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RESEARCH & RESOURCES

Available On-Demand - This webinar has been recorded and is now available for download.

Next-Generation Data Engineering in the Evolving Cloud

Webinar Speaker: James Kobielus, Senior Research Director, Data Management

Date: Tuesday, May 25, 2021

Time: 9:00 a.m. PT, 12:00 p.m. ET

High-quality data is the heart of modern business. It is the essential ingredient in products and services of every type, ranging from the unstructured data needed to build and train artificial intelligence (AI) apps to the “single version of the truth” relational data that drives optimized decisions in every business context.

Robust data engineering processes ensure that data is accurate, relevant, and easily scalable to operationalize regular, ad hoc, and advanced analytics. To realize this value, organizations are implementing cloud-based pipelines that automate ingestion, transformation, cleansing, consolidation, governance, and other key data engineering processes. They are also building end-to-end data engineering pipelines that scale as workloads expand, integrate batch and streaming processes, feed both data warehouses and data lakes, and span decentralized cloud environments of increasing complexity.

Join TDWI’s senior research director James Kobielus on this webinar to explore the challenges and benefits of implementing robust data engineering “supply chains” in the cloud. Attendees will learn the following best practices for deploying robust data engineering pipelines in the next-generation cloud:

  • Building data pipelines across logical data zones to support multiple workloads with staging, cleansing, transformation, and other key processes
  • Scaling processing, storage, and other cloud resources to support expanding workloads across the cloud-based data pipeline
  • Deploying fine-grained centralized governance over structured, unstructured, and other types of data processed in a cloud-based data pipeline
  • Implementing a unified engine for analytical and transformational processing on cloud data platforms

James Kobielus


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