Providing data structure to Big Data to improve its performance

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OLAP-on-Hadoop products bring data structure to big data, optimizing performance and scalability even with large volumes of information.
OLAP-on-Hadoop products bring data structure to big data, optimizing both performance and scalability , enabling users to query and analyze large volumes of information at the speed of thought.

OLAP seems like the antithesis of big data , reminiscent of old-school approaches to data management and analysis. But some big data techniques can only be successful if everyone within an organization can benefit from them .


There are those who would rather write Java or SQL linkedin database raw Hadoop data, and those who would rather run SQL statements in Hadoop. But most prefer access to a data structure designed and built in advance by a data architect .



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OLAP on Hadoop
These are a subset of analytical tools that seem to revive the old concept of online analytical processing (OLAP) by adapting it for Big Data. These tools achieve higher levels of performance and scalability than other solutions .

Products called OLAP-on-Hadoop dimensionalize data and present it in a business-friendly format. With OLAP, business users view metrics as common dimensions . For example, executives can examine sales by product, region, and time. With a click of a mouse, they can swap metrics, add or filter dimensions, pivot axes, and drill down from summary views of business performance to raw data. In other words, OLAP makes it easy for business users to analyze data presented in the same way they view the business .

To dimensionalize data, OLAP-on-Hadoop products require designers to model the data to be analyzed, combined, integrated , cleaned, and validated, before users query it . Most OLAP on Hadoop products don’t just model the data in advance, they materialize it. They create new aggregate data structures that are loaded into memory or into high-performance columnar databases . This is a write-alike scheme, which if you listen to the big data community, is no longer in vogue, but is certainly useful for querying big data.



Optimizing scalability and performance
By modeling, calculating, and storing dimensional aggregates in advance, OLAP-on-Hadoop products achieve scalability and performance in a big data environment . They solve the scalability problem by keeping data in Hadoop where storage is cheap, allowing them to generate huge dimensional cubes with terabytes or more of data. And they solve the performance problem by pre-aggregating data in high-speed data caching , providing speed-of-thought analytics against big data.