How Orchestration Impacts Data Platform Architecture
Data Engineering Podcast
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Navigating the Divide in Data Orchestration: Analytical vs. Product Use Cases
This chapter examines the shifting dynamics of data orchestration, focusing on the contrast between analytical and product-oriented applications. It highlights the distinct demands of business intelligence and machine learning systems, showcasing the need for effective orchestration strategies to tackle modern data complexities.
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Summary
The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.
Announcements
Parting Question
The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.
Announcements
- 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 Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available options
- Introduction
- How did you get involved in building data platforms?
- Can you describe what an orchestrator is in the context of data platforms?
- There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?
- What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?
- Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?
- There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?
- How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?
- What about the reverse?
- How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?
- What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?
- When is an orchestrator the wrong choice?
- What are your predictions and/or hopes for the future of data orchestration?
Parting Question
- From your perspective, what is the biggest thing data teams are missing in the technology today?
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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- Orchestra
- Previous Episode: Overview Of The State Of Data Orchestration
- Cron
- ArgoCD
- DAG
- Kubernetes
- Data Mesh
- Airflow
- SSIS == SQL Server Integration Services
- Pentaho
- Kettle
- DataVolo
- NiFi
- Dagster
- gRPC
- Coalesce
- dbt
- DataHub
- Palantir
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