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.
Megan Kostick is a software developer for the IBM Code developer advocacy team, where she focuses on cross-team metrics and big data analytics. Her previous roles include work in virtualization software products, IBM cloud solutions leveraging the OpenStack, Cloud Foundry, Apple Swift, and Docker open source projects, and IBM’s Linux Technology Center. Megan is also the organizer of the Seattle Swift meetup.
Michael Brewer is a software engineer at IBM, where he works on the IBM Code developer advocacy initiative, focusing on projects related to DevOps architecture and big data analytics. Michael has worked with open source communities such as OpenStack, Swift Language, Docker, and more.
Manuel Silveyra is a senior cloud solutions architect at IBM, where he focuses on OpenStack, Docker, Cloud Foundry, and the Swift programming language. Previously, Manuel was a lead architect at the Linux Integration Center at IBM. He holds a BS in electrical engineering and an MS in computer engineering from the University of Texas at El Paso (go Miners!).
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