Building a data warehouse is about far more than simply storing data.
As enterprise data volumes continue to grow and business scenarios become increasingly complex, traditional big data architectures are facing mounting challenges around latency, performance, resource utilization, and operational costs. At the same time, the entire data lifecycle—from business requirements and data synchronization to data cleansing, processing, production, analytics, and operations—still involves a wide range of complex engineering challenges.
How can open source technologies help enterprises build a modern data warehouse that is easy to use, highly reliable, and built to scale?
This September, Apache DolphinScheduler Meetup is back!
This edition of the community meetup features Zan Liu, Data Analytics Engineer and Senior Big Data Architect, who will share “Building an Enterprise-Grade Data Warehouse with Apache DolphinScheduler.” Drawing on real-world engineering experience, Zan will break down how to use Apache DolphinScheduler as the core foundation for end-to-end workflow orchestration and production operations, combined with a distributed, high-performance OLAP data warehouse, to build a modern enterprise data warehouse.
If you are working on data warehousing, data development, workflow orchestration, OLAP, data operations, or enterprise data platforms, this is a session worth saving the date for.
One Session, the Full Story of Building an Enterprise Data Warehouse
Traditional big data solutions often face a number of practical challenges when deployed in enterprise environments.
Offline batch processing typically comes with minute-level latency, making it increasingly difficult to meet the real-time requirements of modern business scenarios. Tightly coupled compute and storage also make it difficult for enterprises to scale resources independently based on actual needs. Adding more storage or compute capacity separately can even result in lower utilization of the other resource.
At the same time, the complexity of the modern big data ecosystem means more components to manage, higher learning costs, and stricter requirements for version compatibility. For enterprise data teams, connecting these technologies into a stable and efficient data platform is often more important than simply choosing any individual component.
Even when an enterprise has accumulated massive volumes of business data, there is still a long list of challenges between a business requirement being raised and a data task running reliably in production. These include data synchronization, data cleansing, workflow orchestration, application integration, and end-to-end operations.
So, what does it really take to build a modern enterprise data warehouse?
That is the question this session will explore.
Based on hands-on engineering experience, Zan Liu will walk through the complete implementation of an enterprise-grade modern data warehouse, with Apache DolphinScheduler serving as the core foundation for end-to-end workflow orchestration and production operations, combined with a distributed, high-performance OLAP data warehouse.
Starting with business requirements and continuing through data synchronization, data production, data applications, task scheduling, and operations, the session will show how open source technologies can be brought together across the entire data lifecycle to create a complete data production loop.
Even more importantly, the entire solution is built on an open source ecosystem. Beyond the architecture itself, the session will focus on the practical challenges that engineers actually encounter when putting such a solution into production.
By bringing workflow orchestration, data processing, and data analytics together, the goal is to lower the barriers to data development and operations, improve the reliability and scalability of enterprise data platforms, and explore a more flexible approach to enterprise data operations.
🎤 Meet the Speaker
Zan Liu，Data Analytics Engineer | Senior Big Data Architect
With extensive experience in data warehousing and big data, Zan specializes in BI development and low-code Agent development with Dify. He has extensive hands-on experience in enterprise data platform development and data application implementation.
📌 Session Topic
“Building an Enterprise-Grade Data Warehouse with Apache DolphinScheduler”
In this session, you’ll learn:
- What challenges do traditional big data architectures face in real-world enterprise deployments?
- How can Apache DolphinScheduler be used to build an end-to-end workflow orchestration system?
- How can a distributed, high-performance OLAP data warehouse be combined with workflow orchestration to build a modern enterprise data warehouse?
- How can the entire process—from business requirements to data production and data applications—be connected end to end?
- How can data synchronization, cleansing, scheduling, and operations come together to form a unified data production workflow?
- How can enterprises build a data platform that is easy to use, highly reliable, and scalable with an open source ecosystem?
From architecture design to real-world implementation, this time we’re going beyond “which technologies should you choose?” and focusing on “how do you actually make them work in production?”
🗓️ Livestream Details
Date & Time: September 15, 2026, at 8:00 PM(China Standard Time, UTC+8)
Save Your Spot: https://meeting.tencent.com/dm/A0ISviQSgbjM
If you’re building an enterprise data warehouse or looking for a more efficient and reliable workflow solution for your data platform, join us for the livestream and see how a modern enterprise data warehouse can be built step by step with an open source technology stack.
🎁 Exclusive Giveaways During the Livestream
Along with the technical insights, this edition of Meetup also comes with exclusive Apache DolphinScheduler swag.
Join the livestream not only for practical insights and real-world experience, but also for a chance to take home a DolphinScheduler keychain!
September 15 at 8:00 PM. See you in the livestream!
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