Anomalo: Data Quality Monitoring In Databricks

Anomalo x Databricks

The Data Quality Partner for the Data Intelligence Platform

The only data quality solution that integrates seamlessly with Unity Catalog and can monitor any table in Databricks across SQL Warehouse, Spark, and Delta Lake.

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What joint customers are saying

“Anomalo has been the silver bullet in helping us promote trust in data across our organization. Since migrating to Anomalo, it is easy to detect false positives and has removed dependencies on data engineering. This makes both my data engineers and data consumers happy as it means less time fire-fighting issues, and more time using data to build products our customers love.”

Tim Ng

Data Products Engineering Lead at Block

Overview

Anomalo x
Databricks

Anomalo is one of the largest and fastest-growing data quality partners for Databricks, and the only data quality partner that Databricks Ventures has invested in.

Customers choose Databricks to leverage the power of data and AI for decision-making and enhanced customer experiences. This puts trust in your data at the forefront of everything you do with Databricks. As one of the earliest Partner Connect integrations, Anomalo has long worked seamlessly with the Databricks Data Intelligence Platform. Anomalo’s AI-powered data quality monitoring automatically detects data quality issues and their root causes before they affect BI dashboards and reports or downstream AI models.

Customers can begin using Anomalo to monitor any Databricks table across SQL Warehouse, Hive Metastore, and Spark in a matter of minutes, without needing extensive, time-consuming and manual configuration. Our bi-directional integration with Unity Catalog makes it easy to view data quality issues directly in UC’s Data Explorer, and resolve them quickly using Anomalo’s lineage graph and Root Cause Analysis. For joint Databricks Customers like Lebara and Casey’s, this means less time firefighting issues and more time making data-driven decisions.

Databricks Ventures invested in Anomalo’s Series B funding raise. “With data quality at the center of every organization’s data strategy, Anomalo has been a standout partner for Databricks and for customers leveraging our Data Intelligence Platform,” said Andrew Ferguson, VP of Corporate Development and Ventures at Databricks.

Results

Monitor any Databricks table across the entire Data and AI lifecycle

Monitor any Databricks Table across SQL Warehouse, Hive Metastore, and Spark

Drastically reduce the operating costs of building and maintaining manual checks or tests. Anomalo’s AI-based monitoring seamlessly integrates with the Data Intelligence Platform.

Extend the value of Unity Catalog with our native bi-directional integration

Anomalo pushes data quality results directly into Unity Catalog’s Data Explorer UI, which serves as trust signals for their most important tables. We leverage metadata from Unity Catalog for Anomalo’s table observability checks and lineage.

Available via Partner Connect

Access an exclusive free trial of Anomalo via Databricks Partner Connect

Orchestrate Anomalo checks with Databricks Workflows, and fix impacted data with Databricks Notebooks

Use Anomalo to block workflows from completing if quality issues are found. Anomalo’s root cause analysis also isolates anomalous and low-quality data for fast resolution via Databricks Notebooks or SQL.

Enable new AI, ML, and analytics use cases

Data quality is the #1 reason leadership doesn’t invest in more advanced data use cases. With Anomalo’s monitoring, Databricks customers can improve trust and confidence in their data.

Monitor data across the entire medallion architecture with Deta Live Tables

While the medallion architecture ensures that your data is progressively structured and curated, you might still have issues lurking in the data itself. With our Python SDK and API, you can easily monitor data at the bronze or silver layer before it continues through your pipelines.