🧭Scheduler
No data and analytics solution is complete without data and model ops automation.
Data Ops and Model Ops are set of key capabilities required to operationalize and observe (monitor) your data engineering and AI/ML workflows. These capabilities include a multi-step workflow creation, time and condition based scheduler to trigger the workflows, health monitoring of workflows, notifications of failure, logging and audits of data at each step of the workflow, and data lineage to track data dependencies.
Sounds exhausting? Well, without these capabilities, one cannot ensure repeatability and reliability of Data & Model Ops, supporting your analytics outputs.
Many analytics tools stop at configuration of the analytics, but leave the automation and monitoring of analytics to other tools. Clarista on the other hand provides comprehensive set of capabilities outlined above to automate, monitor and audit Data & Model Ops. These integrated capabilities improve time to market, reduce cost of multiple software and provide faster diagnostics and recovery from failures.
Key Features:
Clarista Scheduler provides following key capabilities to configure, automate and monitor data and model workflows:
Multi-step workflows combining Clarista Flows and Clarista Alerts
Ability to execute each step based on the status of the previous step
Flexible options to trigger the workflows based on time, data count or condition
Email notifications based on status of the workflow
Monitoring of each execution run
Audit of each transformation step for Clarista Flows
Links to Clarista Flows and Clarista Alerts for lineage
How is Clarista Scheduler different from other data operations and observability tools?
Efficiency, Transparency and Faster Resolution - Ask any data team and they will highlight the operationalization and maintenance of Data and Model Ops as their biggest challenge. Cloud native capabilities are good for automation, but do not provide business transparency or or impact assessment when processes fail. Third party tools have been split among automation, observability and data quality. Fact is that for data teams, these are all interconnected topics, best addressed through a configurable and no-code unified solution.
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