All Databricks Free Edition Help posts

Re: Unable to Start Serverless Compute in Databricks Free Edition

Ashwin_DSA — Sat, 12 Sep 2026 19:16:51 GMT

Welcome back to Databricks! Here are some options to help you work through this.

Firstly, check whether you are on the Free Edition or the old Databricks Free Edition. This is the most common cause of this exact error. The legacy Community Edition was retired in 2025 and replaced by the Free Edition. If your account has been dormant for a while, you may still be logged into a Community Edition workspace, where serverless compute simply won't start. You need to sign up for the Free Edition separately, even if you use the same email. Head to the Free Edition signup page and create a new Free Edition workspace. Your old Community Edition login won't automatically convert.

Just so you are aware, the Free Edition limitations page states that Databricks may delete Free Edition accounts that remain inactive for an extended period. If your account was deleted due to inactivity, you would need to sign up again from scratch. Your previous data and notebooks would not carry over in that case.

Free Edition accounts are subject to a fair usage policy. If you exceed your quota, compute resources are shut down for the rest of the day (or in extreme cases, the rest of the month). Your data stays intact, and you can resume once the limit resets. If you just logged in and ran several notebooks or queries, this could be the culprit. Try the next day again.

Try doing a hard refresh of your browser (Ctrl+Shift+R or Cmd+Shift+R) to clear any stale session state. If that doesn't help, close all open notebooks, create a brand new notebook, and attach it to serverless compute. Some community members have also reported that imported notebooks default to an older environment version that causes this error. If you imported notebooks, check the environment version in the notebook settings and change it to the latest available version.

If none of the above resolves it, the most reliable path is to sign up for a fresh Free Edition account at databricks.com/try-databricks. Since Free Edition doesn't come with formal support or an SLA, the Databricks Free Edition Help board here in the community is the best place to follow up if you're still stuck. Include any error details or screenshots so others can help narrow it down.

Hope this helps.

If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.

Re: Unable to Start Serverless Compute in Databricks Free Edition

Satyasai — Fri, 11 Sep 2026 05:38:26 GMT

Hi @HariSankar ,

If you opened an old notebook or imported a notebook (DBC, HTML, or Git), then Databricks may default its serverless execution environment version to an older version (like Version 1) that isn't supported anymore on the current Serverless Compute.

Fix: Open your notebook, go into the upper right corner where compute is attached, open Environment / Compute Settings, and update the Environment Version to the latest version (like Version 2 or higher). Or, you can copy-paste your code into a brand new notebook and attach Serverless Compute there.

Unable to Start Serverless Compute in Databricks Free Edition

HariSankar — Fri, 11 Sep 2026 04:51:48 GMT

Hello everyone,

I am using Databricks Free Edition. I had not used my account for quite some time, and I recently logged in to start working again.

When I try to start Serverless Compute, I get the following error:
Cannot start serverless compute

Has anyone faced this issue recently? Is there any fix or workaround?

Could this be related to my account being inactive for a long time or a server-side problem?

Any help would be appreciated.

Thanks.

🚀 Quest 5 Submission: Intelligent RAG Knowledge-Base & Note-Taking Workspace

User_sky — Thu, 10 Sep 2026 18:17:24 GMT

Hi everyone! 👋

I am thrilled to share my final submission for Databricks AppKit Quest 5! I took the initial starter template and evolved it into a full-fledged, serverless Intelligent Knowledge-Base powered by Databricks Apps, Lakebase, and Foundation Model Serving.

