Prerequisite: None

James G. Kobielus

Senior Director of Research for Data Management

TDWI

Success in the modern economy depends on an enterprise’s ability to deliver high-quality data analytics into production applications.

Data and analytics workloads continue to grow. Having scalable back-end pipelines ensures that such mission-critical functions as change data capture, data ingestion, and extract, transform, and load keep pace with the expanding volume, variety, and velocity of data and analytics workloads.

In this keynote, James Kobielus, TDWI senior research director, will discuss how enterprise data and analytics professionals should scale their pipelines, covering such proven techniques as:

• Migrating data and analytics pipeline workloads to elastic, no-copy, multimodel cloud computing platforms
• Separating storage and compute resources for processing data and analytics pipeline workloads
• Orchestrating multiple interdependent data and analytics pipeline workflows
• Leveraging serverless functions for on-demand processing of data and analytics pipeline workloads
• Optimizing data and analytics pipeline processes for both low-latency and batch processing
• Enabling centralized observability over the end-to-end data and analytics pipeline
• Using embedded AIOps features to automate data and analytics pipelines from end to end