30 Databricks Interview Questions for 2026

Practice 30 Databricks interview questions on Lakehouse, Delta Lake, Unity Catalog, ETL, Spark performance, and system design with answers.

Databricks Interview Questions: 30 Most Asked (2026)

Databricks interview questions test whether you can actually build and operate on the platform — not just define terms. If you're preparing for a data engineering role at Databricks or a company running its data stack on Databricks, this is the list that matters: 30 questions across architecture, Delta Lake, ETL, performance tuning, and system design, with answers you can say out loud in a real interview.

This post covers the question categories that come up most often, how the interview process is typically structured, and what to focus your prep time on.

How Databricks interviews are structured

Typical round sequence

The exact format varies by role (data engineer vs. solutions architect vs. ML engineer), but the general shape looks like this:

• Recruiter screen — role fit, compensation expectations, timeline
• Technical phone screen — Databricks platform fundamentals, Spark basics, SQL
• Coding / SQL round — live problem-solving, often on a shared environment
• System design round — end-to-end pipeline design, trade-off discussions
• Behavioral / values round — collaboration, ownership, how you handle ambiguity
• Hiring manager conversation — team fit, scope, growth expectations

What interviewers are actually testing

• Hands-on Databricks platform knowledge — not just "I've read the docs"
• Spark internals: shuffles, partitioning, memory management
• Delta Lake design decisions and trade-offs
• Data modeling in a lakehouse context
• Clear communication of why you'd choose one approach over another
• Production experience: monitoring, debugging, cost management

Textbook definitions get you through the phone screen. Trade-off reasoning gets you the offer.

Databricks interview questions — core architecture & fundamentals

These questions show up early in the process. They test whether you understand the platform beyond surface-level marketing.

Q1: What is the Databricks Lakehouse architecture and how does it differ from a traditional data warehouse?

The Lakehouse combines the reliability and performance of a data warehouse with the flexibility and scale of a data lake. It stores data in open formats (Delta Lake on cloud object storage) while supporting ACID transactions, schema enforcement, and BI-quality query performance. Unlike a traditional warehouse, you don't need to copy data into a proprietary format to run analytics — the same storage layer serves ETL, ML, and SQL workloads.

Q2: Explain the control plane vs. compute plane vs. storage plane in Databricks.

The control plane is managed by Databricks — it handles the UI, job scheduling, notebook management, and cluster orchestration. The compute plane runs in your cloud account (AWS, Azure, or GCP) and is where Spark clusters execute. The storage plane is your cloud object storage (S3, ADLS, GCS) where the actual data lives. This separation means Databricks never stores your data; it only processes it.

Q3: What is Databricks Runtime (DBR) and what does Photon add to it?

DBR is the runtime environment that includes Apache Spark plus Databricks-specific optimizations — Delta Lake libraries, security patches, and performance improvements. Photon is a native vectorized query engine written in C++ that replaces parts of the Spark execution layer. Enabling it (`spark.databricks.photon.enabled = true`) can significantly speed up data transformations and SQL queries, especially on wide tables and aggregation-heavy workloads.

Q4: What are the differences between job clusters and all purpose clusters, and when do you use each?

All-purpose clusters stay running and are designed for interactive development — notebooks, exploration, ad-hoc queries. Job clusters spin up for a specific job and terminate when it finishes. Job clusters are often around 50% cheaper because you only pay for the compute time the job actually uses. Use all-purpose for development, job clusters for production pipelines.

Q5: What is DBFS and how does it relate to cloud object storage?

DBFS (Databricks File System) is an abstraction layer that lets you interact with cloud object storage using familiar file-system paths. Under the hood, `dbfs:/mnt/data/` maps to something like `s3://your-bucket/data/`. It simplifies path management, but the actual data lives in your cloud storage account, not on the Databricks side.

Q6: What is Unity Catalog and why does it matter for data governance?

Unity Catalog is Databricks' unified governance layer for data and AI assets. It provides centralized access control, auditing, lineage tracking, and data discovery across all workspaces. Before Unity Catalog, governance was workspace-scoped and fragmented. Unity Catalog lets you manage permissions at the account level — one policy for who can access what, across every workspace and every data asset.

Databricks interview questions — Delta Lake & storage

Delta Lake questions are the core of most Databricks technical interviews. Expect follow-ups on every answer here.

Q7: What makes Delta Lake ACID compliant and how does it handle concurrent writes?

Delta Lake uses a transaction log (`_delta_log`) that records every change as an ordered, atomic commit. Concurrent writes are handled through optimistic concurrency control — each writer reads the current log version, makes changes, and attempts to commit. If another writer committed first, the transaction retries against the new state. This gives you serializable isolation without locking the entire table.

Q8: How does Delta Lake handle schema evolution vs. schema enforcement?

