This is the Linux app named Open Source Data Quality and Profiling whose latest release can be downloaded as ProfileV6.3.3.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
Open Source Data Quality and Profiling
DESCRIPTION:
This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy.
This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data Creation, Meta Data Discovery, Anomaly Discovery, Data Cleansing, Reporting and Analytic.
It also had Hadoop ( Big data ) support to move files to/from Hadoop Grid, Create, Load and Profile Hive Tables. This project is also known as "Aggregate Profiler"
Resful API for this project is getting built as (Beta Version) https://sourceforge.net/projects/restful-api-for-osdq/
apache spark based data quality is getting built at https://sourceforge.net/projects/apache-spark-osdq/
Features
- Teiid, Mysql, Oracle,Postgres,Access,Db2,SQL Server certified Big data support - HIVE
- Create Hive table, Profile Hive table, Move file to/from Profiler System and Hadoop Grid
- Fuzzy Logic based similarity check, Cardinailty check between tables and files
- Export and import from XML, XLS or CSV format, PDF export
- File Analysis, Regex search, Standardization, DB search
- Complete DB Scan, SQL interface, Data Dictionary, Schema Comparison
- Statistical Analysis, Reporting ( dimension and measure based), Ad Hoc reports and Analytics
- Pattern Matching , DeDuplication, Case matching, Basket Analysis, Distribution Chart
- Data generation, Data Preparation and Data masking features
- Meta Data Information, Reverse engineering of Data Model
- Timeliness analysis , String length analysis, KMean, Prediction, Regression
- Address Correction, Single View of Customer, Product, Golden merge for records
- Record Match, Linkage and Merge added based on fuzzy logic
- Format Creation, Format Matching ( Phone, Date, String and Number), Format standardization
- Data Preparation: Ordinal,Normalization,Bucketing,Regression
- data masking, encryption, randomization of data
Audience
Advanced End Users, Developers, Quality Engineers, Management
User interface
Java Swing
Programming Language
Java
Database Environment
JDBC, ODBC
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/dataquality/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.