Enterprise data and analytics in the cloud with Microsoft Azure and Talend

Enterprise data and analytics in the cloud with Microsoft Azure and Talend

Oct 4, 2022

The emergence of the cloud as a cost-effective solution to delivering compute power has caused a paradigm shift in how we approach designing, building, and delivering analytics to business users. Although forklifting an existing analytics environment into the cloud is possible, there’s substantial benefit for those that are willing to review and adjust their systems to capitalize on the strengths of the cloud.

The key components required to support analytics aren’t different when comparing a company’s data center and the cloud (source data extraction, data integration, data storage, and query processing). However, the architecture of the cloud (with dynamically scalable processing engines, automated system monitoring, and concurrent support for multiple database engines) allows developers to approach system design and software development activities in a more streamlined and cost-effective manner. The cloud’s architecture and advanced functionality reduces the time-to-delivery while simplifying the management and delivery of new analytics solutions. The only obstacle is the developer’s desire to utilize these advanced features.

This presentation will discuss the challenges in migrating analytics workloads to the cloud along with a review of cloud-native principles and implementation best practices from Talend’s customers’ experiences with the Talend Cloud on Microsoft Azure.

You will learn:

  • Key aspects for migrating an existing analytics workload to the cloud
  • Data management practices involved with migrating analytics to a cloud environment
  • Cloud-native principles that separate compute from storage and incorporate serverless data architectures on Microsoft Azure
  • The use of data pipelines to replace the traditional ETL approach to data delivery and integration on Microsoft Azure
  • Architecture patterns and practices that enable scalability across agile data teams