Automate analytics-ready data pipelines
Qlik Compose for Data Lakes automates your data pipelines to create analytics-ready data sets. By automating data ingestion, schema creation, and continual updates, organizations realize faster time-to-value from their existing data lake investments.
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DataOps for Analytics
Modern data integration delivers real-time, analytics-ready and actionable data to any analytics environment, from Qlik to Tableau, Power BI and beyond.
Real-Time Data Streaming (CDC)
Extend enterprise data into live streams to enable modern analytics and microservices with a simple, real-time and universal solution.
Agile Data Warehouse Automation
Quickly design, build, deploy and manage purpose-built cloud data warehouses without manual coding.
Managed Data Lake Creation
Automate complex ingestion and transformation processes to provide continuously updated and analytics-ready data lakes.
Enterprise Data Catalog
Enable analytics across your enterprise with a single, self-service data catalog.
Easy data structuring and transformation
An intuitive and guided user interface helps you build, model and execute data lake pipelines.
- Automatically generate schemas and Hive Catalog structures for operational data stores (ODS) and historical data stores (HDS) without manual coding.
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Continuous updates
Be confident that your ODS and HDS accurately represent your source systems.
- Use change data capture (CDC) to enable real-time analytics with less administrative and processing overhead.
- Efficiently process initial loading with parallel threading.
- Leverage time-based partitioning with transactional consistency to ensure that only transactions completed within a specified time are processed.
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Get Live Views of data
Generate cost-effective low-latency views of data by:
- Merging the latest unprocessed changes in the change table (including the last open partition), on Read.
- Optimizing compute by creating “live views,” both ODS and HDS, without processing changes every time.
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Historical data store
Derive analytics-specific data sets from a full historical data store (HDS).
- New rows are automatically appended to HDS as data updates arrive from source systems.
- New HDS records are automatically time-stamped, enabling the creation of trend analysis and other time-oriented analytic data marts.
- Supports data models that include Type-2, slowing changing dimensions.
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