Consolidating data warehouses
Technological advances, mergers and acquisitions, consolidation, and regulatory compliances have led to significant increases in data sources and volume and a more complex and dynamic business environement than ever before.
To successfully implement and manage an enterprise data warehouse, organizations need to develop a strategy to rapidly adapt to these changes.
However, data warehousing operation requires access to a massive number of records in order to perform even simple analytics.
In order to improvise the performance on these OLTP databases and support the data warehouse for running massive queries, organizations and RDBMS vendors have resorted to design tricks such as materialized views, aggregate tables, data partitioning strategies and indices.
These new metrics are easily integrated into an organization’s existing business intelligence queries, reports, dashboards and analysis, all of which increase productivity and lead to faster data discovery, profiling and data visualization production.
Costs: Data warehouse modernization not only increases an organization’s ability to increase speeds and feeds in the data warehouse environment, but it also provides a great opportunity to optimize the overall costs in areas such as storage and upgrades.
We will closely work with business and IT teams throughout each project to provide assistance in The evolution of big data has created numerous opportunities for organizations to build data warehouses that take full advantage of advanced analytics applications with low cost of ownership.
Modernization does not necessarily mean a complete overhaul of a data warehouse.
This approach identifies and eliminates existing investments that are not producing ROI.
Here are the top reasons modernizing your data warehouse is extremely important: Advanced analytics: The age of analytics is here, and many organizations have invested heavily in reporting and building OLAP applications.
However, they are now making a rapid shift towards advanced forms of analytics such as predictive/prescriptive models and leverage the power of big data.