Relational databases (Oracle, SQL Server, IBM DB2…)

Relational databases

Files (Excel, CSV, XML, JSON…)

Files

Business Applications (SAP, Microsoft Dynamics, Sugar CRM…)

Business Applications

SaaS (Salesforce, NetSuite..)

SaaS

Web Services (RESTful, JSON, OData…)

Web Services

Data Stream (RSS feeds, Kafka…)

Data Stream

No SQL databases ( HBase, Cassandra, MongoDB, Couchbase…)

No SQL databases

Hadoop (Hortonworks, Hive, Apache Spark…)

Hadoop

Social Network (Facebook, Twitter, Linkedin…)

Social Network

Could platforms (Microsoft Azure, Amazon Web Services...)

Could platforms

Data sources connection

Data sources connection

Data Federation

Data Federation

Filtering

Filtering

Calculation

Calculation

Aggregation

Aggregation

Aggregation

Transformation

Quality

Data Quality

Cache/Staging/MDM

Cache/Staging/MDM

Cache/Staging/MDM

Risk & Compliance systems

Data Warehouse

Data Warehouse

Reporting Systems

Reporting Systems

Analytic Application

Analytic Application

Self-service BI

Self-service BI

Dashboards

Dashboards

Fabric

Data Fabric

Data Integration

Data integration involves combining heterogeneous data from disparate sources for providing a unified and consistent view of data.
A typical approach is to extract, transform and load data into a specialized database (an Operation Data Store, a Data Warehouse or a DataMart) using an ETL (Extraction Transformation Load).
An innovative approach consists in using data virtualization technologies for connecting data instead of collecting them.

Data Quality

With the massive increase in the amount of data collected and the broadening data sources, data quality management is more essential and more challenging than ever. Automation of data quality testing and data cleansing has become an imperative.
Data quality features include:  
   • Data profiling
   • Data standardization, matching and cleansing
   • Data enrichment

   • Data governance and stewardship