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Modernizing the Analytics Data Pipeline

Enterprises run on a steady flow of best-fit data analytics. Robust processes ensure these assets are always accurate, relevant, and fit for purpose. Increasingly, organizations are implementing these processes within structured development and operationalization “pipelines.” Typically, analytics data pipelines include data engineering functions such as extract-transform-load (ETL) and data science processes such as machine-learning model development.

Hybrid Data Delivery "Cloud Sources" Walkthrough

We have expanded our Hybrid Data Delivery service to load analytics ready data, from a number of cloud-based data sources, directly to snowflake - without the need for Qlik replicate. This initial update currently allows you to connect to data from over 20 cloud-based data sources such as Amazon Redshift, Google BigQuery, and Salesforce and land it directly to a Snowflake as a target on a scheduled basis, so it can be used with your analytics applications – offering a single solution for on-prem and cloud data movement and replication.