Databricks Cluster Management with Acceldata

Enterprises are increasingly prioritizing data quality and reliability to develop data products and services. To achieve this at scale, they are employing data observability. For those using Databricks, the Acceldata Data Observability platform provides an optimal structure for ensuring data reliability and a framework for optimizing performance and cost. The integration with Databricks gives users comprehensive operational observability into their Apache Spark deployments. Databricks functions as the de-facto cloud platform for Spark, allowing users to perform cluster management and deploy Spark applications in cloud environments. It is an open-source unified data analytics engine for large scale data processing and supports Spark users with expressive (Scala and Python) and simple (Spark SQL) ways of working with petabyte-scale data. Acceldata helps enterprise data teams observe their cluster and the performance of their jobs, enabling them to implement data reliability techniques for Delta Lake on Databricks. The platform provides visualization for spend tracking, cluster or compute usage, workflow debugging, and data insights. It also offers actionable insights, anomaly detection, alerting, reporting, and guardrail recommendations for robust administration of Databricks accounts.