Qlik Use Case Reference Architecture - #1: Data Warehouse Reference Architecture

[Anand_Rao]

Meet or exceed the demands for analytics-ready data marts that enable data-driven insights at the speed of change.

Qlik Data Integration (QDI) delivers on the promise of agile data warehousing with automation that allows users to quickly design, build, deploy, manage and catalog purpose-built data warehouses (especially cloud-based) faster than traditional solutions. Consequently, data engineers can meet or exceed the demands for analytics-ready data marts that enable data-driven insights at the speed of change.

[Anand_Rao]

The architecture comprises the following components:

Data is ingested from transactional systems with low latency. Change data capture for real-time data replication ingests data without impairing production system performance.

Data Warehouse Automation capability accelerates the availability of analytics-ready data by automating the entire data warehouse lifecycle.

Custom Transformation allows users to create flexible, fit-for-purpose data pipelines to transform raw data into data that is ready for analytics.

Data Profiling enables users to assess the quality and structure of data sources to fix data quality issues and promote good data governance.

Machine Learning enriches data with prediction, scoring, classification, and more.

Catalog & Lineage capabilities empower users to discover, govern, and protect data using AI and machine learning built on a layer of common enterprise metadata.

Analytics is used to discover, interpret, and communicate meaningful patterns in data to apply toward effective decision making

Reverse ETL replicates enriched data from the warehouse back to the operational systems of record.