DownloadThis 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
Project Samples
