#databricksbasics search results

In production, DBFS stores system files, logs, and temp artifacts NOT your business data. All production data MUST go to ADLS/S3/GCS under governance of Unity Catalog. #DatabricksBasics

When is repartition(1) acceptable? Exporting small CSV/JSON to downstream systems Test data generation Creating a single audit/control file #Databricks #DatabricksDaily #DatabricksBasics

5/5 Verify & start building In your workspace: You now have a working UC hierarchy. Metastore → Catalog → Schema → Table #DataBricksBasics #UnityCatalog #Databricks #Azure #DataGovernance

4/5 Attach workspaces In the Account Console → Workspaces, select your workspace → Assign Metastore → choose the one you created. 🧭 All data access in that workspace now flows through Unity Catalog. #DataBricksBasics #UnityCatalog #Databricks #Azure #DataGovernance

2/5 Give UC access Create a Databricks Access Connector (a managed identity). Then in Azure → grant it the Storage Blob Data Contributor role on your ADLS container. This replaces old mount-based access no more secrets in code. #DataBricksBasics #UnityCatalog #Databricks…

1/5 Creating Unity Catalog Metastore in Azure Databricks Prep the storage Create an ADLS Gen2 account with Hierarchical Namespace (HNS) enabled. This will hold your managed tables & UC metadata. Think of it as the “hard drive” for Unity Catalog. #DataBricksBasics…

Your dataset’s growing fast… but so are the duplicates. In Databricks, cleaning that up is one line away Keeps only the first unique row per key no loops, no hassle, just clean data at Spark scale. #DataBricksBasics #Databricks #DeltaLake #PySpark #DataCleaning

Got tons of Parquet files lying around but want Delta’s ACID, power versioning, time travel, and schema enforcement? You don’t need to rewrite everything just convert them in place #DataBricksBasics #DeltaLake #Databricks #DataEngineering

Need to trace how your Delta table changed and even query its past state? Delta Lake makes it simple, Full audit trail + time travel, built right into your table. #DataBricksBasics #DeltaLake #Databricks

Performing an UPSERT in Delta is just a MERGE with both update and insert logic perfect for handling incremental or slowly changing data. One command to merge, update, and insert your data reliably. #DataBricksBasics #DeltaLake #Databricks

Need to synchronize incremental data in Delta Lake? MERGE lets you update existing rows and insert new ones in a single atomic operation. No separate update or insert steps needed. #DataBricksBasics #DeltaLake #Databricks

Want to delete rows from your Delta table? It’s built right in: Delta Lake makes deletions as simple as SQL, with ACID guarantees. Example: Delete records where status='inactive': #DataBricksBasics #Databricks #DeltaLake #DataEngineering

Want to update your data in Delta tables? It's as easy as this: #DataBricksBasics #Databricks #DeltaLake #DataEngineering

Ever noticed .snappy.parquet files in your Delta tables? .parquet = columnar file format .snappy = compression codec Databricks uses Snappy by default 🔹 fast to read 🔹 light on CPU 🔹 great for analytics If you want to change you can change it as below #DataBricksBasics…

Did you know every table you create in Databricks is already a Delta table? Under the hood, Databricks = Delta Lake + extras: extras = ⚙️ Photon → faster queries 🧠 Unity Catalog → access control & lineage 🤖 DLT + Auto Loader → automated pipelines #DataBricksBasics…

Want to read a Parquet file as a table in Databricks? ✅ Table auto-registered ✅ Query instantly via SQL ✅ Works with Parquet or Delta #DataBricksBasics #Databricks #DataEngineering

In Databricks, an external table means: You own the data. Databricks just knows where to find it. ✅ Drop it → data stays safe ✅ Reuse path across jobs ✅ Perfect for production pipelines ⚙️ #DataBricksBasics

A traditional managed table is fully controlled by Databricks , It decides where to store, how to track, and even when to delete. Drop it → data & metadata gone. Simple. Safe. Perfect for exploration. #DataBricksBasics

Databricks doesn’t “store” your data it remembers it 🧠 The Hive Metastore keeps track of: Table names Schema File paths When you query, Databricks looks up metadata, finds your files, and reads from storage #DataBricksBasics

11/12 Tune cluster & runtime features. Use autoscaling job clusters, appropriate executor sizes, and Databricks runtime features (I/O cache, Photon where available) to reduce latencies for heavy join stages. #DataBricksBasics

Need to trace how your Delta table changed and even query its past state? Delta Lake makes it simple, Full audit trail + time travel, built right into your table. #DataBricksBasics #DeltaLake #Databricks

Need to synchronize incremental data in Delta Lake? MERGE lets you update existing rows and insert new ones in a single atomic operation. No separate update or insert steps needed. #DataBricksBasics #DeltaLake #Databricks

Want to delete rows from your Delta table? It’s built right in: Delta Lake makes deletions as simple as SQL, with ACID guarantees. Example: Delete records where status='inactive': #DataBricksBasics #Databricks #DeltaLake #DataEngineering

Performing an UPSERT in Delta is just a MERGE with both update and insert logic perfect for handling incremental or slowly changing data. One command to merge, update, and insert your data reliably. #DataBricksBasics #DeltaLake #Databricks

Want to update your data in Delta tables? It's as easy as this: #DataBricksBasics #Databricks #DeltaLake #DataEngineering

Your dataset’s growing fast… but so are the duplicates. In Databricks, cleaning that up is one line away Keeps only the first unique row per key no loops, no hassle, just clean data at Spark scale. #DataBricksBasics #Databricks #DeltaLake #PySpark #DataCleaning

In Databricks, an external table means: You own the data. Databricks just knows where to find it. ✅ Drop it → data stays safe ✅ Reuse path across jobs ✅ Perfect for production pipelines ⚙️ #DataBricksBasics

1/12 Instead of accepting slow joins, tune your data & layout first. Left/right/outer joins can be heavy they usually trigger shuffles. These tips help reduce shuffle, memory pressure, and runtime on Databricks. #DataBricksBasics

Got tons of Parquet files lying around but want Delta’s ACID, power versioning, time travel, and schema enforcement? You don’t need to rewrite everything just convert them in place #DataBricksBasics #DeltaLake #Databricks #DataEngineering

Want to read a Parquet file as a table in Databricks? ✅ Table auto-registered ✅ Query instantly via SQL ✅ Works with Parquet or Delta #DataBricksBasics #Databricks #DataEngineering

Instead of doing a regular inner join... have you tried a broadcast join? ⚡ When one DataFrame is small, Spark can broadcast it to all workers no shuffle, no waiting. One keyword. Massive performance boost. #DataBricksBasics #Spark #Databricks

Ever noticed .snappy.parquet files in your Delta tables? .parquet = columnar file format .snappy = compression codec Databricks uses Snappy by default 🔹 fast to read 🔹 light on CPU 🔹 great for analytics If you want to change you can change it as below #DataBricksBasics…

5/5 Verify & start building In your workspace: You now have a working UC hierarchy. Metastore → Catalog → Schema → Table #DataBricksBasics #UnityCatalog #Databricks #Azure #DataGovernance

1/5 Creating Unity Catalog Metastore in Azure Databricks Prep the storage Create an ADLS Gen2 account with Hierarchical Namespace (HNS) enabled. This will hold your managed tables & UC metadata. Think of it as the “hard drive” for Unity Catalog. #DataBricksBasics…