Here is my project: 🔗 Live Deployed App: https://my-databricks-app-7474654586328503.aws.databricksapps.com/lakebase 🎥 YouTube Demo Video: https://youtu.be/n8nmNVhKEdg?si=leKi5vBQKmeYzBzJ

🌟 Going the Extra Mile (Training Voucher Eligibility)

To qualify for the Databricks training vouchers, I wanted to really push the boundaries of what this app could do. I implemented five significant custom features beyond the basic requirements:

Dual-Pane Markdown Engine: A fully functional Markdown editor with a live HTML preview tab to format notes professionally.
Focus Time Tracker: An integrated session stopwatch that tracks your active study/work time on a specific note and persists it directly to Lakebase PostgreSQL.
Bidirectional Note Linking: A cross-referencing system that allows users to link related notes together, creating a navigable knowledge graph.
Tokenized RAG Assistant: An AI agent that parses natural language questions (dropping stop words), searches Lakebase in real-time, summarizes insights, and provides clickable source citations.
Advanced Tagging & Priority: Visual priority badges and a dynamic tagging system for better organization.

🤖 Agents, Prompts & How I Built It

The Agent I Used: I used Gemini as my primary coding assistant and thought partner throughout this quest to iteratively build, debug, and deploy the application.

My Prompting Strategy: Instead of asking for everything at once, I used an Iterative Specification Prompting approach:

Template Injection: I fed the agent the DevHub RAG_TEMPLATE.md and told it: "Base the AI Assistant RAG on this template, and update my PLAN.md to reflect this new architecture."
Modular Planning: I prompted the agent to break the build into three distinct plans: 01-database-and-schema.md, 02-rag-assistant-service.md, and 03-frontend-ux.md.
Targeted Debugging Prompts: When I hit roadblocks, I used specific diagnostic prompts like: "My app fails to deploy with a '42501: must be owner of schema app' error in the SQL Editor. How do I fix the Service Principal permissions?" and "The RAG agent is doing an exact substring match and failing on natural language. Write a script to patch the backend SQL query to tokenize keywords instead."

How I Achieved the Tasks:

Schema Evolution: I first expanded the basic Lakebase schema to include tables for notes, tags, note_tags, and note_links with cascading deletes.
Frontend UI/UX: I completely redesigned the React frontend, adding the Markdown toggle (marked.parse), the interactive timer, and a dedicated RAG drawer.
Overcoming RAG Limitations: The biggest challenge was making the RAG agent understand conversational questions. I patched the backend to filter out stop-words (like "what", "is", "about") and dynamically build a parameterized ILIKE SQL query for the remaining tokens.
Deployment: After clearing up the local vs. Service Principal ownership conflicts in the Databricks SQL Editor by dropping the legacy tables, I successfully pushed everything to production using databricks apps deploy.

A huge thank you to the Databricks and AngelHack teams for putting together this incredible AppQuest. It was a fantastic hands-on experience with Lakehouse architecture!

#Databricks #DatabricksAppKit #GenerativeAI #Lakehouse #RAG #Serverless

Databricks Quest 4-(Deploy Your First Databricks App) success story

User_sky — Thu, 10 Sep 2026 14:30:28 GMT

🚀 Deployed Databricks AppKit + Lakebase: 2 Tricky Bugs & How I Solved Them!

Just deployed my first full-stack Databricks App using the AppKit Lakebase (PostgreSQL) template on serverless compute!

Using Gemini as an AI debugging agent via raw CLI logs in WSL, I diagnosed and resolved two critical roadblocks:

Bug 1: Missing DABs Variables (postgres_project)
- Fix: Added postgres_project, postgres_branch, and postgres_database directly under the variables: block in databricks.yml to unblock databricks apps deploy.
Bug 2: "Failed to Fetch Todos" (Silent Permission Denied 42501)
- Root Cause: Testing locally (npm run dev) first meant my personal user owned the app schema. The deployed app runs under a Service Principal, which was denied write access.
- Fix: Dropped the schema via DROP SCHEMA IF EXISTS app CASCADE; in the SQL Editor and redeployed. The Service Principal recreated the schema and took proper ownership!

💡 Pro-tip: When building with Lakebase, deploy first, test locally second so your App's Service Principal owns the schema from day one!

