Building a reliable data platform is a neverending task. Even if you have a process that works for you and your business there can be unexpected events that require a change in your platform architecture. In this episode the head of data for Mayvenn shares their experience migrating an existing set of streaming workflows onto the Ascend platform after their previous vendor was acquired and changed their offering. This is an interesting discussion about the ongoing maintenance and decision making required to keep your business data up to date and accurate.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Sheel Choksi and Sean Knapp about Mayvenn’s experience migrating their dataflows onto the Ascend platform
- How did you get involved in the area of data management?
- Can you start off by describing what Mayvenn is and give a sense of how you are using data?
- What are the sources of data that you are working with?
- What are the biggest challenges you are facing in collecting, processing, and analyzing your data?
- Before adopting Ascend, what did your overall platform for data management look like?
- What were the pain points that you were facing which led you to seek a new solution?
- What were the selection criteria that you set forth for addressing your needs at the time?
- What were the aspects of Ascend which were most appealing?
- What are some of the edge cases that you have dealt with in the Ascend platform?
- Now that you have been using Ascend for a while, what components of your previous architecture have you been able to retire?
- Can you talk through the migration process of incorporating Ascend into your platform and any validation that you used to ensure that your data operations remained accurate and consistent?
- How has the migration to Ascend impacted your overall capacity for processing data or integrating new sources into your analytics?
- What are your future plans for how to use data across your organization?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Google Sawzall
- Apache Kafka
- Amazon Redshift
- ELT == Extract, Load, Transform
- Amazon Data Pipeline
- Stitch Data