Meet us at the Databricks Data + AI Summit
Struggling to scale data quality checks in Databricks?
Two words: Pushdown queries.
It’s as simple as that. And we’re excited to show you how to get the most out of Databricks by using Lightup’s Prebuilt Data Quality Checks, AI-Powered Anomaly Detection, and integrated Incident Alerts.
Schedule a 10-minute strategy meeting today, ask us anything at our booth #823.
Let’s talk data quality for Databricks.
When: June 26 - June 29, 2023
Where: Moscone Center, 747 Howard St, San Francisco, CA 94103
Join Our Featured Customer Session
at the Data + AI Summit by Databricks
Learn How McDonald's Leveraged Lightup Data Quality to Deploy Thousands of Checks in Under a Year — Without Developer Cycles
For the McDonald's data and analytics team, developing manual data quality checks with legacy tools was too time-consuming and resource-intensive. It required developer support and data domain expertise.
Join our featured customer session, where you’ll hear from Matt Sandler, Senior Director of Data and Analytics at McDonald’s, about how they use the Lightup Deep Data Quality platform to deploy pushdown data quality checks in minutes, not months — without developer cycles.
During the session, you’ll learn:
- The key challenges of scaling Data Quality checks with legacy tools
- Why fixing data quality (fast) was critical to launching their new loyalty program and personalized marketing initiatives
- How quickly McDonald’s ramped up with Lightup, transforming their data quality struggles into success
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Better Together
Lightup Lakehouse-Native Data Quality Checks for Databricks
AI-Powered Anomaly Detection
Proactively monitor data health at fine granularity, and quickly pinpoint bad data flowing in good data pipelines to ensure reliable, accurate business-critical data and prevent costly outages, before they occur. Identify data issues and outliers with AI-powered anomaly detection, with back- testing and previews for easy fine-tuning.
Modern Architecture
Unlike legacy data quality tools with data pull or "extract and inspect" architecture, Lightup uses a modern pushdown architecture. Lightup pushes prebuilt data quality SQL queries to Databricks, without moving or copying data. Aggregate metrics or Data Quality Indicators (DQI) are pulled from Databricks for anomaly detection and rule testing in Lightup.
Enterprise Scalability
Scale data quality checks across the entire Databricks environment with out-of-the-box deep data checks on actual data (not just metadata), without requiring additional data warehouse storage or new Spark Clusters. Efficient time-bound aggregate queries help scale data quality workloads quickly, with in-place computing in Databricks, not Lightup.
What Our Customers Are Saying
“Lightup has accelerated our ability to define data quality thresholds and metrics which has dramatically condensed the iterative development lifecycle. Now our ability to identify anomalies and resolve issues is much faster, resulting in better data for our business use cases.”
— Data Quality Leader at Fortune 500 Restaurant Enterprise