Data Warehousing

Link structured data from different sources

Data Warehousing includes various methods for merging and consolidating data from heterogeneous sources and creating different perspectives on data.

For example, data from ERP, CRM and other IT systems, office files, data from websites (e.g. stock exchanges) can be extracted, cleaned, matched, consolidated and transferred to a target system. A data warehouse is more than just a temporary storage facility for data, because it provides a uniform, comprehensible and cleansed data source and usually represents the data basis for further analysis procedures.

AUTOMATED DATA FUSION / ETL

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

INTERFACES

Any SQL database can be easily integrated via ODBC and other interfaces, for example MySQL, MSSQL, Oracle SQL, MariaDB, PostgreeSQL or Apache Hadoop as well as NoSQL databases

UNSTRUCTURED DATA

Using intelligent parsing and MapReduce algorithms, collecting and analysing unstructured data is not a problem

DATA MODELS

Sensible data modeling makes it easier to access, use and efficiently store the data that is actually important for your business