MongoDB collections, using the Insert into Table component, which makes DataCleaner the first open source tool that offers data flow modelling and ETL functionality for MongoDB! We also improved on a few other datastores:

• Support for MongoDB datastores, which are both readable and writable with DataCleaner. MongoDB uses a schemaless design principle, so you have the choice of either letting DataCleaner auto-detect a virtual schema, or define it yourself. (Docs).
• Added more configuration options to Fixed width value files. Specifically, there is now the option to specify header line number.
• Added support for custom table mapping of XML structures. For large XML files this is a recommended approach, since with a fixed table model, DataCleaner can do SAX-based XML parsing which is much less memory intensive and a lot faster. (Docs).
• The Command Line Interface (Docs) has been further improved, by allowing you to inject job variables from the command line, which makes it possible to parameterize jobs and thereby reuse jobs for different purposes.

Besides these points, a few bugfixes where fixed and some minor features added. For a full list of changes, check out the DataCleaner 2.4 milestone description in trac.

We hope you enjoy DataCleaner 2.4. We built it to be used, so go grab it right away on the downloads page!

There's a new and nice extension ready for you at the ExtensionSwap: The network tools extension.

Network tools can be used to work with IP addresses in data, resolve hostnames and more. Give it a look if you're dealing with network addresses (or eg. email addresses, website visitors etc.) in your data.

Today we announce the release of DataCleaner 2.3. It contains new functionality, usability improvements and technical changes that make it even more useful for your data quality work. Curious? Just read on!

International data support

• If you are working with international data, then you might have different character sets in your data, for example Chinese or Hebrew. We added the Character set distribution analyzer, which is a profiling option that lets you figure out which character sets are used in your data.
• Working with data containing different character sets can be problematic. Using the new Transliterate transformer you can now transliterate strings from different writing systems to Latin characters.
• There is also a new webcast demonstration, focusing on the international data capabilities of DataCleaner 2.3 in the documentation section.

Grouping of analysis results by a secondary column

• The Pattern analyzer is now able to group patterns based on a secondary column. This is useful for analyses like:
  • Get patterns of phone numbers, grouped by country.
  • Get patterns of email username based on email domain.
• Something similar has been done for the Value Distribution analyzer; this allows for analyses such as:
  • Are all city names distinct, when grouped by postal code?
  • What is the distribution of gender within particular customer types?

Improved charts

• The Pattern finder results can now be shown in a chart. This makes the distribution visible and shows how much of a "long tail" of patterns there is.
• The output of the value distribution analyzer has been improved in a couple of areas:
  • The readability of the chart has been improved.
  • It shows the total number of rows and the distinct count over these rows: the number of different values that exist in the rows. This helps in figuring out how often duplicate values exist.
  • If there are empty strings, we use the <BLANK> keyword for it, so that it is easier to recognize them.

Output

• Next to the already existing output formats (CSV files and H2 datastores) we added writing output to Excel spreadsheets.
• After writing to a datastore, it is now possible previewing the output, so that you can check whether the output is according to your expectations.
• It is now also possible to add the output as a new datastore, so that it can be used as input for a new job.

Other improvements

• Documentation has been generally improved. In particular, logging and command line interface descriptions have been added.
• The extension mechanism has been improved by modularizing several pieces of the application and introducing Google Guice as a generally available dependency injection framework for extension developers.
• And of course we did more than twenty small improvements and bug fixes.

We hope you enjoy the new version of DataCleaner, which you can get a copy of on the downloads page.

As stated earlier, Human Inference is dedicated to deliver a rich set of extensions to the DataCleaner community, as well as we are seeing third party interest in contributing to the ExtensionSwap.


Today we've published a new extension which many DataCleaner users will hopefully find useful: The Regex parser.

With this extension you can easily implement your own parsing logic around regular expressions. The idea is that you use a regular expression to identify groups in your strings. These substring groups are extracted from the original value and isolated so you can process them individually. A quite nice application of DataCleaner's transformer mechanism!

For more information on how to create your own extensions, please refer to the DataCleaner develop page.

DataCleaner 2.2 has been released as of today! This is an exciting new version of our Data Quality Analysis (DQA) and Data Profiling application that is now a lot more extensible, embeddable and compliant with new datastores.

