CloverETL Cluster

Scale up to multi-node architecture for parallel data processing and to assure high availability and geographic “close to data” distribution.


Speed up performance with parallel data transformations, splitting massive data sets into parallel streams. Ensure best possible performance, reliability and availability of your data integration platform.


  • Step-up performance. Go parallel.

    Distribute jobs across multiple worker nodes to speed up processing. If you’re facing large numbers of small transactions, Cluster will balance the jobs uniformly across either all available nodes or to preconfigured allocations. The opposite scenario, where you process large chunks of data in a single transformation, works very well on Cluster too. Just like with a single Server, you can have sections of transformations designed to run in parallel, on multiple cores and, with Cluster, across multiple nodes too.

  • Ready for Cloud

    CloverETL Cluster is easily deployed on any public or private cloud. We have installations running on Amazon AWS, Google, Rackspace or Openstack. Cloud enables you to dynamically scale processing power and geographical availability of individual cluster nodes. The Cluster can be managed centrally from any single node. Additionally, there is no master node, thus no single point of failure.

  • Assure High Availability

    Cluster nodes can take over work loads from their peers that go offline for some reason, ensuring the data integration platform, regular data loads, real-time web services are always available under stringent SLAs.

  • Chunking data to multiple nodes

    You can choose to keep files partitioned on multiple Cluster nodes to speed up processing, CloverETL will automatically split execution of transformations to corresponding nodes where individual parts are stored. You can run parallel loaders to external storages, using multiple streams without serializing the data. Check back with us on availability of this feature for your system.

  • Data locality matters

    Cluster gives you fine controls over allocation of jobs (and even individual operations) to nodes to make sure processing happens as close to the physical location of your data set as possible. This becomes useful when resources are close to specific nodes or directly reside on them, while other nodes would need remote access, possibly resulting in less than optimal speeds.

CloverETL pricing

Includes Subgraphs and Data Quality packages. Includes embedded runtime for manual execution of jobs.

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