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DESCRIPTION:Click for Latest Location Information: http://edw2021.dataversi
 ty.net/sessionPop.cfm?confid=133&proposalid=12877\nMany companies are now c
 hanging their data architecture to create a central data lake on cloud stor
 age or a logical data lake made up of multiple data stores to ingest and pr
 ocess data for use in multiple analytical environments. Data lakes wer
 e initially seen as a place where raw data could be brought together to sup
 port data science. However, many organizations see this as too restric
 tive when there are many other purposes that a valuable collection of data 
 could be used for – for instance, in a data warehouse. This
  session shows how companies can create a multi-purpose data lake to enable
  rapid delivery of data warehouses, data marts, MDM, RDM, Customer Data Pla
 tform, and data science. It shows how data assets can be published in a cat
 alog to make them findable and how you can link these assets together as co
 mponents to rapidly build data and analytical pipelines for competitive adv
 antage.\nWe will cover: \n\n
 What a data lake is and how have they evolved\n
 Why create a data lake? The data science use case\n
 Limitations of a single-purpose data lake\n
 The benefits of a multi-purpose data lake\n
 What is needed to build a multi-purpose data lake\n
 Key technologies in a multi-purpose data lake\n	\n
 The data management platform/data fabric\n		The data catalog\n
 Machine learning and advanced analytics\n		Data virtualization\n	\n	\n
 Building trusted reusable data and analytical assets using pipelines\n
 From data lake to multi-purpose logical data hub – publishing assets 
 to a catalog to fuel reuse\n
 Orchestrating ready-made data and analytical assets in pipelines to rapidly
  deliver high-value insights for competitive advantage\n\n
DTSTART:20210420T080000
SUMMARY:T12: Building a Multi-Purpose Data Lake on the Cloud – The Engine R
 oom of the Data-Driven Enterprise
DTEND:20210420T105959
LOCATION: See Description
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