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Automate analytics-ready data pipelines
Qlik Compose for Data Lakes (formerly Attunity Compose) 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.
Universal data ingestion
Supporting one of the broadest ranges of data sources, Qlik Compose for Data Lakes ingests data into your data lake whether it’s on-premises, in the cloud, or in a hybrid environment. Sources include:
- RDBMS: DB2, MySQL, Oracle, PostgreSQL, SQL Server, Sybase
- Data warehouses: Exadata, IBM Netezza, Pivotal, Teradata, Vertica
- Hadoop: Apache Hadoop, Cloudera, Hortonworks, MapR
- Cloud: Amazon Web Services, Microsoft Azure, Google Cloud
- Messaging systems: Apache Kafka
- Enterprise applications: SAP
- Legacy systems: DB2 z/OS, IMS/DB, RMS, VSAM
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.
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.
Leverage the latest technology
Take advantage of Hive SQL and Apache Spark advancements including:
- The latest Hive SQL advancements including the ACID MERGE operation that efficiently processes data insertions, updates, and deletions while ensuring data integrity.
- Pushdown processing to Hadoop or Spark engines. Automatically generated transformation logic is pushed down to Hadoop or Spark for processing as data flows through the pipeline.
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.
