Using MySQL for Distributed Database Architectures

Do you have data governance on your mind? Do you envision an environment where people are held formally accountable for the data they define, produce and use? Does metadata play a big role in that governed environment? Of course, it does. To manage any “thing” requires that you have quality information about that thing. Join Bob Seiner, of KIK Consulting and TDAN.com, with Gal Alon, Senior Director of Business Development for an industry leading metadata management automation software provider Octopai, as they discuss the who’s, what’s, why’s and how’s of automating and leveraging your metadata environment to successfully govern your organization’s data. Bob and Gal will spend the hour chatting about the role metadata management plays in data governance as well as discuss specific use cases to improve probability of Data Governance success.

In today’s corporate finance departments, the pressure to do more with less is greater than ever. The most successful finance teams partner with other lines of business to drive growth through data-driven insights, using advanced analytics and predictive models. But translating insights into real-time actions requires real-time analytics that can’t be achieved with traditional business intelligence solutions and dashboards. To unleash the full power of your finance data, you need a new approach.

cnvrg.io is used across industries to help enterprises accelerate AI from research to production by simplifying DevOps and dependencies involved in building production-ready ML. Whether you’ve just begun the free trial, you’re a new user, or you’re interested in looking deeper into cnvrg.io features, we invite you to join us in a live workshop on how to get started using cnvrg.io.

Data pipelines facilitate information flows and data exchange for a growing number of operational scenarios, including data extraction, transformation, and loading ETL into data warehouses and data marts, data migrations, production of BI reports, and application interoperability. When data engineers develop data pipelines, they may devise a collection of tests to guide the development process, but ongoing tests are not often put in place once those pipelines are put into production.