Here's a summary of the news in this release:

Extensibility

• The main driver for this release has been a story about extensibility. While releasing the application we are simultaniously releasing a a new DataCleaner website which features an important new area: The ExtensionSwap. The idea of the ExtensionSwap is to allow sharing of extensions to DataCleaner and installation simply by clicking a button in the browser!
• The DataCleaner extension API has been improved a lot in this release, making it possible to create your own transformers, analyzers and filters. If you feel your extensions could be of interest to other users, please share it on the ExtensionSwap and we provide a channel for you to easily distribute it to thousands of users. The Extension API and the ExtensionSwap is further explained in our new webcast demonstration for developers and other techies with an interest.

• We are also releasing a set of initial extensions on the ExtensionSwap: The HIquality Contacts for DataCleaner extension which provides advanced Name, Phone and Email cleansing, based on Human Inferences natural language processing DQ web services. We are also shipping a sample extension which will serve as an example for developers wanting to try out extension development themselves. In the coming months we will make sure to post even more extensions originating from our internal portfolio of tools that we use at Human Inference's knowledge gathering teams.
• In addition to extensibility we are also focusing on embeddability. We want to be able to embed DataCleaner easily into other applications to make profiling and data analysis possible anywhere! We've created a new bootstrapping API which allows applications to bundle DataCleaner and bootstrap it with a dynamic configuration or run it in a "single datastore mode", where the application is tuned towards just inspecting a single datastore (typically defined by the application that embeds DataCleaner). We already have some really interesting cases of embedding DataCleaner in the works - both in other open source applications as well as commercial applications.

Compatibility

• We've added support for analyzing SAS data sets. This is something we're quite proud of as we are, to our knowledge, the first major open source application to provide such functionality, ultimately liberating a lot of SAS users. The SAS interoperability part was created as a separate project, SassyReader, so we expect to see adoption in DataCleaner's complimentary open source communities soon too!

• We've also added support for another type of datastore: Fixed width files. Fixed width files are text files where each column has a fixed width. There is no separator or quote character, like CSV files, instead each line are equal in length and each line will be tokenized according to a set of value lengths.
• An option to "fail on inconsistencies" was added to CSV file and fixed width file datastores. These flags add a format integrity check when using these text file based datastores.
• A bug was fixed, which caused CSV separator settings not to be retained in the user interface, when editing a CSV datastore.
• Japanese and other characters are not supported in the user interface. This "bug" was a matter of investigating available fonts on the system and selecting a font that can render the particular characters. On most modern systems there will be capable fonts available, but on some Unix and Linux branches there might still be limitations.

Other improvements

• The documentation section has been updated! Ever since the initial 2.0 release the documentation have been far behind, but we've finally managed to get it up to date. There are still pieces missing in the docs, but it should definately be useful for basic usage as well as a reference for most topics.
• Application startup time was improved by parallelizing the configuration loading and by delaying the initialization of those parts of the configuration that are not needed for the initial window display.
• The phonetic similarity finder analyzer have been removed from the main distribution, as this was quite experimental and serves mostly as a proof of concept and an appetizer to the community to create more advanced matching analyzers. You can now find and install the phonetic similarity finder on the ExtensionSwap.
• Cancelled or errornous job handling was improved and the user interface responds more correctly by disabling buttons and progress indicators, if a job has stopped.
• Fixed a few minor UI issues pertaining to table sizing and use of scrollbars.

Another release of DataCleaner sees the light of day today! Although this is not a major release, but a minor one, it does ship some quite nice stabilizing improvements and minor enhancements to the UI.


Enhancements in 2.1.1:

• Added a search/filtering text field on the datastores list. This enables you to quickly find your datastore if you have registered more datastores than available on the screen.
• Reference data for country codes was added to the standard distribution, thanks goes to Graham Rhind for providing these.
• Added a horizontal scroll bar to the data previewing windows of there are more than 10 columns.
• Ability to add an extension package with new functionality in the Options dialog at runtime. More focus on extensions will follow in the upcoming releases.
• We've exposed an early preview of our Command-Line Interface (CLI) by allowing you to invoke the application with the "-usage" parameter which will show the CLI options.
• Added number formatting options to the "Convert to Number" transformer.

Bugfixes in 2.1.1:

• Fixed an out-of-memory issue when querying tables with a LOT of columns (150+).
• Fixed an issue that cause the "Limit analysis" check box to not be checked correctly when a job was re-opened after saving.
• Not really a bugfix as it was never an official feature, but now we support restoring user preferences (the userpreferences.dat file) from previous versions of DataCleaner.