Re: Request Access for Notebooks or workspaces from Databricks Community Edition

upendra2 — Thu, 10 Sep 2026 11:08:52 GMT

Databricks Account locked Issue

vikramraj — Wed, 09 Sep 2026 11:08:10 GMT

Hi Team

I am writing to raise a concern regarding continued difficulty accessing a Databricks free trial account using my company email address. This issue has persisted for an extended period and I would appreciate your assistance in resolving it. It would be really appreciable if anyone guide on this.

Details of the issue:

Company Email ID used for signup: [raj.sh@myCompanyName.com]
Issue duration: I have faced the same error for the past three weeks a screenshot is attached.
Partner: Even though we are partner with databricks
Error message (if any): [ "Limit Reached" I am getting: "You've reached today's limit and you've raised limit the maximum number of times this year . Your limit resets tomorrow" ]

I hope anyone of you considers this issue as we are working on a small POC to present a demo to internal clients. But we are completely blocked. Let me know if there is any way to unlock the account and use it , I am attaching a screenshot for refrence.

I await your response. Thank you for your understanding.

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Re: MetaData Framework for Multi-Level Silver Layer PK/FK Creation

SantiNath_Dey — Sun, 06 Sep 2026 16:11:17 GMT

Thank you for quick response

Re: MetaData Framework for Multi-Level Silver Layer PK/FK Creation

Ashwin_DSA — Sun, 06 Sep 2026 15:29:53 GMT

Hi @SantiNath_Dey,

Databricks has solid building blocks for this, and there's even a purpose-built metadata framework you can lean on.

At its core, your pipeline needs to do three things...generate surrogate keys at each level, resolve parent references top-down, and declare the constraints. Here's how to approach each of them individually.

For the first one... surrogate key generation... Databricks supports generated columns using GENERATED ALWAYS AS IDENTITY or GENERATED BY DEFAULT AS IDENTITY on Delta tables. However, identity columns disable concurrent writes and don't survive a full table refresh (a rebuild can reassign different IDs to the same entity). For your use case involving hierarchical data that may be reprocessed, Databricks recommends using deterministic surrogate keys derived from the natural key instead. For example, you could derive an order-preserving surrogate from a composite natural key rather than using sha2() (which scatters data and hurts clustering performance). Check this.

For the second one... top-down parent-child resolution... Since your hierarchy is emp → emp_addr → emp_addr_ofc, you process tables in dependency order. Generate the surrogate key for emp first, then when processing emp_addr, look up the parent's surrogate key via the natural key relationship and store it as the foreign key column. Repeat for emp_addr_ofc referencing emp_addr. A metadata config table can automatically drive this ordering.

And for the constraints... once your tables are loaded with the correct key columns, you register the relationships using ALTER TABLE... ADD CONSTRAINT.

-- Level 1: emp ALTER TABLE silver.emp ADD CONSTRAINT pk_emp PRIMARY KEY (emp_sk); -- Level 2: emp_addr ALTER TABLE silver.emp_addr ADD CONSTRAINT pk_emp_addr PRIMARY KEY (emp_addr_sk); ALTER TABLE silver.emp_addr ADD CONSTRAINT fk_emp_addr_emp FOREIGN KEY (emp_sk) REFERENCES silver.emp; -- Level 3: emp_addr_ofc ALTER TABLE silver.emp_addr_ofc ADD CONSTRAINT pk_emp_addr_ofc PRIMARY KEY (emp_addr_ofc_sk); ALTER TABLE silver.emp_addr_ofc ADD CONSTRAINT fk_emp_addr_ofc_addr FOREIGN KEY (emp_addr_sk) REFERENCES silver.emp_addr;

These are informational constraints in Unity Catalog. They are not enforced at write time, but they serve as metadata for BI tools, query optimizers, and documentation. If you need actual enforcement, pair them with NOT NULL constraints and CHECK constraints, or validate in your pipeline logic before writing.

