Great strides have been made in the world of big data technologies over the past decade. Cloud infrastructure along with new storage paradigms like Hadoop, graph databases and message buses have provided a new dimension to the arsenal of tools available to tackle the deluge of data the world is facing.
A “modern” data warehouse architecture that combines SQL and not only SQL (NO-SQL) technologies is a preferred way to store and enrich enterprise data where “small data” is seen as latest fad, where the focus is back on quality rather than quantity.
Our view is that a well-structured data warehouse, with SQL at its core, still offers a technological maturity that is still lacking from these new world tech solutions that remains relevant to financial service provides.
Data Warehouse High-Level Architecture
The architecture for a data warehouse hasn’t changed much over the years except for the inclusion of “Big Data” sources. The figure above, outlines the layers and concerns of such an environment:
The following high-level stages are generally prescribed when implementing a data warehouse that will provide a return on investment to the business:
Our executive team has extensive experience in data warehousing and business intelligence in banking, credit risk, stress testing and data management. We see an overlap in these areas that provide a sweet spot for a return on investment made. If you or your team could benefit from this approach, please contact us to discuss how we can assist you and your team.
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