Modernize your analytics platform by integrating cloud-based Data Reconciliation, Data Quality and Data Observability tool to automatically detects 100% of all data quality issues with minimal human intervention using Al/ML
Connects to many sources and target systems and automates data reconciliation.
Run SQLs on regular basis in your analytics platform, detects anomalies & create alerts.
Automates ETL testing when you change code or copy data between systems.
Enhances Data Quality and Builds trust in your data.
Automates Schema Compare, Prepare Deployment Script & Integrate With Source Control.
Create ER Diagram and Perform Schema Compare to Create Deployment Script.
Centralize Data Masking rules & row access policy and access provisioning.
Automates Schema Compare & Synch database with your Version Control tools.
After every data load into your analytics platform, even though ETL runs successfully, how can we be sure data in analytics platform reconciles with source system? 4DAlert’s AI/ML based API solution reconciles data between analytics platform and source systems and certifies the accuracy of the data. This helps to detect and alert before users complain about the data.
4DAlert’s data observability module identifies and helps you troubleshoot and work to avert data-related issues across all data platforms within your analytics landscape. The solution leverages machine learning to generate adaptive rules and criteria, helps identify the root causes of the issues and fix systemic problems.
4DAlert’s data observability module identifies and helps you troubleshoot and work to avert data-related issues across all data platforms within your analytics landscape. The solution leverages machine learning to generate adaptive rules and criteria, helps identify the root causes of the issues and fix systemic problems.
In built AI/ML module leverages pre-built rule catalog to check data quality issues such as blank vs non-blank, unique vs non-unique, outlier detection, enumeration check, range check, address check, duplicate checks etc and calculate overall data quality index for the enterprise.
Start building trust in your data. Be the first to know when data breaks before your users tell.
The automated CI/CD, schema comparison and change deployment feature maintains consistent schemas across environments. It automatically compares and synchronizes database schemas, minimizing deployment errors and speeding up change rollouts.
Data from multiple sources often mismatches between source and target. 4DAlert connects diverse data sources, using its AI/ML engine to automatically reconcile data and alert stakeholders via email, texts, and Slack.
When connecting to source systems isn't possible due to restrictions or rigidity, 4DAlert’s AI engine uses historical trends to detect data anomalies and reconciliation issues in new data.
Organizations struggle with syncing data across multiple systems. 4DAlert’s flexible architecture connects various systems and compares key data points to maintain consistency.
This step involves retrieving the object comparison list, which helps in identifying schema differences between versions, aiding in the deployment process.
In this step, the changes made to the Data Definition Language (DDL) are pushed to source control, ensuring that all modifications are properly tracked and versioned.
Execute Data Definition Language (DDL) to Apply Schema Changes
The final step involves executing the generated DDL to apply the necessary schema changes, completing the automated pipeline process.
Stay in control of your data. Get notified on data issues before users use bad data to take wrong decisions.
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