Your Fastest Way to Analytics-Ready Data Lakes

Automate Your Data Pipeline

Attunity Compose for Data Lakes automates the data pipeline to create analytics-ready data.  By automating data ingest, Hive schema creation, and continuous updates, organizations realize faster value from their data lakes.

Universal Data Ingestion

Attunity supports the industry’s broadest range of sources and targets, enabling you to ingest data into your data lake on premises, in the cloud, or for hybrid environments. Sources include:

  • RDBMS: Oracle, SQL, DB2, MySQL, Sybase, PostgreSQL
  • Data warehouses: Exadata, Teradata, IBM Netezza, Vertica, Pivotal
  • Hadoop: Hortonworks, Cloudera, MapR
  • Cloud: AWS, Azure, Google Cloud
  • Messaging systems: e.g., Apache Kafka
  • Applications: e.g., SAP
  • Legacy: IMS/DB, DB2 z/OS, RMS, VSAM

Easy Data Structuring and Transformation

Automatically generate the schema and structures in the Hive Catalog for Operational Data Stores (ODS) and Historical Data Stores (HDS) – with no manual coding.

  • Intuitive and Guided Process for modeling, execution and updates
  • Pushdown Processing to Hadoop.  All necessary transformation logic is generated automatically and pushed down to Hadoop for processing as data flows through the pipeline

Continuous Updates

You can be confident that your ODS and HDS accurately represents your source system.

  • Use change data capture process (CDC) to enable true real-time analytics with less administrative and processing overhead
  • Efficiently process Big Data loads with parallel threading
  • Time based partitioning with transactional consistency ensures that only those transactions that are completed within the time specified are processed

Ensure Data Consistency

Leverage the latest SQL advancements in Hive, including the new ACID MERGE operation, to efficiently process data insertions, updates and deletions while ensuring data integrity and avoiding user impact.

Slowly Changing Dimensions

To analyze historical data, your HDS can support Type 2 slowly changing dimensions and append new rows as updates arrive from source systems.  This new record is time stamped, enabling you to easily perform trend analysis and other time-oriented analytics.

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