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Operationalize Your Data Lake to Accelerate Business Insights

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2 www.zaloni.com Introduction Enterprises today generate and have access to huge volumes of data from a mul tude of sources. Whereas businesses used to consider their data primarily a cost, requiring funding for ever-increasing amounts of storage, now most enterprises consider their data an asset—and are looking for new ways to leverage it for compe ve advantage or to improve the bo om line. It's our posi on that data lakes—centralized repositories for raw data from mul ple sources that can be made available to many users for nearly any purpose—will become essen al to the modern data architecture. Data lakes will be fed by various structured data sources, real- me data streams, such as from the Internet of Things, and unstructured data like emails, videos, photos, audio files, presenta ons and more. All of the data will be stored in this centralized repository—whether in the cloud or on premises or a hybrid—where it can be transformed, cleaned and manipulated by data scien sts and business users. Then, prepared datasets can be fed back into a tradi onal enterprise data warehouse for business intelligence analysis, or to other visualiza on tools for data science, data discovery, analy cs, predic ve modeling and repor ng. Although enterprises have been using data for business intelligence and marke ng for decades, the volume, types and real- me availability of data that is generated today, as well as modern data architectures and tools that can accommodate and analyze all of this data, are changing the ways enterprises can derive value from data. It is the inherent and essen al flexibility of data lakes that promises to give enterprises the agility and scalability they require to discover mely, valuable business insights from big data. This paper will discuss: · The benefits of a data lake · A holis c approach to opera onalize the data lake · The most common use cases for data lakes today · Example customers who have successfully implemented a data lake Benefits of a data lake approach The benefits of integra ng a data lake into your overall data architecture can be significant. 1. Cost-savings Save millions in storage costs and data processing Scale-out architectures (e.g., Hadoop, S3) can store raw data in any format at a frac on of the cost of a tradi onal enterprise data warehouse (EDW). In fact, we helped one client achieve 20 mes the storage capacity of their EDW at 50% of the cost of a previously planned EDW upgrade. Another client achieved a 100x cost The promise of the data lake is to allow more cost-effective storage, access and management of all the data in an enterprise. A governed data lake has the goal of providing a comprehensive and governed view of data within the organization.

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