Rather than hand-coding each table's key generation and constraint registration, you can define a metadata configuration (JSON or a Delta table) that describes the hierarchy:

[ {"table": "emp", "natural_key": ["emp_id"], "surrogate_key": "emp_sk", "parent": null}, {"table": "emp_addr", "natural_key": ["emp_id", "addr_id"], "surrogate_key": "emp_addr_sk", "parent": {"table": "emp", "join_key": ["emp_id"]}}, {"table": "emp_addr_ofc", "natural_key": ["emp_id", "addr_id", "ofc_id"], "surrogate_key": "emp_addr_ofc_sk", "parent": {"table": "emp_addr", "join_key": ["emp_id", "addr_id"]}} ]

A generic Python script reads this config, topologically sorts by parent dependencies, and for each table: (1) generates the deterministic surrogate key from the natural key columns, (2) joins to the parent table to resolve the parent's surrogate key as the FK column, and (3) issues the ALTER TABLE ADD CONSTRAINT statements.

If you want a production-grade metadata-driven framework, take a look at sdp-meta (formerly dlt-meta) from Databricks Labs. It's a metaprogramming framework designed for exactly this pattern... you maintain JSON/YAML metadata describing your tables, and it dynamically generates Lakeflow pipelines for your bronze and silver layers. The benefits are consistency across hundreds of tables, less custom code, and easier maintenance since you're updating config files rather than pipeline logic.

If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.

MetaData Framework for Multi-Level Silver Layer PK/FK Creation

SantiNath_Dey — Sun, 06 Sep 2026 14:02:56 GMT

Hi Team,
Our source data originates from MongoDB as hierarchical JSON and is ingested into our Silver layer, where it is already normalized into tabular structures. However, primary and foreign key constraints are not enforced during ingestion. We must establish and maintain PK/FK relational integrity post-loading through an automated post-processing step.The Challenge:The source system features a deep, multi-level parent-child tree hierarchy (e.g., emp -> emp_addr -> emp_addr_ofc). We require a metadata-driven modeling framework and script execution strategy to dynamically generate surrogate keys, resolve parent-child references top-down, and enforce relational integrity across all levels.

Please find below screen shot for your reference.

Re: Recommendation for Data Reconciliation Frameworks (Legacy vs. Migrated Validation)

ThiamLee — Sun, 06 Sep 2026 11:04:48 GMT

This is a great use case for a data reconciliation framework. Automating schema, row counts, aggregates, and row-level hash comparisons would save a lot of manual effort and make migration validation much more reliable.

Re: Recommendation for Data Reconciliation Frameworks (Legacy vs. Migrated Validation)

Ashwin_DSA — Sat, 05 Sep 2026 19:44:57 GMT

Hi @SantiNath_Dey,

Great question. This is something I have dealt with firsthand across multiple large-scale data warehouse migrations, and I actually wrote a blog post recently that covers exactly this. I would highly recommend checking it out: Speed Up Data Warehouse Migration Validation.

But let me give you the short version here, mapped directly to the three areas you mentioned.

For schema and volumetrics, you don't need a separate tool. Databricks SQL scripting now lets you build a reusable stored procedure that handles all of this in one parameterised call. In the blog, I walk through a validate_migration procedure that takes a source table, a target table, key columns, and check columns as parameters, then automatically runs row-count comparisons and column-level aggregate checks, and writes every result to a central migration_validation.results table with timestamps. The key win here is consistency. Every team member runs the same procedure, every result lands in the same place, and you stop comparing screenshots in meetings.

Your question about aggregations and hashes is covered in Tip 2 of the blog. For column-level aggregates (SUM, AVG on numeric/decimal columns), the stored procedure handles that through a loop over your check columns. For hash-based comparison, I recommend generating an MD5 hash per row using MD5(CONCAT_WS('|', ...)) across the columns you care about, then doing a FULL OUTER JOIN on the primary key to find mismatches. Something like:

SELECT s.policy_id, s.row_hash AS source_hash, t.row_hash AS target_hash FROM source_hashed s FULL OUTER JOIN target_hashed t ON s.policy_id = t.policy_id WHERE s.row_hash != t.row_hash OR s.policy_id IS NULL OR t.policy_id IS NULL;

This gives you three things that a simple EXCEPT doesn't. You can identify which rows differ (not just that they differ), you can join back to the source to see what changed, and you can store hashes for incremental comparison on subsequent runs.