Thanks to everyone involved in the making of this release of DataCleaner.

DataCleaner 2.1.1 is available as a traditional download or as a Java Web Start application on the downloads page. Keep in touch with your feedback to the application on the forums.

We're happy to announce the release of DataCleaner 2.1! This is a quite significant release and something that we hope users will recognize as a step forward from the 2.0 versions.

The major news in DataCleaner 2.1 are:

• There was a lot of work done on the user interface (see media page):
  • We decided to remove the left-hand side window containing environment configuration options.
  • Instead all these options have now been moved to the job building window so the user only has to focus on a single window for all the interactions needed to build a job.
  • The welcome/login dialog has also been removed in favor of a more discrete panel that can be pulled in or hidden from the main window.
  • Datastore selection and management is considered the first activity in the application, which is why it is also the first step to handle in the main window.
• You can now stop jobs in case you decide to change something before it is done.
• Bar and line charts were added to a lot of the analysis result screens, including String analyzer, Number analyzer, Date/time analyzer and Weekday distribution (see media page).
• All "preview data" windows now contain paging controls so you can move backwards and forwards in the data set.
• Most common database drivers (MySQL, PostgreSQL, Oracle, MS SQL Server and Sybase) have been added to a default set of drivers.
• Configuration of the Quick analysis function in the Options dialog.
• Various minor bugfixes.
• Transformer for extracting date parts (year, month, day etc.) from date columns.

We hope you enjoy DataCleaner 2.1. Please head over to the downloads page to get it!

Eobjects.org and its contributors are pleased to announce that DataCleaner 2.0.2 has just been released.

DataCleaner 2.0.2 is a minor, but not unimportant, release containing a few bugfixes and a set of 8 feature enhancements:

• Tabs and buttons in the workbench are disabled when no source columns have been selected.
• A special widget have been added to the "Source" tab, making it very easy to apply row count based sampling of the input data.
• When possible, filters now have the ability to optimize the query of a job (aka. Push-down optimization). This was implemented for the "Max rows", "Equals" and "Not null" filters.
• The growing amount of transformers caused a long list in the "Add transformer" popup. Therefore transformers are now grouped by category and displayed accordingly.
• The visualization of execution flow now allows removing column items and filter outcome items, making the graph more comprehensible, especially for very large jobs.
• The "Coalesce string" transformer now has a "Consider empty strings as null" flag, which is particularly useful when dealing with CSV files.
• Text-based dictionaries and synonym catalogs will get their cached values flushed, if the file they read from changes.
• The "Convert to date" transformer now includes the ability to specify your own date masks, if date strings require it.
• A bug was fixed when passing null values to the the email standardizer.
• A bug was fixed pertaining to proper presentation of "mixed" tokens in the the Pattern finder.

With these improvements in place we see that DataCleaner 2.x is really catching along and we're very pleased with the quality and pace of improvements we are seeing. Go to the Downloads page right away to grab the new version.

Since the release of DataCleaner 2.0, we've seen a renewed interest and a lot of activity around eobjects.org, DataCleaner and Human Inference. We're happy to get all this valuable feedback and it has also meant that there where some low hanging fruit to as well as a few very minor bugs that we could easily add into the existing DataCleaner 2.0 release. This is why, already a week after 2.0 was released, we're releasing an update: 2.0.1.

The update consist of minor updates:

• Filter outcomes where added to the flow visualization.
• A bug was fixed in the widget for selecting the tokenizer's separators.
• The "Equals" filter can now have multiple values to compare with.
• Some minor cosmetical improvements.

For more detail, take a look at the milestone contents at Trac.

DataCleaner 2.0.1 is available at the downloads page and the update has also been automatically applied to our Java Web Start users.

The Open Source software community eobjects.org is happy to announce the release of DataCleaner 2.0. This release marks the biggest advance in technology and features for the DataCleaner platform throughout the history of the project.

Amongst exciting new features in DataCleaner 2.0 are:

• Data transformations, allowing you to preprocess, extract, refine, combine and calculate data items as a part of your data profiling jobs.
• Filtering, sampling and subflow management, allowing you to define criteria to exclude and include particular items of data.
• Richer reporting with charts, graphs, navigation trees and more.
• A bunch of new data quality functions for date gap analysis, phonetic similarity finding, synonym lookups and more.
• More configuration options and added data quality measures for existing data quality functions like the Pattern finder, String analyzer and more.
• Reusable profiling jobs, where you define your processing flow once and consequently run it on any data.
• Support for MS Excel 2007+ spreadsheets.

