How Orchestration Impacts Data Platform Architecture
Data Engineering Podcast
00:00
Navigating Data Orchestration Challenges
This chapter delves into the inefficiencies of traditional orchestration systems like Airflow and the impact on data teams. It presents AI-driven solutions for streamlining data migrations and discusses the evolution of orchestration from centralized to decentralized models, alongside tool comparisons.
Transcript
Play full episode
Transcript
Episode notes
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
- It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity.
- As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
- 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
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
