Understanding OLAP on Big Data: Why do you need it

By October 16, 2018 July 24th, 2019 Uncategorized

Understanding OLAP on Big Data: Why do you need it

If you have built your Big Data platform in the cloud or an on-premise environment, but find it difficult to extract insights when you need them, then you must read this article to explore how OLAP on Big Data technology can help you overcome the challenges of speed and scale associated with traditional BI approaches.

What is OLAP on Big Data?

OLAP on Big Data is a powerful concept that involves pre-aggregation of massive volumes of data into multidimensional cubes and then querying them to get faster results. The process involves building OLAP cubes with all the dimensions and measures on which the user would want to explore the data and then enabling BI tools to fetch data directly from these cubes. Since cubes serve the queries, there is no need to connect to the Big Data platform every time the user asks a question and responses are instant. Besides this, users do not need to move away from their traditional BI environment to perform Big Data analytics. They can continue using their existing tools and work seamlessly with Big Data through the OLAP cubes, without worrying about the size and scale of the data.

OLAP is not new, will it work on Big Data?

In the world of analytics, OLAP is not a new concept and is used widely to enable easy, interactive business intelligence on enterprise data. However, when traditional OLAP solutions are brought into the world of Big Data, they fail to perform as they cannot deal with the massive increase in the data volume, the explosion of cardinality and dimensions, and the large variety of data sources.

So, the question is how to achieve OLAP on Big Data?

To be able to deal with the scale of Big Data, the OLAP layer needs to be built directly on the Big Data platform, utilizing its build and store capacity. This will also ensure that the cubes can handle many dimensions to deal with the variety of data as well as accommodate the high volume and velocity of Big Data.

How Kyvos delivers OLAP on Big Data?

Kyvos uses its innovative OLAP on Big Data technology to pre-aggregate huge volumes of data across multiple dimensions and build highly optimized and elastic cubes that are stored in a distributed manner across the Big Data infrastructure.

The unique feature of Kyvos technology is that it can build extremely large-sized cubes both in the cloud as well as on-premise Big Data platforms, providing instant, interactive access to hundreds of billions of rows of data.

These multi-dimensional cubes store data in a granular form supporting in-depth analysis across any dimension with interactive response times. The advanced technology revolutionizes Big Data analytics for enterprises enabling unmatched performance and unlimited scalability.

Though there are several other solutions available in the market that do OLAP on Big Data, most of them work on the principle of expected queries or partial aggregation. This means that the queries that are known are served well, but when new questions are asked, they fail on response times. However, Kyvos pre-aggregates the entire data, ensuring all queries, standard or ad-hoc, old or new, are served equally, enabling multi-dimensional analytics on Big Data at massive scale and with sub-second response times.

What OLAP on Big Data can do for you?

By creating an OLAP layer on your Big Data platform, you can interact with your Big Data visually using a BI tool of your choice. There is no need to wait for insights, and you can access massive volumes of data to get quick answers to all the questions that you may have. You can slice and dice, drill-down, and explore all aspects of your data to get deeper insights and make smarter, more informed decisions.

Here are some examples of how organizations are using Kyvos’ OLAP on Big Data technology to improve their decision-making:

  • A leading multinational software company uses Kyvos to analyze 1.2 billion interactions from their 230 million customers across several touchpoints, getting results within 10 seconds. This helps them gain quick insights into their customers’ behavior and use that knowledge to increase sales, improve retention, and deliver superior experiences. Read Case Study
  • Kyvos helped a leading global investment bank transform risk-based forecasting and planning with instant BI on 500 billion transactions. Today, even with 100 concurrent user sessions most big data queries are returned in less than 5 seconds. This is almost 300 times faster than the earlier architecture, where the same queries took 650 seconds to complete with only 20 concurrent user sessions. Read Case Study
  • A global digital media company uses OLAP on Big Data to conduct interactive analytics on the massive amount of viewership data generated from over 6 million subscribers. With around 250 million rows of data being added per day, Kyvos enables them to conduct instant analysis on 14-month data to understand media metrics and trends, programming successes and failures, viewer behaviors by demographics, and more. Read Case Study

Watch Our Webinar

If you want to learn more on how you can use Kyvos’ OLAP technology to achieve multi-dimensional analytics on your Big Data, watch the recording of our webinar “BI at Exponential Scale at Bell Canada.”

In this webinar our speakers, Mark Huang, Director – Data Engineering at Bell Canada and Ajay Anand, VP Products & Marketing – Kyvos Insights, discuss why Bell Canada chose Kyvos’ OLAP on Big Data solution to meet the reporting needs and performance expectations of their business users.

Watch Webcast

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