Increasing volumes and varieties of data, combined with self-service data access, is overwhelming existing reporting tools and infrastructure. To scale for digital era demands, organizations are adopting new cloud #Hadoop -based, #datalake architectures and next generation OLAP semantic layers. As early adopters make this move, they are learning from expensive “lift and shift” failures. “Lift and optimize” approaches are proving to be far more cost-effective and successful. #OLAP is not dead Contrary to the rumors of OLAP’s impending death when in-memory analytics entered the market years ago, OLAP is still not dead. The rules never changed. Exponential growth in data sources, varieties and types did rapidly surpass what traditional OLAP solutions were designed to handle. Thus, new OLAP on Hadoop technologies such as Apache Kylin, AtScale, Kyvos Insights, and other similar solutions were invented for the big data analytics world. Today OLAP is in high demand. Last year, a CEO of a large system implementation firm in the United States expressed the unicorn was not a data scientist. Their firm received over 300 data scientist resumes for one open role. They could not find any OLAP talent with classic Kimball technique, dimensional modeling skills. Surprisingly, the unicorn was someone with OLAP design skills. Assess and optimize for the cloud There are many benefits for both the business and IT to use new OLAP on Hadoop solutions. Speed, scale, simplicity and saving money on cloud bills are usually cited as the top reasons for migration from legacy on-premises OLAP offerings. OLAP on Hadoop solutions make big data analytics easy and familiar. The business can use it within mainstream self-service BI tools such as Excel, SAS, Tableau, Qlik, and TIBCO Spotfire that they already know and love. Without a user-friendly, governed semantic layer that can intelligently cache and pre-aggregate, self-service big data analytics would be overwhelming, frustrating, and expensive. Keep in mind that cloud analytics can be billed by compute or query scan usage.
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