Qlik Compose® for Data Lakes

The quickest route to analytics-ready data lakes.

Speed up and streamline the process of designing, developing, testing, deploying, and updating data warehouses.

Automate data pipelines ready for analytics

Conventional approaches to constructing and overseeing data warehouses struggle to meet the evolving demands of businesses. The lengthy and error-prone Extract, Transform, Load (ETL) development process, consuming a significant portion (usually 60-80%) of preparation time, frequently results in an outdated data model before the commencement of a Business Intelligence (BI) project. Modifying these fragile data warehouses leads to further delays, ties up valuable resources, and postpones the return on investment (ROI) for the project.

To expedite the journey to analytics, it is imperative to enhance and streamline the entire lifecycle of data warehouse creation and management wherever feasible.

Effortless structuring and transformation of data

A user-friendly and guided interface assists in constructing, modeling, and executing data lake pipelines.

Automatically create schemas and Hive Catalog structures for Operational Data Stores (ODS) and Historical Data Stores (HDS) without the need for manual coding.

Ongoing updates

Have assurance that your Operational Data Stores (ODS) and Historical Data Stores (HDS) faithfully mirror your source systems.

  • Employ change data capture (CDC) to facilitate real-time analytics with reduced administrative and processing overhead.
  • Conduct the initial loading efficiently through parallel threading.
  • Utilize time-based partitioning with transactional consistency to guarantee that only transactions completed within a specified time frame are processed.
 
 

Live Views of data

Create cost-effective, low-latency views of data through the following methods:

  • Merge the most recent unprocessed changes in the change table (including the last open partition) during reading.
  • Optimize computation by establishing “live views” for both Operational Data Stores (ODS) and Historical Data Stores (HDS) without the need to process changes every time.

Historical data store

Generate datasets tailored for analytics from a comprehensive Historical Data Store (HDS).

  • Automatically append new rows to the HDS as updates arrive from source systems.
  • Automatically timestamp new HDS records, facilitating the development of trend analysis and other time-oriented analytic data marts.
  • Supports data models incorporating Type-2, slowing changing dimensions.