Building a data platform is a complex journey that requires a significant amount of planning to do well. It requires knowledge of the available technologies, the requirements of the operating environment, and the expectations of the stakeholders. In this episode Tobias Macey, the host of the show, reflects on his plans for building a data platform and what he has learned from running the podcast that is influencing his choices.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- I’m your host, Tobias Macey, and today I’m sharing the approach that I’m taking while designing a data platform
- How did you get involved in the area of data management?
- What are the components that need to be considered when designing a solution?
- Data integration (extract and load)
- What are your data sources?
- Batch or streaming (acceptable latencies)
- Data storage (lake or warehouse)
- How is the data going to be used?
- What other tools/systems will need to integrate with it?
- The warehouse (Bigquery, Snowflake, Redshift) has become the focal point of the "modern data stack"
- Data orchestration
- Who will be managing the workflow logic?
- Metadata repository
- Types of metadata (catalog, lineage, access, queries, etc.)
- Semantic layer/reporting
- Data applications
- Data integration (extract and load)
- Implementation phases
- Build a single end-to-end workflow of a data application using a single category of data across sources
- Validate the ability for an analyst/data scientist to self-serve a notebook powered analysis
- Data modeling requirements
- Specific implementation details as integrations across components are built
- When to use a vendor and risk lock-in vs. spend engineering time
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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