Whether you are planning to migrate your enterprise data to the cloud or have already migrated it, this article will help you understand why you need OLAP on big data. Read further to understand how to overcome the challenges of speed and scale associated with traditional BI approaches in the cloud.
What is OLAP on Big Data?
OLAP on big data is a powerful concept that involves the pre-aggregation of massive amounts of data and builds multidimensional cubes to get super-fast query 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 the BI tools to query data directly from these cubes.
Since cubes serve the queries, there is no need to connect to your cloud big data storage 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 on the cloud. They can continue using their existing tools and work seamlessly with their cloud data through the OLAP cube, without worrying about the size and scale of the data.
OLAP is Not New but How to Make it Work for Big Data in the Cloud
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 tools connect to Big Data Analytics on the cloud, they fail to perform as they cannot deal with the massive volumes, the explosion of cardinality and dimensions, and the large variety of data sources.
So, the question is how to achieve OLAP on big data in the cloud? Is it even possible to aggregate trillions of rows of data? What compromises need to be made while dealing with complex data?
One option is to go in for an in-memory OLAP solution, where all aggregations are computed in advance and stored in memory. However, in this case, you have to work within the memory limitations of the underlying infrastructure. Most in-memory OLAP solutions are designed to serve the most frequent use cases but slow down when new questions are asked. In a real-life business scenario, enterprises often have to compromise on the level of aggregation or the scale of data that can be handled.
Another option is to build the OLAP layer directly on the cloud. Cloud resources can be used to build as well as store the cubes. This would make it easy to deal with the scale and complexity of today’s data. However, achieving OLAP on the cloud at big data scale is tough due to to the combinatorial explosion that occurs while dealing with huge volumes of data.
Breaking Barriers of Speed and Scale with Smart OLAP™ on Big Data Technology
To overcome these challenges, Kyvos introduced its revolutionary Smart OLAP™ technology that enables aggregation at a scale that cannot be achieved with in-memory solutions or partial aggregation. Leveraging the scalability and flexibility of the cloud platform, Kyvos builds massively scalable cubes that are stored on the cloud itself. By creating an OLAP layer directly on the cloud, it enables users to interact with their enterprise data visually using a BI tool of their choice.
Our Smart OLAP™ technology outperforms other OLAP on big data solutions as it does not work on the principle of a limited number of expected queries or partial aggregation. The highly optimized cubes store data in a granular form, supporting in-depth analysis across hundreds of dimensions and measures with interactive response times. . Since the whole data is pre-aggregated, all queries, cold, warm or ad-hoc, are served quickly.
What OLAP on Big Data can do for You?
Growing expectations, volatile markets, and increased competition make it necessary to empower your users so that they can ask any question from their data and get their answers instantly. Users should not be penalized for asking complex questions or computing queries on massive data and can be empowered to get insights on years of historical enterprise data without compromising on performance.
With Kyvos’s Smart OLAP™ technology, users do not have to wait for insights. They can access massive volumes of data on the cloud instantly and get quick answers to their business questions. They can slice and dice, drill down and explore years of enterprise data to gain deeper insights and make smarter, more informed decisions.
Here are some examples of how organizations are using our Smart OLAP™ on big data technology to improve their decision-making:
- A leading multinational software company uses Kyvos to analyze 1.2 billion unique member IDs from their 230 million customers across several touchpoints, getting results within 2 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 hundreds of 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 massive amount of content viewership data generated from 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 trends and media metrics, programming KPIs, viewer behaviors by demographics, and more. Read Case Study
Watch Our Webinar
If you want to learn more about 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.
FAQ
What is OLAP, and how does it relate to Big Data?
OLAP, or online analytical processing, is the methodology to perform multidimensional analytics on enterprise data. OLAP on big data involves pre-aggregation of this data to build cubes and get superfast query responses.
What are the advantages of using OLAP for analyzing Big Data?
By building cubes with all probable dimensions and measures the users need, OLAP on big data helps connect to big data storage for every query and deliver instant responses. Users need not migrate from their traditional BI environment to analyze massive data loads – despite its complexity.
How does OLAP handle the scalability and volume of Big Data?
Unlike in-memory solutions, Kyvos enables smart aggregation at an unprecedented scale and volume. The platform helps build massively scalable cubes with an OLAP layer directly on the cloud so that users can interact with any data size visually via the BI tool/s of their choice.
How does OLAP on Big Data support complex analytical queries?
Since OLAP on big data doesn’t work on the principles of partial aggregation or limited queries, the highly optimized OLAP cubes can store business data in a granular form to support in-depth analysis across dimensions and measures. When all data is pre-aggregated, it’s easier to serve any queries – warm, cold, or ad hoc.
What are the challenges or limitations of implementing OLAP on Big Data?
Though OLAP on big data is a proven technology, handling massive and complex datasets remains challenging. Traditional OLAOP tools and platforms cannot deal with ever-increasing data volumes backed by an explosion of dimensions and cardinality. Plus, in-memory OLAP solutions can’t meet the growing analytical needs of modern businesses.