For more information about what’s new in DataCleaner 2.0, see the full list of new features in DataCleaner 2.0.

Today it was also announced that Human Inference, the European data quality authority has finished their acquisition of the eobjects.org site, to actively enter the market for entry-level Open Source data quality products. All projects on eobjects.org will remain open source and the benefit for the community and the products are apparent. The release of DataCleaner 2.0 is the first visible outcome of the acquisition, resulting from several months of intense cooperation between Human Inference and the community members, to put together a state-of-the-art data profiling application.

For more information about the eobjects.org acquisition, see the press release on the Human Inference website.

Times are really exciting in the eobjects.org community these days. We hope you’re all as enthusiastic about the new DataCleaner 2.0 as we are. The application is ready for download and for immediate launch through Java Web Start, so visit the DataCleaner website now.

Here it is: DataCleaner 1.5.4 :)

Although this release is a minor release it contains a few exciting features and fixes:

• We've updated the MetaModel version to 1.2 which adds support for two new datastores:
  • dBase databases (.dbf files)
  • MS Access databases (.mdb files)
• We've fixed a bug pertaining to text-file dictionary "file not found" errors.
• A lot of the other underlying libraries have been updated, providing improvements to performance and stability.

Head on over to the downloads page to grab the new DataCleaner.

After much waiting, we are finally ready to release DataCleaner 1.5.3. Here's the wrap-up on what's been going on:

• The MetaModel dependency has been upgraded to version 1.1.8, which means:
  • Improved Excel spreadsheet support
  • Improved SQL Server support
  • Improved performance for CSV files
• Fixed a bug that caused certain database connection errors to be ignored in terms of user feedback.
• Fixed a bug that caused re-opening of database dictionaries to throw a NullPointerException.
• Fixed a bug related to dictionary lookups of null values.
• Added support for Teradata databases.
• Added connection templates for SQL Server connections.
• Added support for selection of custom encodings when reading CSV files.
• Fixed a minor bug relating to reading files on the classpath when running in Java WebStart mode (which manifested in an exception thrown when clicking on "About DataCleaner").

So as you can see, it's been a mix of minor bugfixes and a couple of improvements to compatibility and performance regarding certain datastores. We hope you enjoy this new release of DataCleaner. As always, you can ...

• Get your copy at the /downloads page.
• Run it directly from the internet using Java WebStart.

Let us know what you think!

About half a year ago we received an exciting inquiry from Jos van Dongen on behalf of him and his co-author Roland Bouman, telling us that they where writing a new book about Open Source Business Intelligence and in particular Pentaho-based solutions. And for this they where looking into DataCleaner for the data profiling section of the book!

The book is now out! It's called "Pentaho Solutions" and it's published by Wiley Publishing. You can read about it and buy it on their website as well.

The book contains a walkthrough for building a data warehouse using Open Souce tools and in doing so applying DataCleaner for the important job of profiling and validation.

We congratulate Roland Bouman and Jos van Dongen for their great work to promote Open Source Business Intelligence and thank them for mentioning DataCleaner while they're at it!

Dear DataCleaner users,

We are happy to announce the release of DataCleaner 1.5.2. Users of DataCleaner 1.5.0 or 1.5.1 won't be able to see a lot of changes in the user interface, but this release actually holds quite a lot of improvements “beneath the surface”:

• The most notable improvement is in the Value Distribution Profile. Previously this profile consumed quite a lot of memory which could lead to out-of-memory errors in extreme cases. This has been fixed by using on-disk caching with the berkeley db when nescesary.
• Another notable feature is that we can now distribute DataCleaner as a single JAR file. This means that we will be serving the application as a Java WebStart application (ie. run it as if it's an online application) and we are also considering other distribution options.
• When starting the application, it automatically downloads regular expressions from the RegexSwap.
• A bug in regards to matching number-based columns in dictionaries was reported and fixed.
• A bug in regards to invalid characters in XML-export formats was reported and fixed.
• When opening files, we are now ignoring suffix case so that .CSV files can be opened as well as .csv.
• The number of columns shown in the preview window are automatically restricted if there are too many to show on a single screen.

