Strategies for Fitting a Data Lake into a Modern Data Architecture

In modern data architectures, we’re increasingly moving from single node design systems to distributed architectures using multiple nodes often spread across multiple databases and multiple continents. Such architectures bring many benefits (such as scalability and resiliency), but can also bring a lot of pain if incorrectly architected.

The migration to cloud-based data architectures continues at a rapid pace, including databases and data management. Oracle databases are part of this trend, and during this webinar you will learn how to automate the provisioning and management of Oracle databases so that you can deliver an “as-a-service” experience with 1-click simplicity. Experts will walk you through the process of

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.

When you need to monitor and control variation in your process, a control chart will help you to separate special causes from common cause variation or random variation. Yet how do todays types of data, including full inspection, impact this tool's effectiveness? The control chart was created in the 1920s, a time when measurement, data capture and calculation were done manually, making sampling essential. Today, how data is collected has all changed.