Databricks Data Quality Partner

Data Quality Monitoring for Databricks

Lightup connects directly to Databricks, with full support for Unity Catalog, providing no-code, low-code, and custom SQL Data Quality Checks to ensure that data processed and analyzed in Databricks is correct, complete, and consistent — without moving or copying data the old-fashioned way.

The accuracy, precision, and reliability of Databricks data, AI/ML applications, analytics, and services depend on the quality of the data it processes. Maintaining high-quality data is absolutely critical for running workloads in Databricks with accurate output and trustworthy insights.

Ensure Trusted Data in Databricks

To help drive high confidence and trust in Databricks data, enterprises need modern Data Quality Monitoring tools that are powerful, easy-to-use, extensible, and deeply integrated with Databricks.

Enterprises turn to Lightup Data Quality for Databricks to:

Accelerate time-to-market, deploying no-code and low-code assisted-SQL Data Quality Checks in minutes, not months.
Identify data quality issues in real time, find the root causes, and remediate problems, preventing data outages before they occur.

Pushdown Checks, Without Data Movement

Lightup deploys scalable Data Quality Checks 10x faster than legacy tools, optimized by:

Aggregate queries with in-place processing at the data source, without moving or copying data.
Time-bound pushdown queries, only using delta or incremental time-ranged data.
Partition-aware queries, only scanning specific partitions where data resides.

Query Governance

With Lightup, get full control over query governance, ensuring queries don’t scan too much data or run too long. Configure Lightup to colocate resources within Databricks while it’s on to run at optimal times, after jobs finish.

Integration Architecture

Lightup doesn’t scan more data than it has to, keeping every query efficient and scalable — without choking system performance, even on massive data volumes.

Key Benefits

Reliable Data and Insights

Increase confidence in Databricks data, making it the go-to trusted source for all data projects, increasing the reliability of analytics and insights for data-driven decision-making.

Fast Ramp-up, Fast ROI

Deploy Data Quality checks in less than 20 minutes vs. months, enabling data teams to reach Data Quality coverage goals 10x faster than legacy data quality tools — without developer cycles.

Easy Custom Queries

Create custom Data Quality queries with “SQL fragments,” simply adding business logic to system-generated assisted SQL checks — without learning a proprietary rule engine.

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Lightup Data Quality makes Databricks even better. The Lightup and Databricks integration enables our joint customers to monitor the quality of data in Databricks, instilling data trust on day zero.

— Lightup and Databricks Partnership

Lightup Data Quality Design Patterns for Databricks

Lightup supports multiple deployment patterns to fit how your Databricks pipelines are built — from scheduled batch checks to real-time streaming validation. Choose the pattern that matches your architecture.

Scheduled Checks

When processing pipeline transformation operations, such as going from one delta table to another, run Data Quality Checks on a schedule at different stages of the pipeline to catch issues before they propagate downstream.

Trigger Mode

When orchestrating ETL and declaring pipeline definitions, include closed-loop response actions before processing data further — such as quarantining bad data or breaking the pipeline upon Data Quality failures.

Delta Live Tables

When processing high-volume streaming data with no latency, deploy a structured streaming job in the Databricks cluster that continuously calculates Data Quality Indicators as new data arrives, with sub-second processing and analysis.

Resources

Blog

Docs

Get Started with Lightup Data Quality for Databricks

Proactively monitor and ensure the quality of your Databricks Lakehouse data with Lightup.

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