You can download DataCleaner from the downloads page or you can use our new feature: Get it via Java WebStart!

This release underlines the ongoing evolution of DataCleaner to be a more and more professionally capable data profiler and data quality tool. Seeing that DataCleaner is being used in large corporations world wide I wish to address some thoughts that I have been having and that I know users are pondering with: How do you best combine the low adoption cost of Open Source applications like DataCleaner with the high flexibility that most commercial business-software provide? To service this need we've opened up a new division of the company that I work with, Lund&Bendsen. Whether you need to deploy DataCleaner to high-scale installations, integrate the applications with your existing systems or develop customized profiles, validation rules or satisfy other enterprise needs, we offer you first class services and in-depth expertise you wont find anywhere else.

To cut to the chase: DataCleaner 1.5.2 is here and we wish to extends the community development with a professional effort. So don't hesitate to let us know if you see an opportunity to invest. Adding value by targeting your use of the product is in the interest of both customer, developer and community and this is the reason our business is there.

To all you non-business users out there: Sorry for the obvious commercial rant and we hope you all enjoy the newest DataCleaner release.

Best regards,[[BR]]
Kasper Sørensen[[BR]]
Founder of eobjects.org and the DataCleaner project

We're happy to announce the release of DataCleaner version 1.5.1. This release is a minor release, nevertheless containing a few nice features - especially for the users who are enjoying the exporting features that was introduced in 1.5:

• An additional HTML export format have been added to the built-in export formats (usable when exporting Profiler results in the desktop app and when executing the runjob command-line tool).
• The export format is now choosable directly in the desktop app.
• Four new measures where added to the String Analysis profile: avg. chars and max/min/avg white spaces.

The new version of DataCleaner is (as always) downloadable for free on the downloads page and feedback from users is also greatly appreciated, ie:

• Fill out our online user survey, or
• Post your comments and questions at our discussion forum.

We hope that you all enjoy DataCleaner 1.5.1.

"Finally!" one might say. And this is definately what is going through my head right as I write this news-item. Finally, DataCleaner 1.5 has been released! Once again the effort to bring about the best open source data quality solution is bearing fruit.

The new release is definately one of the most significant ones in the history of DataCleaner. The overall goal of the release has been to step up from the shadows of the "small tools" pool and mark DataCleaner as an enterprise-ready application for profiling and validating datastores of all kinds - both in scheduled mode, on servers and in an intuitive desktop environment.

For those of you with an interest in every little detail about this release, please feel free to review the complete list of changes - for everyone else, here's the recap:

• Change of license to LGPL.
• Multi-threaded execution of Profiler and Validator.
• Command line (batch) execution of DataCleaner tasks.
• More elaborate status information during profiler and validator execution.
• New profile: Date mask matcher.
• New profile: Regex matcher.
• Load regex from the online RegexSwap repository.
• Automatic download and install of popular database drivers.
• More file types supported (.dat, .txt)
• XML file support improved (.xml)
• Memory improvements in Time analysis profile.
• Improved logging when running profiling and validation.
• Information schema provided for file-based datastores.
• Lazy-loading of columns in datastore-tree.

We hope you enjoy the new DataCleaner 1.5! Now go over and download it right away.

Things are starting to shape up for the big release of DataCleaner 1.5. We are starting off with a bit of excitement around in the data quality community.


Probably the most dedicated online magazine about data quality, data quality pro, have launched a series of articles about profiling, validating and comparing data with DataCleaner. So far an introductory tutorial (including a complete and realistic example data-set) and a background article/interview have been published:

• Learn how to profile and validate data (for free) using DataCleaner
• Interview with Kasper Sørensen, creator of DataCleaner

We hope that you will enjoy the articles and we thank data quality pro for their great interest in our community.

Today we are announcing the first company, Lund&Bendsen, to officially support DataCleaner and MetaModel on a commercial level. These eobjects.org projects are, as you know, independent projects that are run with the community in mind. But as time goes on they grow and for companies to pick them up and start using them in a commercial setting we also welcome third party commercial support to help spread the projects to environments where community-based support is insufficient.

Lund&Bendsen is a Danish company with a strong expertise in Java development and training. Their service offerings include training, customization, integration and enhancement of DataCleaner and MetaModel so if your company is considering applying DataCleaner they might be interested in hiring some professionals to aid them in the process.

Over time more companies are expected to join in on commercial support for the eobjects.org projects. Keep up to date on the DataCleaner support page and don't hesitate to contact us for any inquiries in this regard either.

