Data Warehousing – Merging all kinds of data sources

Data warehousing includes various methods and algorithms, for example, to extract, cleanse, reconcile, consolidate and transfer data from ERP and other IT systems, office files, data from websites or Internet exchanges into a target system which is used as a buffer (so-called Data Lake) and is usually the database for analysis methods.

Of particular importance is the ETL process (Extract, Transform, Load). Via using specific processes developed ETL process chains enable automated merging of data from a wide variety of internal and external data sources. Practical experience shows that depending on the project, ETL process chains can become even more complex than the actual analyzes.

  • Automated Data Fusion / ETL

    Merging of separate but related data from relevant data sources via customer-specific and automatable ETL process chains.

  • ODBC/JDBC Connection

    ODBC drivers can easily connect to any SQL database, including MySQL, MSSQL, Oracle SQL, MariaDB, PostgreeSQL and Apache Hadoop Hive.

  • NoSQL Connection

    The connection of NoSQL databases, for example Cassandra, MongoDB, Neo4j or HBase, is also a matter of routine for our data scientists.

  • Unstructured Data

    Using intelligent Parsing and MapReduce algorithms, collecting and analyzing unstructured data is easy.