The Missing Link in Data Governance

Key topics discussed during the webinar:
*Data in Motion pitfalls and opportunities
*Validating data doesn’t equate to slowing down your process
*Comparing and contrasting approaches
*The missing link in data governance explained
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Spotlight

OTHER ON-DEMAND WEBINARS

Analyze Data Faster with an Open Source Columnar Database

MariaDB

If you are looking for the scalability and performance needed to support interactive, ad hoc analytics on billions of rows – and with SQL – this latest Data Science Central webinar will show you how to combine distributed, columnar storage and parallel query processing with powerful aggregate functions to deliver faster time to insight using modern, on-demand analytics, as well as how to leverage the power of Kafka and Spark connectors to plug into existing data pipelines. We will discuss the architectural overview of columnar databases, share real-world use cases and give a live demonstration.
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Predictive Analytics vs. Data Science: Why We Built One and Not the Other

In this webinar, Heap’s head of Data Science, David Robinson, will talk about why we took a data-science-led approach to insights that starts with the questions teams actually care about. He’ll also talk about how a combined quantitative + qualitative platform gives teams the predictive information they need and the context that makes that information actionable.
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How to Operationalize Data Mapping for Engineering

In this webinar, we will discuss the importance of data maps in building a privacy program. We look at the gap between privacy and engineering teams, the problems of scaling privacy programs with a business process approach to data mapping, and how an application-led approach helps you develop dynamic & complete data maps.
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Expert Panel: Modernizing Your Data Warehouse and Analytics Ecosystem

TDWI research has found that organizations are increasingly modernizing their data warehouse environments. Often the current environment is not sufficient to support new analytics initiatives or they need to support new data types for analytics. Many enterprises are moving to the cloud as part of this journey. In fact, cloud data warehouses and cloud data lakes are already mainstream. The popularity of automated tools is growing as environments become more complex.
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