HDFS is the primary data storage system used by Hadoop applications.
Files can be captured and copied or moved to a different location. CSV and XML files can be processed for a table target. As a target, HVR can write files in multiple formats including Parquet, JSON, Avro, CSV or XML with many options to fine tune the format and define compression. Compare is supported through Hive external tables, or directly by reading/parsing the files.
 | Key Features: See how change data capture, data validation and repair, and reporting all in one tool simplify data integration management. |
 | Table Creation and Initial Load. Mapping data between source and target is automated and made easy. |
 | Log-based Change Data Capture. Only the changes are moved between source and target, a low impact way to move data. |
 | Data Compare. Have assurance your data is accurate. HVR includes the ability to compare data before consumption. |
 | Insights: This tool gives you the ability to view how data is moving in real-time, be proactive and identify chokepoints. |
Interview from theCUBE HVR and Snowflake, Partners in the Cloud At AWS re:Invent 2019, HVR Partner, Chris Degan of Snowflake, and HVR CEO, Anthony Brooks-Williams, sat down with the hosts of theCUBE for a live chat about data movement, AWS, ...
Data Integration Architecture: Understanding Agents The question of whether or not to use an agent when performing data integration, especially around use cases with log-based Change Data Capture (CDC) and continuous, near real-time delivery, is common. In this video, HVR’...
Bi-directional data movement need not be feared when using HVR for real-time data integration. In this video, Glenn Goodrich, Director of Enablement, explains how bi-directional data movement can be accomplished efficiently, accurately, and in real-time with HVR. HVR includes a...
Whether you’re moving your data to a data lake or data warehouse, from on-prem to the cloud, we support it.
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