Automated Data Quality Management in Databricks

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150+ out-of-box and customizable DQ checks across all dimensions, advanced data profiling, AI/Ml driven anomaly detection.
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Monitoring and alerting of data content, pipelines, and utilization metrics across your data landscape.
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150+ out-of-box and customizable DQ checks across all dimensions, advanced data profiling, AI/Ml driven anomaly detection.

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150+ out-of-box and customizable DQ checks across all dimensions, advanced data profiling, AI/Ml driven anomaly detection.
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Monitoring and alerting of data content, pipelines, and utilization metrics across your data landscape.
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Effective Unstructured Data Quality Management with the DQLabs Platform

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Automated
Data Quality Management in Databricks

Overview

Databricks is a cloud-based data platform that integrates data engineering, machine learning, and analytics into a unified workspace. Built on Apache Spark, it enables organizations to efficiently process and analyze large datasets. The platform supports a variety of data formats and provides tools for real-time data processing, making it suitable for diverse applications, including data preparation and machine learning model development. Databricks’ architecture promotes collaboration among data scientists, engineers, and analysts, streamlining workflows and enhancing productivity. Its unique Lakehouse model combines the best features of data lakes and warehouses, addressing the complexities of modern data management.

DQLabs allows organizations to easily discover and profile data assets within their Databricks environment. Users can search for specific data (e.g., email information) across all organizational assets, including those stored in S3 and managed through Glue Data Catalog or Tableau. The platform supports out-of-the-box capabilities for data profiling, enabling users to assess data quality across different stages of processing within Databricks, without extensive coding. DQLabs can utilize Databricks’ compute power by running Spark SQL queries or leveraging the existing Spark instance, allowing for processing without moving data out of Databricks. DQLabs allows users to select specific attributes of interest, focusing on business-critical data rather than analyzing the entire dataset. This targeted approach helps avoid overwhelming the system with unnecessary information. This integration also enables out-of-the-box data observability for Databricks to reduce data downtime. Just connect and monitor data across your modern data lakehouse for data quality issues and remediation in minutes.

Data Quality and Observability for Databricks

The out-of-the-box data quality scoring feature allows users to quickly assess the quality of their data using predefined scoring metrics. The system automatically profiles datasets and assigns scores based on various quality dimensions, such as completeness, accuracy, consistency, validity, timeliness and uniqueness.

DQLabs provides continuous monitoring for data in Databricks, allowing organizations to detect issues in real time. Whether it’s missing values, duplicates, inconsistencies, or schema changes, DQLabs automatically identifies problems and alerts teams before they impact downstream analytics or business decisions.

DQLabs provides users with the ability to define and customize data quality rules to suit their specific business requirements. This flexibility allows organizations to set parameters for various data quality dimensions tailored to their unique data needs. With no-code option, even business users can easily create these data quality rules without having to rely on technical personnel.

DQLabs ensures that data used in Databricks for machine learning and advanced analytics is of high quality. By automating data quality checks and detecting issues early, organizations can trust the data fed into their models, leading to more accurate predictions, insights, and business outcomes. Data quality is often the primary barrier preventing organizations from embracing advanced data use cases. With DQLab’s platform, Databricks users can enhance data trust and confidence, driving more impactful decision-making and innovation in their AI, ML and analytics use cases.

DQLabs integrates seamlessly with Databricks to automate the profiling and cataloging of data assets, reducing manual effort and enhancing data discovery. This integration automates data cataloging through smart data sensing and metadata creation. It detects and extracts metadata from structured sources, identifying key properties like partition keys and data types. This automation eliminates manual entry, reduces errors, and speeds up data onboarding, ensuring that metadata remains accurate and up-to-date for effective data management.

DQLabs for Databricks includes customizable dashboards and reporting features that allow users to create tailored views of their data quality metrics. These dashboards can be configured to display relevant information for different stakeholders, whether it’s a high-level overview for executives or detailed metrics for data engineers.

Maintaining data quality over time is a continuous challenge, especially as data evolves. DQLabs addresses this with its anomaly detection and drift analysis features, which enable organizations to monitor data in real-time, automatically detecting anomalies and data drift. For instance, DQLabs can identify when the distribution of data values in a dataset changes unexpectedly, prompting a review to ensure the data remains accurate and reliable.

Seamlessly integrate with your
Modern Data Stack

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Automated
Data Quality Management in Databricks

Overview

Data Quality and Observability for Databricks

Leverage Out-of-the-Box Data Quality Scoring

Real-Time Monitoring of Data Pipelines

Create Customizable Data Quality Rules

Empower AI, ML, and Analytics Initiatives

Automate Data Profiling and Cataloging

Design Custom Dashboards and Reporting Views

Detect Anomalies and Analyze Data Drift

Seamlessly integrate with your
Modern Data Stack

Getting started with DQLabs is fast and seamless!

Company

subscribe to newsletter

Automated Data Quality Management in Databricks

Overview

Data Quality and Observability for Databricks

Leverage Out-of-the-Box Data Quality Scoring

Leverage Out-of-the-Box Data Quality Scoring

Real-Time Monitoring of Data Pipelines

Real-Time Monitoring of Data Pipelines

Create Customizable Data Quality Rules

Create Customizable Data Quality Rules

Empower AI, ML, and Analytics Initiatives

Empower AI, ML, and Analytics Initiatives

Automate Data Profiling and Cataloging

Automate Data Profiling and Cataloging

Design Custom Dashboards and Reporting Views

Design Custom Dashboards and Reporting Views

Detect Anomalies and Analyze Data Drift

Detect Anomalies and Analyze Data Drift

Seamlessly integrate with your Modern Data Stack

Getting started with DQLabs is fast and seamless!

Company

subscribe to newsletter

Automated
Data Quality Management in Databricks

Seamlessly integrate with your
Modern Data Stack