There was a post on the data engineering subreddit recently that discussed how difficult it is to keep up with the data engineering world. Did you learn Hadoop, great we are on Snowflake, BigQuery and Databricks now. Just learned Airflow, well now we have Airflow 3.0. And the list goes on. But what doesn’t change, and what have been the lessons over the past decade. That’s what I’ll be covering in this talk. Real lessons and realities that come up time and time again whether you’re working for a start-up or a large enterprise.

Airflow has been used by many companies as a core part of their internal data platform. Would you be interested in finding out how Airflow could play a pivotal role in achieving data engineering excellence and efficiency using modern data architecture. The best practices, tools and setup to achieve a stable but yet cost effective way of running small or big workloads, let’s find out! In this workshop we will review how an organisation can setup data platform architecture around Airflow and necessary requirements. Airflow and it’s role in Data Platform Different ways to organise airflow environment enabling scalability and stability Useful open source libraries and custom plugins allowing efficiency How to manage multi-tenancy, cost savings Challenges and factors to keep in mind using Success Matrix! This workshop should be suitable for any Architect, Data Engineer or Devops aiming to build/enhance their internal Data Platform. At the end of this workshop you would have solid understanding of initial setup and ways to optimise further getting most out of the tool for your own organisation.

How a Complete Beginner in Data Engineering / Junior Computer Science Student Became an Apache Airflow Committer in Just 5 Months—With 70+ PRs and 300 Hours of Contributions This talk is aimed at those who are still hesitant about contributing to Apache Airflow. I hope to inspire and encourage anyone to take the first step and start their journey in open-source—let’s build together!

Maintaining consistency, code quality, and best practices for writing Airflow DAGs between teams and individual developers can be a significant challenge. Trying to achieve it using manual code reviews is both time-consuming and error-prone. To solve this at Next, we decided to build a custom, internally developed linting tool for Airflow DAGs, to help us evaluate their quality and uniformity - we call it - DAGLint. In this talk I am going to share why we chose to implement it, how we built it, and how we use it to elevate our code quality and standards throughout the entire Data engineering group. This tool supports our day-to-day development process, provides us with a visual analysis of the state of our entire code base, and allows our code reviews to focus on other code quality aspects. We can now easily identify deviations from our defined standards, promote consistency throughout our DAGs repository, and extend the tool with additional new standards introduced to our group. The talk will cover how you can implement similar solution in your own organization, we also published a blog post on it https://medium.com/apache-airflow/mastering-airflow-dag-standardization-with-pythons-ast-a-deep-dive-into-linting-at-scale-1396771a9b90

At the enterprise level, managing Airflow deployments across multiple teams can become complex, leading to bottlenecks and slowed development cycles. We will share our journey of decentralizing Airflow repositories to empower data engineering teams with multi-tenancy, clean folder structures, and streamlined DevOps processes. We dive into how restructuring our Airflow architecture and utilizing repository templates allowed teams to generate new data pipelines effortlessly. This approach enables engineers to focus on business logic without worrying about underlying Airflow configurations. By automating deployments and reducing manual errors through CI/CD pipelines, we minimized operational overhead. However, this transformation wasn’t without challenges. We’ll discuss obstacles we faced, such as maintaining code consistency, variables, and utility functions across decentralized repositories; ensuring compliance in a multi-tenant environment; and managing the learning curve associated with new workflows. Join us to discover practical insights on how decentralizing Airflow repositories can boost team productivity and adapt to evolving business needs with minimal effort.

We will explore how Apache Airflow 3 unlocks new possibilities for smarter, more flexible DAG design. We’ll start by breaking down common anti-patterns in early DAG implementations, such as hardcoded operators, duplicated task logic, and rigid sequencing, that lead to brittle, unscalable workflows. From there, we’ll show how refactoring with the D.R.Y. (Don’t Repeat Yourself) principle, using techniques like task factories, parameterization, dynamic task mapping, and modular DAG construction, transforms these workflows into clean, reusable patterns. With Airflow 3, these strategies go further: enabling DAGs that are reusable across both batch pipelines and streaming/event-driven workloads, while also supporting ad-hoc runs for testing, one-off jobs, or backfills. The result is not just more concise code, but workflows that can flexibly serve different data processing modes without duplication. Attendees will leave with concrete patterns and best practices for building maintainable, production-grade DAGs that are scalable, observable, and aligned with modern data engineering standards.

This session explores how GitHub uses Apache Airflow for efficient data engineering. We will share nearly 9 years of experiences, including lessons learnt, mistakes made, and the ways we reduced our on-call and engineering burden. We’ll demonstrate how we keep data flowing smoothly while continuously evolving Airflow and other components of our data platform, ensuring safety and reliability. The session will touch on how we migrate Airflow between cloud without user impact. We’ll also cover how we cut down the time from idea to running a DAG in production, despite our Airflow repo being among the top 15 by number of PRs within GitHub. We’ll dive into specific techniques such as testing connections and operators, relying on dag-sync, providing short-lived development environments to let developers test their DAG runs, and creating reusable patterns for DAGs. By the end of this session, you will gain practical insights and actionable strategies to improve your own data engineering processes.

