ANOW! Automate orchestrates Azure Databricks workloads, enabling unified monitoring, event-driven job execution, and cost governance across hybrid cloud environments. We provide tasks for managing clusters, running jobs, and monitoring activity, ensuring predictable Azure spend and enterprise-grade SLA protection for critical data pipelines.
The ANOW! Automate integration with Azure Databricks facilitates comprehensive operational control over your data analytics and machine learning platform within Azure. It offers bidirectional interaction, allowing ANOW! to trigger Databricks jobs, manage compute resources like clusters, and monitor their status, while also receiving operational feedback from Databricks for unified observability. Specific tasks include 'Azure DataBricks Run Job' for executing code, 'Azure DataBricks Start Cluster' and 'Terminate Cluster' for resource lifecycle management, and 'Azure DataBricks Cluster Monitor' and 'List Clusters' for real-time operational oversight.
The integration functions through a Databricks agent, which runs on a Server Node with Azure connectivity or an edge node of the Databricks cluster. This agent is essential for connecting the cluster to Azure and managing all ANOW!-initiated tasks. Access to the Azure Databricks workspace is established via a defined Endpoint, which is the URL used to access the workspace, enabling the management of data engineering, analytics, and machine learning tasks. This architecture ensures secure and reliable communication between ANOW! Automate and your Databricks environment.
This integration is designed for enterprise IT decision-makers, data engineering teams, and FinOps professionals managing complex hybrid IT landscapes. This is especially helpful for organizations looking to improve Databricks operational efficiency, ensure compliance, and manage costs predictably across their data platforms, without needing expensive infrastructure overhauls.
Consolidate Databricks jobs, notebooks, and pipelines into a single operational dashboard alongside all other workloads. This provides native access to run status and logs, enabling faster incident detection and reduced Mean Time to Resolution (MTTR) across your entire data platform.
Orchestrate the full lifecycle of Databricks clusters and SQL warehouses. ANOW! starts resources just before a workload and terminates them afterward, eliminating idle compute costs and enabling organizations to demonstrate active governance of Databricks consumption to finance and FinOps teams.
Replace rigid schedules with real-time, event-driven orchestration. ANOW! launches the relevant Databricks job the moment a prerequisite is met, ensuring processing always uses the most current data and delivering results without artificial delays, improving data freshness and SLA attainment.
Apply dynamic SLA models to Databricks workloads, continuously recalculating critical paths and flagging jobs at risk of breaching targets. ANOW! executes predefined remediation actions, such as targeted retries or controlled escalation, ensuring measurable improvements in SLA attainment.
Use Cases
DATA ENGINEERING
Orchestrate complex data pipelines spanning ingestion from various sources to processing in Databricks and delivery to BI platforms.
FINOPS
Automate the lifecycle of Databricks clusters and SQL warehouses to optimize compute costs.
MLOPS
Coordinate full MLOps lifecycles, from feature engineering to model deployment and monitoring.
DATA GOVERNANCE
Centrally enforce dependencies and validate data layers across Databricks medallion architectures.
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Our architecture is designed to comply strictly with EU regulations, such as GDPR. With local support and implementation partners, we ensure our customers in DACH, Benelux, and Nordics have complete data sovereignty and compliance peace of mind for their data processing and storage.
Yes, our solution is designed for seamless, no-rip-and-replace integration. It enhances your current investments by coexisting with your existing tools and providing a unified view, ensuring a smooth transition with minimal operational disruption.
ANOW! Automate can manage Databricks jobs, notebooks, and clusters. This includes tasks such as running jobs, starting and terminating clusters, monitoring cluster status, and listing active clusters, providing granular control over your Databricks environment.
ANOW! enables cost-aware orchestration by right-sizing compute per workload, enforcing approval gates for high-cost runs, and ensuring clusters are promptly idled or terminated post-completion. This leads to controlled, predictable Azure spend and demonstrable governance of Databricks consumption.
ANOW! provides unified monitoring by consolidating all Databricks activity into a single operational dashboard. This offers native access to run status, logs, and cluster health, ensuring real-time visibility and efficient resource control across all your data estate components.
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ANOW! Automate integrates with dbt (Data Build Tool) to orchestrate and monitor data transformation workflows, supporting both dbt Core and dbt Cloud instances. This enables unified control over dbt runs, models, and tests, providing native access to run status and execution logs, ensuring data freshness and audit readiness.
ANOW! Automate integrates with Azure Data Factory (ADF) to provide unified orchestration and control over your data pipelines within hybrid environments. This integration allows ANOW! Automate to trigger and execute both Azure Data Factory Triggers and Pipelines, consolidating fragmented data operations into a single operational dashboard for faster incident detection and consistent status views.
