PALO ALTO, Calif., June 15, 2022 (GLOBE NEWSWIRE) — Anomalo, the comprehensive data quality platform company, today announced that Anomalo is available on Databricks Partner Connect. Partner Connect helps Databricks customers discover validated data, analytics and AI tools and integrate these tools directly into their Databricks Lakehouse. One of Databricks’ premier data quality partners, Anomalo is offering an exclusive free trial to Databricks customers so they can start monitoring their tables immediately to detect and cause data quality issues if they don’t. are not already Anomalo customers.
“Data quality has become a fundamental part of the modern data stack,” said Roger Murff, vice president, ISV Partners at Databricks. “Companies shouldn’t have to wait for data quality issues to pile up, when they become harder and more expensive to fix. With Databricks Partner Connect, we’re excited to help businesses be proactive about data quality, enabling them to try Anomalo in just a few clicks and instantly see the value of automated, accessible, high-coverage monitoring. .
Today’s announcement follows the recent announcement of Anomalo’s integration with Databricks and makes it even easier for shared customers to use Databricks and Anomalo together: https://www.globenewswire.com/Home/404Page?aspxerrorpath=/news-release/2022/05/23/2448670/0/en/Anomalo-Integrates-With-Databricks-to-Help-Enterprises-Build-Confidence -in-Their-Data.html
Databricks combines the best of data warehouses and data lakes to deliver an open, unified platform for data and AI. This helps organizations streamline their data ingestion and management and make that data available for everything from business decision making to predictive analytics and machine learning.
However, the quality of dashboards and data-driven products depends on the quality of the data that powers them. When scaling their data efforts, many companies are quickly faced with an unfortunate fact: much of their data is missing, outdated, corrupted, or subject to unexpected and unwanted changes. As a result, businesses spend time addressing issues with their data rather than unlocking the value of that data and are exposed to silent failures in data that might go undetected for months or longer.
Anomalo solves the data quality problem by monitoring enterprise data and automatically detecting and rooting data issues, empowering teams to resolve any issues with their data before making decisions, executing operations or to feed models. Anomalo leverages machine learning to uncover a wide range of data failures with minimal human intervention. This contrasts with older approaches to data quality monitoring that require extensive work to write data validation rules or set limits and thresholds.
With today’s announcement, Databricks customers can now start monitoring data quality in their Databricks Lakehouse immediately in their Databricks workspace.
“Whether you use your Databricks Lakehouse for analytics or machine learning and AI, the quality of your results depends on the quality of the underlying data. We are therefore delighted to partner with Databricks to offer their customers a great tool to automatically detect and understand the root causes of data issues, thereby preventing these issues from leading to incorrect BI dashboards or learning models. faulty automatic,” said Elliot Shmukler. , co-founder and CEO of Anomalo.
Popular use cases
- Business Intelligence / Analytics: When users are tracking key business metrics, it is essential that they are able to understand why those metrics are changing and detect and resolve any data quality issues impacting the metrics. They need to understand if a movement in the metric was really caused by a change in customer behavior or happened because data was missing or stuck in a downstream pipeline. With Anomalo, users benefit from automatic monitoring of key metrics as well as checks on the underlying data in their Lakehouse, so issues that cause unexpected metric movements are quickly detected.
- Machine learning: Data quality issues can cause a machine learning model to see data that is very different from what it was trained on, resulting in erratic and suboptimal model behavior. To prevent this, Anomalo allows users to quickly and easily monitor data used as input to machine learning models to detect any unusual changes or data drift.
Anomalo helps companies build trust in the data they use to make decisions and create products. Businesses can simply connect Anomalo’s comprehensive data quality platform to their data warehouse and start monitoring their data in less than 5 minutes, all with minimal setup and without a single line of code. Then they can automatically detect and understand the root cause of data issues, before anyone else. Anomalo is backed by Norwest Venture Partners, Foundation Capital, Two Sigma Ventures, Village Global and First Round Capital. For more information, visit https://www.anomalo.com/ or follow @anomalo_hq.
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