Metadata management is a cornerstone of effective data governance, yet it presents unique challenges distinct from traditional data engineering. At scale, efficiently extracting metadata from relational and NoSQL databases demands specialized solutions. To address this, our team has developed custom Airflow operators that scan and extract metadata across various database technologies, orchestrating 100+ production jobs to ensure continuous and reliable metadata collection. Now, we’re expanding beyond databases to tackle non-traditional data sources such as file repositories and message queues. This shift introduces new complexities, including processing structured and unstructured files, managing schema evolution in streaming data, and maintaining metadata consistency across heterogeneous sources. In this session, we’ll share our approach to building scalable metadata scanners, optimizing performance, and ensuring adaptability across diverse data environments. Attendees will gain insights into designing efficient metadata pipelines, overcoming common pitfalls, and leveraging Airflow to drive metadata governance at scale.

A real-world journey of how my small team at Xena Intelligence built robust data pipelines for our enterprise customers using Airflow. If you’re a data engineer, or part of a small team, this talk is for you. Learn how we orchestrated a complex workflow to process millions of public reviews. What You’ll Learn: Cost-Efficient DAG Designing: Decomposing complex processes into atomic tasks using the TaskFlow, XComs, Mapped tasks, and Task groups. Diving into one of our DAGs as a concrete example of how our approach optimizes parallelism, error handling, delivery speed, and reliability. Integrating LLM Analysis: Explore how we integrated LLM-based analysis into our pipeline. Learn how we designed the database, queries, and ingestion to Postgres. Extending Airflow UI: We developed a custom Airflow UI plugin that filters and visualizes DAG runs by customer, product, and marketplace, delivering clear insights for faster troubleshooting. Leveraging Airflow REST API: Discover how we leveraged the API to trigger DAGs on demand, elevating the UX by tracking mapped DAG progress and computing ETAs. CI/CD and Cost Management: Get practical tips for deploying DAGs with CI/CD.

MWAA is an AWS-managed service that simplifies the deployment and maintenance of the open-source Apache Airflow data orchestration platform. MWAA has recently introduced several new features to enhance the experience for data engineering teams. Features such as Graceful Worker Replacement Strategy that enable seamless MWAA environment updates with zero downtime, IPv6 support, and in place minor Airflow Version Downgrade are some of the many new improvements MWAA has brought to their users in 2025. Last, but not the least, the release of Airflow 3.0 support brings the latest open-source features introducing a new web-server UI, better isolation and security for environments. These enhancements demonstrate Amazon’s continued investment in making Airflow more accessible and scalable for enterprises through the MWAA service.

MWAA is an AWS-managed service that simplifies the deployment and maintenance of the open-source Apache Airflow data orchestration platform. MWAA has recently introduced several new features to enhance the experience for data engineering teams. Features such as Graceful Worker Replacement Strategy that enable seamless MWAA environment updates with zero downtime, IPv6 support, and in place minor Airflow Version Downgrade are some of the many new improvements MWAA has brought to their users in 2025. Last, but not the least, the release of Airflow 3.0 support brings the latest open-source features introducing a new web-server UI, better isolation and security for environments. These enhancements demonstrate Amazon’s continued investment in making Airflow more accessible and scalable for enterprises through the MWAA service.

How a Complete Beginner in Data Engineering / Junior Computer Science Student Became an Apache Airflow Committer in Just 5 Months—With 70+ PRs and 300 Hours of Contributions This talk is aimed at those who are still hesitant about contributing to Apache Airflow. I hope to inspire and encourage anyone to take the first step and start their journey in open-source—let’s build together!

As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

In the rapidly evolving field of data engineering and data science, efficiency and ease of use are crucial. Our innovative solution offers a user-friendly interface to manage and schedule custom PySpark, PySQL, Python, and SQL code, streamlining the process from development to production. Using Airflow at the backend, this tool eliminates the complexities of infrastructure management, version control, CI/CD processes, and workflow orchestration.The intuitive UI allows users to upload code, configure job parameters, and set schedules effortlessly, without the need for additional scripting or coding. Additionally, users have the flexibility to bring their own custom artifactory solution and run their code. In summary, our solution significantly enhances the orchestration and scheduling of custom code, breaking down traditional barriers and empowering organizations to maximize their data’s potential and drive innovation efficiently. Whether you are an individual data scientist or part of a large data engineering team, this tool provides the resources needed to streamline your workflow and achieve your goals faster than ever before.