The Technology Evaluation Centers (TEC) have published an interesting, unbiased and independent analysis of the market for Open Source business intelligence products. We are delighted to see that the article features a section about data quality and that TEC points at DataCleaner as a competent choise within the open source products:

In such situations, where the vendor does not support a specific functionality,
organizations can look to complementary open source solutions; the DataCleaner
project from eobjects.org, for instance, provides functionality to help profile
data and monitor data quality. It also points to a significant advantage with
open source applications: the fact that software is developed by the community
and for the community makes it much simpler to share innovative solutions
quickly and seamlessly.

You can read the whole article by Anna Mallikarjunan from TEC by going to their website (user registration is required).

Another batch of updates, fixes and improvements for the upcoming DataCleaner release is ready. This time it's Release Candidate 2 offering a preview of what's to come in DataCleaner 1.5.

• DataCleaner download site: http://datacleaner.eobjects.org/downloads

The main changes since Release Candidate 1 are multithreaded execution, the command line interface (runjob.sh / runjob.cmd), some UI updates and a few bugfixes. Go download the release candidate and use it as an opportunity to influence the development process by posting your comments on the DataCleaner forum.

After working hard for a couple of days to implement substantial new features regarding integration of eobjects services and automatic download and install of popular database drivers, a new release candidate of DataCleaner is ready!

• DataCleaner download site: http://datacleaner.eobjects.org/downloads

We hope that a lot of people will use the release candidate and provide feedback for further development towards the 1.5 final release.

I've spent the last couple of days implementing a couple of cool enhancements to the DataCleaner desktop-application:

• Automatic download and install of popular database drivers. Followed along with template connection strings in the "Open database" dialog. This will hopefully make it much easier for less experienced users to set up a connection to their database of choice.
• Direct integration with the new RegexSwap system so that the regexes that you post online will be accessible from within the desktop-application.

Screenshots have been posted to the media page.

Wait for DataCleaner 1.5 for these features or [BuildingDataCleaner build it yourself] to check them out now.

Only a few days after the launch of the new DataCleaner website, we are once again ready with new exciting features. This time we are launching the first edition of our new regular expression (regex) sharing subsite called "RegexSwap".

RegexSwap is a specialized forum for sharing, categorizing, commenting and voting on regular expressions that can be used in DataCleaner and other regex-based applications. It is really easy to post your own regular expressions, test them online on the website, comment and vote on the regexes that you have found useful. In time the next releases of DataCleaner will also take advantage of this online "always up to date" regex resource and offer direct integration with RegexSwap.

RegexSwap is still in beta but is ready at a functional level which is why we are launching publically it now. It will recieve dedicated attention in the weeks and months to come.

Dear everybody,

As a special christmas present we have been working hard to design a new website for DataCleaner! Hopefully you will all enjoy the new site, which have been designed to further support our community and let it grow by incorporating more features to socialize and share ideas online. So go visit it now at the new URL:

• http://datacleaner.eobjects.org

Among the new features are a more personal profile system which is linked to some of the communities that our users already use frequently, namely LinkedIn and SourceForge. We have a whole new media section with cool screenshots and webcasts. We are also redesigning our mailing list structure. Instead of the single mailing list that we have been using so far, we are launching new "announcement" and "dev" mailing lists.

Our goal is to continuously launch new features on the website. The first one being a user survey to gain a better insight into the minds of our users and community. So be sure to fill it out. In the future we will add more exiting features such as online sharing of regular expressions and reference data for DataCleaner dictionaries.

The old website will continue to exist, but primarily as a wiki and bugtracking system. During the next couple of days we will be editing the wiki pages to make them more suitable for wiki-style editing (by everyone) as opposed to the former readonly strategy.

We hope you like our christmas present and that you will let us know. and we wish you all a great 2009. Without a doubt, it will bring exiting times for DataCleaner and the DataCleaner community.

As we're moving steadily along towards the release of DataCleaner 1.5 we are fixing a few bugs and enhancing a lot of features. This leads to the desire to release our work since practically nothing has undergone changes that could destabilize the application since the 1.4 release. So today we're releasing DataCleaner 1.5 "snapshot". This also marks the first release under our new LGPL license.

