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Storing your enterprise data on Hadoop is the first step, but once you have it all in one place, it becomes necessary to make it accessible to all the stakeholders who need it and exactly when they need it.

Moved to Hadoop? Now what?

To get insights into your business, you need self-service access to Hadoop data so that you can generate reports quickly and get instant answers to all your questions. Most organizations find it challenging when users try to connect traditional BI and analytical tools directly to Hadoop, as they face significant performance issues.

As data volumes explode and the complexity of reports increases, most BI tools fail on response times. Wait times get longer, ranging from a few minutes to a few hours, and business users cannot be expected to wait for that long. As a result, data cannot be used for problem-solving or better decision making.

Traditional OLAP and its problems

Organizations have tried several analytical approaches to solve the problems associated with the speed and scale of big data. When they attempt to use traditional OLAP (Online Analytical Processing) solutions on big data, they fail as these conventional tools cannot deal with the volume, cardinality, dimensions, and the variety of big data.

Another common approach is to pull data from Hadoop to an external data mart and then perform analysis. However, OLAP-in-memory causes latency and brings in limitations on the amount of data that can be processed.

An alternative solution is to shift analytical capabilities to the data platform instead of trying to move data to the analytical environment.

OLAP on Hadoop comes to the rescue

OLAP on Hadoop solves the problems of big data analytics without the need to move data out of the Hadoop platform. Multi-dimensional OLAP cubes are created directly on Hadoop, and these cubes provide instant response to all queries enabling quick analytics on massive amounts of data on a variety of metrics.

Kyvos has developed an innovative OLAP on Hadoop technology that helps pre-aggregate trillions of rows of data across hundreds of dimensions, building highly optimized cubes that are stored in a distributed manner across the Hadoop infrastructure. These multi-dimensional cubes store data in the most granular form supporting in-depth analysis across any dimension with interactive response times.

Though achieving OLAP at this scale is tough, Kyvos uses its advanced algorithms to contain the combinatorial explosion that happens while dealing with massive data. This makes the solution infinitely scalable, delivering performance at a scale that cannot be achieved with partial aggregation or in-memory solutions. Once created, these OLAP cubes can be processed incrementally, making it easy to add new data and replace old data without manual intervention or custom coding. Users can use their existing BI tools to connect to these OLAP cubes and conduct instant, interactive analysis on their big data.

Case in Point: OLAP on Hadoop at Bell Canada

Several Fortune 500 companies across the globe are using Kyvos’ OLAP on Hadoop technology to enable instant BI on Hadoop. At the Strata Data Conference this year, Kyvos’ customer, Bell Canada, presented how they increased the scale of BI exponentially with OLAP on Big Data. Bell Canada, one of the largest telecommunications companies in Canada, offers Internet, Wireless, TV, Home Phone, and other services to its customers and relies heavily on data to make accurate business decisions and deliver better services.

With thousands of reports of varying types and frequencies, used by over 10,000 employees across the organization for different purposes, they were facing issues in giving interactive access to huge volumes of data that was being generated continuously. Their existing BI tools, MicroStrategy and Tableau, could not deal with the increasing scale of data and deliver the required performance.

They implemented Kyvos’ OLAP on Hadoop solution to meet the reporting needs and performance expectations of their business users. The solution helped them increase the scale of their BI by delivering very high performance on almost any size of data. Sub-second query responses surpassed their business SLAs of 3-5 seconds response time.

Want to try OLAP on Hadoop?

If you want to get more details on the need for OLAP on Hadoop and how it revolutionizes analytics on big data, download our whitepaper on BI on Big Data Trends.

To assess how OLAP on Hadoop can benefit your organization, you can request a demo now or write to us at

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