Architecting a Big Data Platform for Analytics

For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years, allowing organizations to use business intelligence (BI) tools to analyze, compare, and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.

Today, we are over twenty years into data warehousing and BI. In that time, many companies have built up multiple data warehouses and data marts in various parts of their business. Yet despite the maturity in the market, BI remains at the forefront of IT investment. Much of this demand can be attributed to the fact that more and more data is being created. However, it is also the case that businesses are moving away from running on gut feel towards running on detailed factual information. In this vibrant market, software technology continues to improve with advances in analytical relational database technology, as well as the emergence of mobile and collaborative BI.

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