TechD
New technologies have permanently transformed how customers communicate, interact, research and shop for goods and services. Retail data paired with retail analytics, can assist retailers in understanding and responding with actionable retail insights to the disrupted landscape and changing customer expectations.
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As more companies are leveraging the Data Lake to run their warehouse workloads, we’re seeing many companies move to an Open Data Lakehouse stack. The Open Data Lakehouse brings the reliability and performance of the Data Warehouse together with the flexibility and simplicity of the Data Lake, enabling data warehouse workloads to run on the data lake.
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Qlik offers solutions for creating analytics-ready data sets on the Databricks’ Unified Analytics Platform. They are designed to automate streaming data pipelines to make data seamlessly available to accelerate machine learning (ML), artificial intelligence (AI) and data science initiatives.
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tdwi.org
In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.
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