The hash approach above helps with row-level integrity. For billion-row tables, computing a hash per row and comparing on the primary key is far more efficient than a full EXCEPT. And because you are joining on the PK, you get a clean mismatch report that tells you whether a row is missing, extra, or changed...which is exactly what your team needs for root-cause analysis.

You mentioned you are currently using custom Python/PySpark scripts. The blog makes the case that you can replace all of that with a DBSQL-native validation architecture that's simpler to maintain and more powerful:

SQL Scripting for reusable, parameterized validation procedures
EXCEPT + MD5 hashes for row-level comparison at scale
Unity Catalog for automatic lineage tracking and tagging tables with validation status (so you can answer "who validated what and when?" without digging through Slack)
AI/BI Dashboards that query your validation results table directly... stakeholders see pass/fail status in real time instead of waiting for someone to email a spreadsheet
Databricks Jobs to schedule validation runs on a cadence (daily during migration, hourly during cutover) with alerts when a check flips from pass to fail

The blog has full code examples for each of these, including the stored procedure, the hash comparison views, the Unity Catalog tagging queries, and the dashboard dataset SQL. I would start there and adapt the patterns to your specific tables.

Hope this helps.

If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.

Recommendation for Data Reconciliation Frameworks (Legacy vs. Migrated Validation)

SantiNath_Dey — Sat, 05 Sep 2026 18:49:16 GMT

Hi Team,

We need to perform a data quality check between an legacy table and a newly migrated table generated by refactored code. Specifically, we want to validate:

Schema & Volumetrics: Total row counts, column counts, and column data type distributions (e.g., integer vs. string counts).

Aggregations & Hashes: Column-level aggregates (sum and average for numeric/decimal types) and overall table-level MD5 hashes.

Row-Level Integrity: Primary key-based row-level MD5 hash comparisons to detect individual discrepancies.

Although we currently use custom Python/PySpark scripts, is there an existing data reconciliation framework that can automate these checks and generate detailed mismatch reports for further analysis?

Re: Demo: REQUIRED - Data Setup and Exploration

Advika — Wed, 02 Sep 2026 09:55:22 GMT

Thanks for confirming @YuryRu!

The labs shown in the courses and videos aren’t included with the free self-paced courses. For independent, hands-on practice, you can use Databricks Free Edition to build your own projects. However, Free Edition does not include the guided labs used in the courses.

If you’d like to access the notebooks and hands-on labs shown in the course, you can choose either of the following options:

Enroll in an Instructor-Led Training (ILT) course – this provides access to the hands-on labs for 7 days.
Get Databricks Academy Lab Subscription (Databricks Academy → Subscription Plans → Databricks Academy Labs) – this is a great option for long-term learning, as it provides one year of access to both the labs and the self-paced courses included in the subscription.

Re: Demo: REQUIRED - Data Setup and Exploration

YuryRu — Fri, 28 Aug 2026 15:43:23 GMT

Hi @Advika,

The screenshot is of the notebook shown in the course video, but I’m not sure if there’s a way to have it in my Databricks Free Edition so I can practice the demos and labs.

Re: Demo: REQUIRED - Data Setup and Exploration

Advika — Fri, 28 Aug 2026 10:02:13 GMT

Hello @YuryRu,

From the screenshot you shared, it looks like you’ve found the notebook. Please let me know if you’re still unable to locate it.

Re: Demo: REQUIRED - Data Setup and Exploration

YuryRu — Thu, 27 Aug 2026 17:41:36 GMT

Demo: REQUIRED - Data Setup and Exploration

YuryRu — Thu, 27 Aug 2026 15:01:04 GMT

Hi!

I’m new to Databricks.
I’m taking the AI/BI for Data Analysts course, and I’d like to know how I can access the notebook “Demo: REQUIRED - Data Setup and Exploration.”