Zuar Runner
Utilizing the data that's spread across your organization shouldn't be so difficult! With Zuar Runner you can automate the flow of data from hundreds of potential sources into a single destination. Collect, transform, model, warehouse, report, monitor and distribute: it's all managed by Zuar Runner.
Pull data from Amazon/AWS products, Google products, Microsoft products, Avionte, Backblaze, BioTrackTHC, Box, Centro, Citrix, Coupa, DigitalOcean, Dropbox, CSV, Eventbrite, Facebook Ads, FTP, Firebase, Fullstory, GitHub, Hadoop, Hubic, Hubspot, IMAP, Jenzabar, Jira, JSON, Koofr, LeafLogix, Mailchimp, MariaDB, Marketo, MEGA, Metrc, OneDrive, MongoDB, MySQL, Netsuite, OpenDrive, Oracle, Paycom, pCloud, Pipedrive, PostgreSQL, put.io, Quickbooks, RingCentral, Salesforce, Seafile, Shopify, Skybox, Snowflake, Sugar CRM, SugarSync, Tableau, Tamarac, Tardigrade, Treez, Wurk, XML Tables, Yandex Disk, Zendesk, Zoho, and more!
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dbForge Schema Compare for MySQL
dbForge Schema Compare for MySQL is a fast, easy-to-use tool to compare and synchronize structures of MySQL, MariaDB and Percona databases. The tool provides a comprehensive view of all differences between MySQL, MariaDB and Percona database schemas, generates clear and accurate SQL synchronization script that can be used to update database schema.
Key features:
AI Assistant
Fast comparison of any databases, including extra-large ones
Clear display of comparison results
Capability to save and load comparison settings
Filtering, sorting, grouping for efficient management of compared objects
Text comparison feature shows DDL differences of compared objects
Synchronization script preview for any compared object
Schema synchronization wizard allows you to generate a standard-driven synchronization script with additional options
Integrated SQL editor for advanced work with SQL scripts and query files
Well-tested functionality, reliable and safe for your database
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Data Quality Validator (DQV)
DQV is Kumaran Systems' data quality and testing platform for teams who move, mask, or validate large volumes of data. It compares source and target datasets field by field, flags drift, and generates a mismatch report instead of manual spreadsheet checks.
It covers five areas: field-level comparison with drift detection, migration mapping between schemas, deterministic PII masking, record- and table-level validation with on-the-fly correction, and synthetic data generation for teams without production data to test against.
It connects to SQL Server, Oracle, MySQL, PostgreSQL, AWS, Azure, GCP, flat files, JSON, XML, and REST APIs, and plugs into Informatica, Databricks, and CI/CD pipelines, or runs standalone as a library or CLI tool.
In production, DQV has validated 26.6 million bank records in under 22 minutes. A free trial is available, alongside individual, enterprise, and on-premises licensing.
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