As SSBI tools evolved, data scientists were still wrestling with the overall challenge of finding an analytical database as flexible for analytics as relational databases were for transactional data processing.
Development of Analytical Databases
Progressive software vendors sought to overcome the limitations of data warehouses, cubes, and SSBIs and began working towards creating databases that were both flexible and able to process analytical workloads. These analytical databases, or column stores, were the next step in the trend to provide business analysts the tools and flexibility they need. These analytical databases have evolved into massively parallel processing (MPP) analytic databases that are more flexible and more performant than Cubes even in the cases where large amounts of data are being stored and queried.
However, these analytical databases require that data be copied into them using processes very similar to the aforementioned ETL processes and have similar drawbacks. The load processes are typically slower than in traditional data warehouse based on row based technology because there is an extra step required to optimize the data for quick analytical retrieval. This extra step is required to convert the data from a row-based format into a columnar format and then apply field level data compression. Although these extra steps provide significant performance improvements, they also require additional time that delays the analysts’ ability to analyze the data. It is impossible to access real-time data in analytical databases due to this load time latency.
Advantages and Disadvantages of Analytical Databases
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