McKnight Consulting Group
Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake a place for unmodeled and vast data will be provisioned widely in 2019. Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
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Modern data applications and analytics rely on a wide variety of data both inside and outside the company; organizations depend on enriched data sets for better insights. This need, in part, has driven many companies to move to cloud data warehouses and cloud data lakes. However, it’s no longer simply about migrating to the cloud. It’s about modernizing using a combination of industry-leading services in the cloud and cloud-native data management services to deliver better business decisions, faster.
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alation.com
In an age of data lakes, enterprise data catalogs are becoming a necessity. Done right, machine learning data catalogs can facilitate discoverability and ensure compliance while fostering the user engagement needed to build a robust data culture. In this webinar, Andrew Brust, analyst at GigaOm Research, and Aaron Kalb, co-founder and chief data officer at Alation, discuss why people are an essential part of successful self-service analytics, and how data catalogs can help foster collaboration and analytics enthusiasm.
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If there is anything that current events prove, it’s that analytics based on the quick delivery of up-to-date, trustworthy data is critical. But getting the right insight precisely when you need it demands more than a pretty visualization.
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