Here are the changes from 1.4 so far:

• Change of license to LGPL.
• New profile: Date mask matcher.
• New profile: Regex matcher.
• More file types supported (.dat, .txt)
• XML file support improved (.xml)

Although this is in principle a development/beta release, we feel that it would be worth working with for most of your profiling needs. So... Go on, [GetDataCleaner download it], tell us what you think and we'll see you around!

We've made a principal decision at eobjects.org to change the preferred license of our projects from the Apache License 2.0 to the Lesser General Public License (LGPL).

The main difference between the two licenses are that the LGPL requires any modifications to be contributed back to the Open Source community (ie. licensed under a similar license; LGPL or GPL). The eobjects.org projects are gaining the obvious advantages of the LGPL by ensuring that improvements are submitted back to the projects. This also means that we don't risk that anyone sell modified versions of our projects. It is still just as appropriate to use the projects as a part of commercial applications, but any modifications must be contributed back to the community.

• The Apache License 2.0: http://www.apache.org/licenses/LICENSE-2.0
• Lesser General Public License: http://www.gnu.org/licenses/lgpl-3.0.txt

Initially this change in license will affect the two flagship projects of eobjects.org: DataCleaner and MetaModel. This means that the next versions of these projects (DataCleaner 1.5 and MetaModel 1.1 accordingly) will be LGPL licensed. Also, new projects will be LGPL licensed unless special circumstances suggest otherwise.

We've just uploaded a [wiki:DataCleanerProfilerIntroWebcast/1.4 webcast of the new DataCleaner 1.4] which provides a long awaited update for the old 0.4 webcasts!

Go enjoy the webcast - and be sure to [GetDataCleaner download the newest version of DataCleaner]. Over and out!

I'm please to announce the release of DataCleaner 1.4! This is a release that we feel will satisfy a lot of users with improvements and fixes for a lot of issues. Here's a very short compilation of changes, for more details, take a look at the roadmap.

• Replaced "Repeated values" profile with better and more advanced "Value distribution" profile.
• Dictionary matcher drill-to-details options.
• New application logo.
• Lots of small bugfixes and UI beautifications.
• Lots of sample dictionaries and regexes.

We hope you enjoy the new version of DataCleaner - [GetDataCleaner Get it now]!.

After some considerations about the future of DataCleaner, we've updated the roadmap to reflect our current plans for the direction of development. We are planning on releasing DataCleaner 1.4 by the end of the month and after that two new milestones have been added:

• DataCleaner 1.5: The main focus of this release is to provide a command line interface for our data quality framework. This means that users will be able to easily create batch jobs that they can schedule using their favorite scheduler. Other features will also include Pattern Finder improvements and a couple of new profiles.
• DataCleaner 1.6: We have a lot of suggestions that have been filling up our backlog. DataCleaner 1.6 will be all about getting everybody's needs into the application before we get ready to begin the webapp. Some of the exciting features of DataCleaner 1.6 will be relationship profiling and exporting of results.

Great news everybody. The Open Source Days '08 conference in Copenhagen will feature a so-called Lightning Speak by Kasper Sørensen on the topic of DataCleaner and the eobjects.org community.

We're really happy to get the message of DataCleaner out to more people and a conference like this is an ideal spot for demonstrations, discussions and experiences. Read more about the lightning speak at Kasper's blog:

• http://kasper.eobjects.dk/2008/09/presenting-datacleaner-at-open-source.html

Update: The presentation is over and you can now also read the retrospective at Kasper's blog:

• http://kasper.eobjects.dk/2008/10/fast-as-lightning.html

We've released a development/snapshot release of DataCleaner 1.4 in order to get early reactions for all the improvements and new features as well as supporting our users with up to date functionality. In my own opinion the development release is just as stable and "safe to use" as 1.3, but of course it lacks a bit of the manual testing that we put into the real releases.

You can download the development release at our sourceforge download site.

Here's a short list of fixes since DataCleaner 1.3:

* Better memory handling and garbage collection
* Reference columns in drill-to-details windows
* Better error handling when loading schemas
* Quoting of string values in visualized tables (in order to distinguish empty strings and white spaces)
* New profile: Value Distribution, which is an improved version of the Repeated Values profile. The Value Distribution profile has an option to configure the top/bottom n values to include in the result.
* Better control of profile result column width.
* Bugfix: Copy to clipboard functions now work properly.
* Bugfix: Scrollbars added to visualized tables.

Take a look at the roadmap for more current developments of DataCleaner.