Our latest Data Window blog covers the first steps to setting up Databricks. Written from a graduate perspective, it's a practical introduction to creating a workspace, your first notebook and running your first command. 👉https://lnkd.in/gXpFyQBt #Databricks #DataEngineering #DataAnalytics #CloudData
Databricks Setup for Data Engineers
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I just ran my first query on Databricks Free Edition! ⚡ Turning raw data into insights directly in the browser. If you've been curious about Databricks, try it out for free here: https://lnkd.in/evkYMpJG%2Ftry-databricks%3Futm_source%3Dachievements%26utm_medium%3Dlinkedin%26utm_campaign%3Dfe_learning_achievements%26utm_content%3Drun-queries-1 #Databricks #FreeEdition #Learning
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Just earned my Microsoft Fabric Data Engineer Associate certificate ✅. Data platforms are converging fast. Fabric puts the lakehouse, warehouse, orchestration, and streaming layer on one governed surface. I wanted some hands on reps before that becomes the default stack instead of the new thing. What I learned 💡 : 1. The transaction log of Delta Lake is what makes the lakehouse vs warehouse choice answerable, not ideological. Medallion layering is a consequence of that, not a style preference. 2. Idempotency is a design choice, not a retry setting. Watermarking and incremental load pattern pipelines fail silently and the exam scenarios were unforgiving of this. 3. Most “we need this live” requirements are really “we need this in 15 minutes.” Eventstream → Eventhouse → KQL made that cost difference between the two concrete. 4. Workspaces must act like environments, not like shared folders. Git integration and deployment pipelines are the boring part nobody demos, and the part that decides if a platform lives or dies in a team. 5. Governance is not the destination. Row level security and workspace roles need to be designed in, not bolted on later. #DataEngineering #MicrosoftFabric #Azure #Spark #MedallionArchitecture #KQL #DeltaLake #DataEngineer
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Spent some time building out a test environment in Microsoft Fabric Lakehouses on @MicrosoftLearn. The architecture simplifies a few big operational headaches: ▪︎Zero-Copy Architecture: Using OneLake Shortcuts, I was able to target external S3/ADLS Gen2 buckets directly. Skipping full-copy ETL saves massive bandwidth and eliminates duplicate storage costs. ▪︎ACID on Parquet: Because tables persist in Delta Lake format (Parquet files + _delta_log), concurrent reads and writes handle batch updates without file corruption or locks. ▪︎Dual-Engine Access: Engineers can run PySpark operations on the Delta tables while analysts run ad-hoc queries on the exact same underlying files through the auto-generated SQL Analytics Endpoint—no data duplication required. A solid conceptual base. Next step: testing PySpark notebook performance with V-Order optimization and checking how VACUUM commands clean up uncommitted transaction logs under heavier write loads. #MicrosoftFabric #DataEngineering #Lakehouse #DeltaLake #PySpark
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30 Days of Databricks – Day 22 | Understanding Databricks Jobs & Scheduling Today I learned about Databricks Jobs, which are used to automate and schedule data engineering workloads. Instead of manually running notebooks every day, we can create a Job that executes tasks automatically based on a schedule, event, or dependency.
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Most teams don't have a Databricks problem. They have an underutilization problem. You're paying for features that can: ⚡ Speed up queries 📉 Reduce storage costs 🧠 Optimize workloads automatically 🔧 Eliminate manual maintenance Yet many teams still rely on notebooks + clusters and leave the rest untouched. Here's a question: Which of these features have you actually enabled in production? ⬜ Photon Engine ⬜ Liquid Clustering ⬜ Predictive Optimization ⬜ Optimize Write + Auto Compaction ⬜ Adaptive Query Execution (AQE) ⬜ Serverless Compute 👇 Be honest. Which one is still on your "we'll enable it later" list? #Databricks #DataEngineering #ApacheSpark #DeltaLake #BigData #Lakehouse #DataPlatform #MicrosoftFabric #Analytics #CloudData
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Why would anyone want their Gmail in Databricks? Support teams tracking response times, compliance teams building an audit trail, anyone doing communication analysis at scale. Turns out there's a real use case here and Databricks shipped a connector for it. Lakeflow Connect now has a Gmail connector, currently in Beta. It pulls messages, labels, drafts and filters straight out of a mailbox using a Google service account with domain-wide delegation. Messages and message labels sync incrementally through the Gmail History API. Everything else gets a full refresh on every pipeline run, so keep that in mind if you're ingesting a mailbox with years of drafts sitting in it. ⚠️Each connection covers a single mailbox. Need three inboxes ingested? That's three connections, each one stamping its own mailbox column. What would you actually use inbox data for once it's sitting in a lakehouse table? #databricks #dataengineering #lakeflow #dataengineer
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Most Databricks customers are spending around $300,000 a year on the platform. And a significant chunk of that is going to waste. Idle clusters, the wrong compute type for the job, ungoverned workspaces, BI queries that run far longer than they should. These are fixable problems. They just require knowing where to look. On August 27 at 2 PM CEST, our CTO Alexandru Puiu and BI Technical Lead Vlad-Alexandru Mihalcea are running a free live session on how to get your Databricks deployment under control without slowing your team down. One hour. Practical tips you can act on the same week. Live Q&A at the end. 📅 August 27 | 2:00 PM CEST | Free 🎤 Alexandru Puiu, CTO @ mindit.io 🎤 Vlad-Alexandru Mihalcea, BI Technical Lead @ mindit.io 👉 Register now: https://lnkd.in/dinHFXCh
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It's always great to hear success stories from our customers. On one specific workload, the difference took us by suprise: Databricks: 39 minutes LakeSail: 13 seconds On the same query and the same hardware, LakeSail delivered a ~180x speedup. One workload does not tell the whole story, but it shows what becomes possible when the engine is rebuilt from the ground up. EDIT: I misread the query results. Databricks took 39 minutes, not 29 minutes.
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Frontend Developer • Contractor • Freelancer • Implementing clean, robust and maintainable software
"Why was this month expensive?" - the question every Databricks estate eventually asks, usually at month-end, when the trail is cold. At one large enterprise, spend had jumped about $35K a week. In Blueprint's Lakehouse Optimizer, the answer took under five minutes: tenant → workspace → the week it started → the two jobs and the config change behind them. Their words: "It would have been impossible without LHO to figure out where it's coming from." The product decision that makes this possible: cost, workload and configuration history live in one connected drill, so "when → what → why" is a navigation, not an investigation. The honesty that keeps it credible: it works because the telemetry was already flowing before the spike - visibility is a thing you set up ahead of the question. How long did your last cost-spike investigation take? #Databricks #DataEngineering #LakehouseOptimizer #Spark
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