Orchestrate End-to-End Data Warehouse Processes

ActiveBatch Integrated Jobs Library: Reduce Scripting with Infinite Extensibility

Streamline your data with prebuilt connectors, a super-charged REST API adapter, and ActiveBatch’s Service Library. Execute stored procedures, command lines, .NET assemblies, and more. Build reliable, end-to-end workflows that manage data and dependencies across any system or technology (IoT, artificial intelligence, RPA):

Event-Driven Architecture: Advanced Scheduling for Data Center Operations

Eliminate manual tasks with event triggers for data processes, including email, file events, FTP file triggers, database modifications, and more. Run jobs at specific times with granular date/time scheduling options, custom calendars, multiple time zones, and custom tags. Reduce delays and false starts with constraint-based scheduling and improve SLAs with proactive monitoring and auto-remediation.

Lifecycle Management Tools: Built-In Collaboration for Seamless DevOps

Simplify DevOps with centralized logging, self-documenting Job Steps, and in-depth reporting. Streamline management and continuous delivery with templates, variables, and references that enable IT to reuse logic and pass runtime values without managing thousands of individual objects.

Big Data and Hadoop Automation

ActiveBatch simplifies the development and maintenance of processes through a unique, templated approach to automating and integrating the Hadoop Ecosystem. ActiveBatch Workload Automation runs within the framework of Hadoop grids or clusters from prominent vendors such as Cloudera, MapR, Hortonworks, Amazon, and others, with support for Pig, HBase, Spark, Oozie, Hive, and more.

Smart Queue and Heuristic Queue Allocation: Intelligent, Automated Data Center

Automated infrastructure management across on-premises, hybrid cloud, and multi-cloud environments. Provision and deprovision servers in real time to reduce downtime, automatically pair workloads with servers best fit for the job, or leverage machine learning to optimize the distribution of workloads across multiple platforms.