Spark_Declarative_Pipelines_Devoxx_2026.pdf

Transcript

1. Apache Spark Declarative Pipelines (SPD) In Action Avionics Demo Frank Munz, Databricks Devoxx 2026
2. Frank Munz, Databricks
3. About me • I am Frank Munz • Principal @ Databricks: Data and AI • My past: large scale data & compute • Based in 🍻 ⛰ 🥨 󰎲 • Built up AWS Tech Evangelism in Central Europe • SW architect, data scientist, published author etc.
4. ©2022 Databricks Inc. — All rights reserved 4 Apache Spark ™
5. ©2022 Databricks Inc. — All rights reserved Spark SQL and Dataframe API # PYTHON: read multiple JSON files with multiple datasets per file cust = spark.read.json('/data/customers/') cust.filter(cust['car_make']== "Audi").show() cust.createOrReplaceTempView("customers") %sql select * from customers where car_make = "Audi" -- PYTHON: -- spark.sql('select * from customers where car_make ="Audi"').show()
6. ©2022 Databricks Inc. — All rights reserved Automatic Parallelization and Scaling
7. ©2022 Databricks Inc. — All rights reserved Reading from Files vs Reading from Streams # read file(s) cust = spark.read.format("json").path('/data/customers/') # read from messaging platform sales = spark.readStream.format("kafka|kinesis|socket").load()
8. ©2022 Databricks Inc. — All rights reserved Data Engineering -> Data Pipelines Data Intelligence Platform Orchestration Data Ingestion Data sources Cloud storage Message queues Databases Enterprise applications Serverless Compute | Unified Governance | Reliable Storage Use cases SQL analytics & BI AI/ML apps Real-time apps Data sharing Data warehousing Machine learning Real-time processing Data Transformation Data Intelligence Engine
9. ©2022 Databricks Inc. — All rights reserved 9 >10y of Simplifying Apache Spark™
10. ©2022 Databricks Inc. — All rights reserved 10 … why simplify?
11. ©2022 Databricks Inc. — All rights reserved 11 What is declarative?
12. Declarative 12 orders table Query Declare a plan Execute it
13. ©2022 Databricks Inc. — All rights reserved 13 What is … a data pipeline ?
14. data pipeline Something that produces and updates a set of datasets
15. Dataset Dataset Dataset Dataset Query Query Query Query Dataset Query Dataset Query
16. ©2022 Databricks Inc. — All rights reserved 16 Why make pipelines declarative?
17. Developing and operating data pipelines is hard
18. raw_orders Query fact_orders Query customers Query Surprisingly hard: multiple queries/tables
19. Sequential Execution could run at the same time
20. Multithreading
21. Things get very complicated quickly table4 Query table5 Query table3 Query table2 Query table1 Query
22. ©2022 Databricks Inc. — All rights reserved 22 What if there is an error?
23. Analysis Error: Catchable before execution ⚠
24. Add code for runtime retries
25. Manual Retry (command-line args)
26. Add an Orchestrator / Workflow System ? Code DAG object
27. What if we want to run streams continuously? raw_orders table Streaming Query fact_orders table Streaming Query customers table Batch Query Run continuously Run continuously Run every 15 minutes
29. Problem: first pipeline run • raw_orders streaming query starts • fact_orders streaming query starts • fact_orders streaming query tries to read from raw_orders table • raw_orders table doesn’t exist yet -> ⚠error⚠ raw_orders table Streaming Query fact_orders table Streaming Query
30. ©2022 Databricks Inc. — All rights reserved 30 What if we make the whole pipeline declarative? Build plan -> execute it
31. ©2022 Databricks Inc. — All rights reserved 31 Spark Declarative Pipelines (SDP)
32. Spark Declarative Pipelines Core Componentes Datasets: pipeline.yml for catalog, database, config, source paths
33. Streaming Table (ST) • Continuously updated table • Exactly once ingestion • Append only • Real-time, incremental data ingestion • Low latency streaming transformations What is it? use it for…
34. Materialized View (MV) • A precomputed, persistent table based on a query • Efficient aggregation on static or periodically updated data. • (possibly) incrementally computed What is it? use it for…
35. Gold Layer Silver Layer Bronze Layer customers_raw Streaming Table orders_raw Streaming Table customer_orders Materialized View Declarative Pipelines Declarative Pipeline with STs and MVs Putting things together customers_clean Streaming Table orders_clean Streaming Table
36. Python API raw_orders Query convention in Spark 4.1: import … as dp
37. raw_orders Query fact_orders Query customers Query No need for DAG obj
38. ⚙ Spark Declarative Pipelines • Transformations Python  or SQL files • Initialize via  spark‑pipelines init --name orders‑pl • Execute with  spark‑pipelines [dry-]run  • SDP automatically handles pre-validation, dependencies, retries & parallelization. • Triggered/batch and  continuous/streaming mode
39. ©2022 Databricks Inc. — All rights reserved 39 It's demo time!
40. ©2022 Databricks Inc. — All rights reserved 40 Getting Started with Spark Declarative Pipelines: OSS + Lakeflow Brand new: OSS and Lakeflow Tutorials (realtime flight data) Create a Databricks forever Free Edition account, and add the avionics SDP demo to your portfolio on LinkedIn
41. ©2022 Databricks Inc. — All rights reserved Build this SDP in your next coffee break
42. ©2022 Databricks Inc. — All rights reserved 42 Custom Python Datasource for OpenSky API
43. 🚀 Python Data Source API spark.readstream.format("opensky") • Use familiar spark.read.format() and write.format()  • OSS • simple pip install • Supports batch + streaming
44. ©2022 Databricks Inc. — All rights reserved 44 Lakeflow
45. ©2025 Databricks Inc. — All rights reserved Lakeflow Spark Declarative Pipelines Efficiently clean, transform and join data Build batch and streaming pipelines with a declarative approach Automated pipeline configuration with reduces maintenance burden Simplified pipeline development Reliable production infrastructure Fully compatible with the open source Spark Declarative Pipelines Built on an open standard
46. Genie Code • Natural language prompt to create, debug, explain and document • Creates, manages and debugs data pipelines • Genie Code Agent skills for Claude Code • Can be extended with Skills.md and MCP servers An autonomous AI agent for Data Science, Engineering and Analytics
47. ©2022 Databricks Inc. — All rights reserved 47 Spark Real-time Mode
48. Now you can get O(100ms) in Spark Structured Streaming New: Real-Time Mode in Apache Spark!
49. Updated architecture, same interface • We extended the microbatch architecture by adding concurrent stages, streaming shuffle, and continuous data flow. • Same interface. Only a single line of change!
50. Air Traffic Control System Spark Streaming, Rea-time mode and Databrick Apps (REC)
51. ©2022 Databricks Inc. — All rights reserved 51 Acknowledgement Michael Armbrust & Sandy Ryza for some of the intro slides
52. 52 Convert Messy Sales Data to AI insights databricks.com/demos Use Lakeflow Connect, Jobs and SDP to create a marketing solution with AI for a global food corporation. Video Walkthrough with GitHub Repo + DABs
53. 53 Get to know Genie Code Genie Code data engineering video demo: Use Genie Code to create a complete SDP pipeline from a prompt. With Auto Loader, JSON ingestion, and medallion architecture. Genie Code Step by Step Guide
54. Zerobus Ingest Demo Zerobus Ingest demo: ingest streaming IoT events without Kafka from all over the world