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Originally published February 18, 2014
As I covered in my previous article, modern enterprise data systems are tasked with responding to evolving business landscape – where customer interactions are extending beyond traditional data collection and sharing mechanisms. With data arriving at a greater speed and in different formats, the role of a data warehouse as a single unified place for enterprise data has to change as well.
Historically, enterprises were using data warehouse solutions in an attempt to merge data arriving from operational systems into a single integrated corporate data set so that they could more accurately report on business events. Today, when digital organizations are using technology to fundamentally evolve their businesses, focus is shifting to enabling capabilities such as data federation and virtualization to help reduce time to value and unlock business insights from different systems.
Data is coming from more sources than ever before – including both structured and unstructured data types. In the enterprise, that means data silos are here to stay. And, today they go beyond various files and spreadsheets to include NoSQL data stores, data lakes, on-demand feeds, cloud-based file systems, etc.
As a result, architectural solutions such as data polyglot persistence are gaining enterprise traction. This allows companies to take advantage of data from these disparate sources without having to architect and migrate all data to a single data store. Frankly, businesses don’t have any appetite for expensive and time-consuming data engineering efforts. They are expecting fast, agile insights and accelerated time to value with rapid delivery cycles.
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