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Actian Blog / OLAP in Data Warehouses

OLAP in Data Warehouses

Close Up View Of A Laptop With A Business Chart On The Screen Picture Id1205248124

Decisions within an organization are made in three possible ways. One way is based on the experience of the individual making the decision. The second way is based on analytics. The third way is based on a combination of one and two, knowledgeable experience and analytics. Based on experience or expertise in a subject, we can often make very well-informed decisions and obtain desired outcomes. To enhance our expertise, we can use metrics based on factual data. The data may reveal information that was absent from our expert opinion. Of course, using both expert opinion and analytics is the best approach for solving problems or thinking strategically for the business. The use of data warehouses is one way to gather analytics to improve decision making within an organization.

Use of OLAP in Data Warehouses

Online Analytical Processing in Data Warehouses allows rapid calculation of analytical business information using metrics for modeling, planning, or forecasting. OLAP is the foundation of analytics that support many business applications for reporting, simulation models, information-to-knowledge transitions, and trend and performance management.

Data contained in a data warehouse is often used for OLAP. OLAP solutions enhance a data warehouse with aggregate data and business calculations.

OLAP vs. OLTP

Online transaction processing (OLTP) is designed to handle transactions by getting data organized and written to a database as quickly as possible. OLAP, on the other hand, focuses on reading data as quickly as possible to service business analytics. OLTP data is sent to the OLAP data warehouse for computations so as not to affect the real-time online users of the OLTP database that often number in the thousands.

OLAP works with large amounts of data stored in a data warehouse. This data is not real-time but is synced to be as relevant as possible to the decision it will support. Techniques such as data mining and big data analytics are used to gather intelligence from all the data stored in the data warehouse. Processing as such for OLAP data is very performance intensive. An online user would experience a degradation in the application’s response time if accessing real-time data. When to use OLAP – when you need help with decisions to analyze the business. Data warehouses are typically used by 100s of people at the same time.

What is OLAP Cube in Data Warehouses

An OLAP cube is a data structure in the data warehouse that is optimized for improving the performance of data analysis. An OLAP cube is sometimes referred to as a hypercube. OLAP cubes contain multidimensional data and information from different unrelated sources for logical and orderly analysis. The cube could incorporate different data types from multiple data sources that have been transformed. Subsequent analytical operations are performed on the data to create relationships with the other acquired data, including “slicing and dicing” the data to fit specific criteria to enable additional perspectives for decision support.

One of the challenges with OLAP is that it requires the use of complex schemas to implement and administer the technology. Managing and administering the cube is very time consuming, but it provides excellent value to the organization when done.

Use cases of OLAP in a Data Warehouse

How to use OLAP becomes a capability based on the creativity and expertise of the user. With all the data and information available in the data warehouse, including manipulating and viewing the data from many different perspectives, OLAP can become a critical capability needed by the business. OLAP in a data warehouse can help with:

  • Planning
  • Budgeting
  • Reporting
  • Various analysis
  • Asking “what if” questions
  • Business modeling
  • Creating data relationships that did not exist

OLAP is used to support the use of data in any way experts see fit for the decision that needs to be made for the organization. Many business applications can take advantage of OLAP capabilities, including different roles in the organization, viewing data and information from unique perspectives to enable dynamic decision making.

Actian can help OLAP users looking to simplify the BI life cycle. The Actian Vector analytics database provides a viable alternative to OLAP Cubes with its ground-breaking technology, superior performance and in-database analytic capabilities.

About Teresa Wingfield

As the Director of Product Marketing at Actian, Teresa Wingfield focuses on hybrid cloud data solutions. Prior to joining Actian, Teresa managed cloud and security product marketing at industry leaders such as Cisco, VMware, and McAfee. She was also Datameer’s first Vice President of Marketing where she led all marketing functions for the company’s big data analytics solution built on Hadoop. Before this, Teresa was VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from MIT’s Sloan School and software engineering from Harvard University.