Summary In this episode of the Data Engineering Podcast Arun Joseph talks about developing and implementing agent platforms to empower businesses with agentic capabilities. From leading AI engineering at Deutsche Telekom to his current entrepreneurial venture focused on multi-agent systems, Arun shares insights on building agentic systems at an organizational scale, highlighting the importance of robust models, data connectivity, and orchestration loops. Listen in as he discusses the challenges of managing data context and cost in large-scale agent systems, the need for a unified context management platform to prevent data silos, and the potential for open-source projects like LMOS to provide a foundational substrate for agentic use cases that can transform enterprise architectures by enabling more efficient data management and decision-making processes.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. 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 for the details. This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Your host is Tobias Macey and today I'm interviewing Arun Joseph about building an agent platform to empower the business to adopt agentic capabilitiesInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of how Deutsche Telekom has been approaching applications of generative AI?What are the key challenges that have slowed adoption/implementation?Enabling non-engineering teams to define and manage AI agents in production is a challenging goal. From a data engineering perspective, what does the abstraction layer for these teams look like? How do you manage the underlying data pipelines, versioning of agents, and monitoring of these user-defined agents?What was your process for developing the architecture and interfaces for what ultimately became the LMOS?How do the principles of operatings systems help with managing the abstractions and composability of the framework?Can you describe the overall architecture of the LMOS?What does a typical workflow look like for someone who wants to build a new agent use case?How do you handle data discovery and embedding generation to avoid unnecessary duplication of processing?With your focus on openness and local control, how do you see your work complementing projects like OumiWhat are the most interesting, innovative, or unexpected ways that you have seen LMOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LMOS?When is LMOS the wrong choice?What do you have planned for the future of LMOS and MASAIC?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements 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 [email protected] with your story.Links LMOSDeutsche TelekomMASAICOpenAI Agents SDKRAG == Retrieval Augmented GenerationLangChainMarvin MinskyVector DatabaseMCP == Model Context ProtocolA2A (Agent to Agent) ProtocolQdrantLlamaIndexDVC == Data Version ControlKubernetesKotlinIstioXerox PARC)OODA (Observe, Orient, Decide, Act) LoopThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

There have been lots of social media posts declaring things to be dead - SQL, R, data engineering, BI, etc.

I give my thoughts on these proclamations, why it's a wrong way to think about our space, and more.

Ed Digby will share lessons from building a data platform that processes 17 billion rows of transaction data across 12,000 UK convenience stores, focusing on delivering fast, simple, actionable insights while managing startup constraints and costs. The talk covers cutting through complexity, focusing on what matters, and building data systems that deliver real value.

Data and analytics leaders and their data engineering teams are tasked with evaluating and selecting data integration tools. However, there are many options, which can be confusing. This session will explain the various types of data integration tools and technologies available in the market, and help you select the right data integration tool for your needs.

Summary In this episode of the Data Engineering Podcast we welcome back Nick Schrock, CTO and founder of Dagster Labs, to discuss the evolving landscape of data engineering in the age of AI. As AI begins to impact data platforms and the role of data engineers, Nick shares his insights on how it will ultimately enhance productivity and expand software engineering's scope. He delves into the current state of AI adoption, the importance of maintaining core data engineering principles, and the need for human oversight when leveraging AI tools effectively. Nick also introduces Dagster's new components feature, designed to modularize and standardize data transformation processes, making it easier for teams to collaborate and integrate AI into their workflows. Join in to explore the future of data engineering, the potential for AI to abstract away complexity, and the importance of open standards in preventing walled gardens in the tech industry.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementThis episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. 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 for the details. This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Nick Schrock about lowering the barrier to entry for data platform consumersInterview IntroductionHow did you get involved in the area of data management?Can you start by giving your summary of the impact that the tidal wave of AI has had on data platforms and data teams?For anyone who hasn't heard of Dagster, can you give a quick summary of the project?What are the notable changes in the Dagster project in the past year?What are the ecosystem pressures that have shaped the ways that you think about the features and trajectory of Dagster as a project/product/community?In your recent release you introduced "components", which is a substantial change in how you enable teams to collaborate on data problems. What was the motivating factor in that work and how does it change the ways that organizations engage with their data?tension between being flexible and extensible vs. opinionated and constrainedincreased dependency on orchestration with LLM use casesreducing the barrier to contribution for data platform/pipelinesbringing application engineers into the mixchallenges of meeting users/teams where they are (languages, platform investments, etc.)What are the most interesting, innovative, or unexpected ways that you have seen teams applying the Components pattern?What are the most interesting, unexpected, or challenging lessons that you have learned while working on the latest iterations of Dagster?When is Dagster the wrong choice?What do you have planned for the future of Dagster?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links Dagster+ EpisodeDagster Components Slide DeckThe Rise Of Medium CodeLakehouse ArchitectureIcebergDagster ComponentsPydantic ModelsKubernetesDagster PipesRuby on RailsdbtSlingFivetranTemporalMCP == Model Context ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA