Open source data persistence: Creating order from chaos

Megan Kostick, Michael Brewer, and Manuel Silveyra explain how they tackle the issue of working across large distributed teams, share solutions to data persistence challenges, and offer an overview of their automated data model for bringing data from multiple teams into a single place in a consistent manner.

Bringing data together all in one place in a consistent manner can be challenging. One team uses SQL, and another uses NoSQL, and yet another just keeps everything stored in a manual spreadsheet. Being able to automate and analyze the data while maintaining data persistence is key. Megan Kostick, Michael Brewer, and Manuel Silveyra explain how they tackle this issue and share solutions to data persistence challenges. They offer an overview of their automated data model for bringing data from multiple teams into a single place in a consistent manner, covering the multiple layers needed to ensure data persistence while cutting down on time-consuming manual data consolidation. Regardless of whether data comes from PostgreSQL, DB2, or MongoDB, users are able to create a unified and automated persistence layer through Elasticsearch that enables faster processing and read times. Join in to learn how to get started streamlining your data from multiple sources.