Loading...
You are here:  Home  >  Data Education  >  BI / Data Science News, Articles, & Education  >  BI / Data Science Articles  >  Current Article

OLAP, Hadoop, and Multi-Dimensional Analytics Combined

By   /  August 16, 2017  /  No Comments

Kyvos InsightsOLAP offers OLAP (Online Analytical Processing) software, which works directly with Hadoop storage systems. The use of Hadoop counteracts OLAP’s historical problem of lacking scalability. Their software constructs “cubes” on Hadoop which, in turn, supports multi-dimensional analytics. The OLAP/Hadoop combination supports the processing of both unstructured and structured data at any scale, in turn, allowing businesses and organizations to analyze Big Data with a fair amount of ease.

Ajay Anand, Vice President of Products at Kyvos Insights, said:

“At this stage of the evolution of Big Data, customers do not always know what the technology can offer. It is a journey, and they need to get educated over time. They first look at the low-hanging fruit, and then want to know what else they can do, that they could not before. In order to explore, interactivity is critical, because otherwise it is really hard to follow a train of thought and get insights. If the user can get an interactive response, a tremendous increase in productivity is possible.”

Businesses have been using Hadoop to collect and manage Big Data since 2007, but have often struggled with finding the best way to process the data for maximum insights. OLAP, once upon a time, had been the primary method for divining Business Intelligence. Kyvos Insights has eclectically combined the two systems to combine their individual strengths. They offer a spectrum of products and solutions, such as customer analytics, focused marketing, log analytics, and a variety of data visualization techniques. Users of their products do not have to purchase additional hardware to create a stand-alone Data Warehouse, but can use an existing Hadoop system to store data. Their “cubes” software has revolutionized the storage and processing of Big Data.

OLAP

OLAP allows data to be viewed from different perspectives, and extracted quickly and selectively. For example, an analysis can be displayed as a spreadsheet, showing paper sales in Montana for the month of July, and compared to the revenue figures nationwide for the same month, and then compared to other product sales in Montana for the month of July. OLAP accomplishes these tasks by storing data in a multi-dimensional database. A relational database might be considered two-dimensional, while a multi-dimensional database treats each data attribute (a product, a geographic sales region, a time period) as separate dimensions. OLAP software can “find” the intersections of different dimensions, and display them in a variety of visually useful ways.

Kyvos Insights

Kyvos’ software also provides a reliable form of data mining, and can be used for finding previously unknown relationships between data items. According to Anand:

“All the transactional data is not needed during a trend analysis, the OLAP database can be smaller than the Data Warehouse. With an Open Database Connectivity (ODBC), the data can be sent from existing relational databases, and used to develop a multi-dimensional database. The goal of Kyvos’ software is to translate the structured and unstructured data collected by Hadoop into OLAP patterns, which can then be used in Business Intelligence software. A very useful feature is the ability to merge data from various sources into a single data cube.

A “cube,” or data cube (sometimes called a hypercube), contains a multi-dimensional collection of values (dimensions), generally taken from multiple databases. Kyvos Insights provides a persistent cube, which operates as a dimensional cache of base data, including stored aggregations. A cube is made up of a variety of “cuboids,” with each cuboid stored on different nodes within the cluster.

On April 12, 2017, Kyvos Insights announced that its software product, “Kyvos,” now supports the leading Cloud platforms, such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure (these represent roughly 82 percent of the public cloud platform market). The Kyvos’ BI Consumption Layer approach allows people to process Big Data uniformly, regardless of the infrastructure (data can be stored in multiple Cloud infrastructures, or on premise). As a consequence, business people can analyze Cloud-based data with Kyvos, and the flexibility of using the BI tools of “their own choosing,” (Tableau, MicroStrategy, Excel, etc.) to gain business insights.

Ajay Anand said:

“Our customers can now migrate from on-premise to cloud-based environments seamlessly. With Kyvos support for Google Cloud, developers and business users have access to highly scalable, reliable, fast, inexpensive data storage and computation infrastructure to enable BI on Big Data. Our customers can now migrate from on-premise to Cloud-based environments seamlessly.”

Bypassing the Data Scientists and Programmers

Kyvos Insights is fairly simple to use, and has organized the data in Hadoop so business people can access the program without the need to write new code. Their software effectively eliminates the need for a Data Scientist, allowing business professionals to access the data directly. Anand stated:

“Most users of Big Data have been Data Scientists and programmers. But such experts are hard to find, and we wanted to bridge the barrier to bring data to the business users. Our premise was that there should be a seamless way to connect with Hadoop.”

Kyvos Insights’ primary focus is on making Big Data easily accessible to business people. Their system allows nontechnical individuals to do the research, and decide what information is useful. A significant problem with Hadoop has been that it is “not user-friendly.” Working with Hadoop is difficult and complicated. Kyvos’ software offers a self-service analytics program, free from concerns of scalability or granularity, that makes Hadoop easy. A business person can explore data, view it through different paradigms, and follow a clue/hunch with instant response times.

Working with Big Data

Traditionally, Data Warehouses have collected and extracted data, and then saved it in a data mart, making the scale more manageable for using visualization tools. The process makes for a clumsy transfer and storage system. Kyvos Insights has streamlined this process by layering an OLAP-style analyses directly onto Hadoop.

The Internet of Things follows millions of devices for tracking and obtaining Big Data, making scalability an issue. To make sense of this, a computer system must not only provide scalability, but also lots of detail. Real scalability allows for researching an entire target population, instead of simply using random samples. This type of scalability leads to the use of details. For example, a client wanting to gain insights into the Latino market, rather than using surveys and samples, can access Big Data, which is more reliable. Their system allows for much more detailed research, and better statistical results.

Kyvos comes with its own Big Data tools, but can also be used with other BI tools. Anand said:

“Users of Tableau, for example, can get the same kind of interactivity with Big Data by using our product. A lot of Big Data projects languish because people have created Data Lakes in Hadoop, but the business users cannot access them. Our software overcomes that problem.”

The Objective

Kyvos Insights wants to unlock the potential of Big Data Lakes using their “OLAP on Hadoop” technology. Using people with years of analytics experience and expertise, the company plans to revolutionize the Big Data Analytics industry by providing business people with the technology to interactively research and analyze Big Data. The company is located in Los Gatos, California, and has partnered with a number of companies, including Tableau, MapR, Hortonworks, and Cloudera.

Photo Credit: ra2studio/Shutterstock.com

About the author

Keith is a freelance researcher and writer.He has traveled extensively and is a military veteran. His background is physics, and business with an emphasis on Data Science. He gave up his car, preferring to bicycle and use public transport. Keith enjoys yoga, mini adventures, spirituality, and chocolate ice cream.

You might also like...

Property Graphs: The Swiss Army Knife of Data Modeling

Read More →