- 502 Views
- 5 replies
- 16 kudos
From 40 Minutes to 8 minutes: Why We Dropped MERGE in Our SAP BW to Databricks Gold Layer
Infra keeps getting faster, but the fix for a slow notebook is usually a config line, not a bigger cluster.One Spark Config, 32 Minutes Saved: Replacing MERGE with Dynamic Partition OverwriteBy @Phani_sannala , co-authored with @sridharplv Our gold n...
- 502 Views
- 5 replies
- 16 kudos
- 16 kudos
The zombie row bug is the real argument here reframing this from "MERGE is slow" to "MERGE is silently wrong for full-slice delivery" is what makes it land. Only thing I'd stress: dynamic partition overwrite is exactly as safe as your completeness ch...
- 16 kudos
- 574 Views
- 0 replies
- 1 kudos
Stop Translating Alteryx Boxes - A Lakebridge-assisted, test-driven migration to Azure Databricks
Lakebridge can assess an Alteryx estate, but the current support matrix does not list Alteryx for automated conversion or direct reconciliation. A safe migration therefore combines Lakebridge assessment with deliberate redesign, native Databricks eng...
- 574 Views
- 0 replies
- 1 kudos
- 931 Views
- 0 replies
- 0 kudos
Apache Spark 4.2 is officially here! Key architectural updates for AI-Native & Governed Platforms
Hi community!Matei Zaharia and the Databricks team just announced the release of Apache Spark 4.2. As a Data Architect, seeing how this engine is evolving to bridge the gap between traditional data engineering, governance, and the AI era is incredibl...
- 931 Views
- 0 replies
- 0 kudos
- 704 Views
- 1 replies
- 0 kudos
From RAG Demo to Production on Databricks: 7 Things Teams Should Validate First
From RAG Demo to Production on Databricks: 7 Things Teams Should Validate FirstBy Naveen AyallaMany teams can build a RAG demo quickly.Upload documents, create embeddings, connect a model, ask a question, and show an answer.But production is differen...
- 704 Views
- 1 replies
- 0 kudos
- 0 kudos
Thanks for reading. I’m especially interested in hearing from people who have worked on real RAG or GenAI workflows.Which one has been the biggest challenge for your team?1. Choosing the right source data2. Access control and governance3. Improving r...
- 0 kudos
- 550 Views
- 0 replies
- 1 kudos
How we solved the "18-Hour Running Job" problem with Data-Driven Timeouts
Hi everyone,I recently dealt with a frustrating scenario: a Databricks job that usually takes minutes ran for 18 hours without failing, quietly consuming compute and blocking downstream pipelines.The driver hadn't crashed, and the job hadn't failed—i...
- 550 Views
- 0 replies
- 1 kudos
- 654 Views
- 0 replies
- 1 kudos
One Cluster per Task — Proven, Ready, and Waiting
Part 3 of 3: Databricks Streaming ArchitectureBy the end of Part 1 & Part 2, we knew what the real answer was. We just hadn’t committed to it yet.Not because it wouldn’t work. We tested it. We documented it. The code was ready. The answer was one clu...
- 654 Views
- 0 replies
- 1 kudos
- 11244 Views
- 3 replies
- 6 kudos
Resolved! CI/CD on Databricks with Asset Bundles (DABs) and GitHub Actions
Hi all.If you've ever manually promoted resources from dev to prod on Databricks — copying notebooks, updating configs, hoping nothing breaks — this post is for you.I've been building a CI/CD setup for a Speech-to-Text pipeline on Databricks, and I w...
- 11244 Views
- 3 replies
- 6 kudos
- 6 kudos
Hi, Great question! Databricks Asset Bundles (DABs) are the recommended approach for CI/CD on Databricks. Here is a comprehensive walkthrough. WHAT ARE DATABRICKS ASSET BUNDLES? DABs let you define your Databricks resources (jobs, pipelines, dashboar...
- 6 kudos
- 841 Views
- 0 replies
- 1 kudos
How Upgrading to Databricks Runtime 16.4 sped up our Python script by 10x
Wanted to share something that might save others time and money. We had a complex Databricks script that ran over 1.5 hours, when the target was under 20 minutes. Initially tried scaling up the cluster, but real progress came from simply upgrading th...
- 841 Views
- 0 replies
- 1 kudos
- 3900 Views
- 2 replies
- 10 kudos
Optimizing Delta Table Writes for Massive Datasets in Databricks
Problem StatementIn one of my recent projects, I faced a significant challenge: Writing a huge dataset of 11,582,763,212 rows and 2,068 columns to a Databricks managed Delta table.The initial write operation took 22.4 hours using the following setup:...
- 3900 Views
- 2 replies
- 10 kudos
- 10 kudos
Hey @Louis_Frolio ,Thank you for the thoughtful feedback and great suggestions!A few clarifications:AQE is already enabled in my setup, and it definitely helped reduce shuffle overhead during the write.Regarding Column Pruning, in this case, the fina...
- 10 kudos
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