Schema enforcement (also called schema-on-write) rejects writes that don't match the existing table schema. Schema evolution allows the schema to change — you enable it with `.option("mergeSchema", "true")` on a write operation. Enforcement protects data quality; evolution lets you add new columns as upstream sources change. In practice, use enforcement by default and enable evolution deliberately when you know the schema change is intentional.

Q9: What is time travel in Delta Lake and how do you use it?

Time travel lets you query previous versions of a Delta table. You can read a specific version with `SELECT * FROM table VERSION AS OF 5` or a specific timestamp with `TIMESTAMP AS OF '2025-01-01'`. It's useful for auditing, debugging data issues, and reproducing ML training datasets. The retention window depends on your VACUUM schedule.

Q10: What are the key differences between Delta Lake and Parquet?

Parquet is a columnar file format. Delta Lake is a storage layer built on top of Parquet that adds ACID transactions, a transaction log, time travel, schema enforcement, and the ability to do updates, deletes, and merges. A Delta table is Parquet files plus a `_delta_log` directory. You get everything Parquet gives you, plus reliability guarantees that plain Parquet can't provide.

Q11: What is the OPTIMIZE command and when should you run it?

`OPTIMIZE` compacts small files in a Delta table into larger ones, improving read performance. Small files accumulate naturally from streaming writes and frequent appends. Run it on tables that are read-heavy and have accumulated many small files — typically as a scheduled maintenance job, not after every write.

Q12: What is Z Ordering and when does it improve query performance?

Z-Ordering co-locates related data in the same set of files based on the columns you specify. `OPTIMIZE table_name ZORDER BY (column_a, column_b)` rearranges data so queries filtering on those columns skip more files. It's most effective on high-cardinality columns that appear frequently in WHERE clauses. Don't Z-Order on columns you rarely filter by — it adds write cost with no read benefit.

Q13: What does VACUUM do and what is the default retention period?

`VACUUM` removes data files that are no longer referenced by the Delta transaction log. The default retention period is 7 days (168 hours). You can override it with `VACUUM table_name RETAIN 168 HOURS`, but setting it below the default risks breaking time travel and concurrent readers. In production, keep the default unless you have a specific reason to shorten it.

Databricks interview questions — ETL pipelines & ingestion

These questions test whether you've built real pipelines, not just read about them.

Q14: What is Auto Loader and how does it differ from COPY INTO?

Auto Loader (`cloudFiles`) incrementally ingests new files from cloud storage using file notification or directory listing. It tracks which files have been processed using a checkpoint, so it never reprocesses data. `COPY INTO` is a SQL command that also loads new files, but it's simpler and doesn't scale as well for high-volume, continuous ingestion. Use Auto Loader for production streaming ingestion; use `COPY INTO` for ad-hoc or low-frequency batch loads.

Q15: What are Delta Live Tables (DLT) and what problem do they solve?

DLT is a declarative framework for building ETL pipelines. You define the desired state of each table — what it should contain and what quality rules it must pass — and DLT handles orchestration, dependency resolution, error handling, and infrastructure. It removes the boilerplate of managing pipeline DAGs manually and makes pipelines easier to maintain and monitor.

Q16: What are DLT Expectations and how do you use them for data quality?

Expectations are data quality rules you attach to DLT tables. You define constraints like `CONSTRAINT valid_id EXPECT (id IS NOT NULL) ON VIOLATION DROP ROW`. DLT evaluates every row against these rules and can drop, flag, or fail rows that violate them. This gives you built-in data quality enforcement without writing separate validation logic.

Q17: Explain the Medallion Architecture (Bronze/Silver/Gold) and when you'd use it.

Bronze is raw ingestion — data as it arrives, minimal transformation. Silver is cleaned and conformed — deduplication, type casting, joins. Gold is business-level aggregates and feature tables ready for analytics or ML. The pattern works well when you need auditability (Bronze preserves the raw record), incremental refinement, and clear separation between data engineering and data consumption layers.

Q18: How do you implement SCD Type 2 in Databricks using MERGE INTO?

`MERGE INTO` lets you match incoming records against existing records and apply conditional logic. For SCD Type 2, you match on the business key, close the existing record (set an end date and mark it inactive) when a change is detected, and insert the new version as an active record. The merge condition handles both inserts (new keys) and updates (changed attributes) in a single atomic operation.

Q19: What is Change Data Feed (CDF) in Delta Lake and when is it useful?

CDF captures row-level changes (inserts, updates, deletes) made to a Delta table and exposes them as a readable stream. You enable it with `delta.enableChangeDataFeed = true`. It's useful for building incremental downstream pipelines — instead of reprocessing the entire table, consumers read only the changes since their last checkpoint.

Databricks interview questions — performance tuning & optimization

This is where senior-level interviews spend the most time. Expect "how would you diagnose this?" follow-ups.

Q20: How do you optimize Spark jobs for performance in Databricks?

Start with the data layout: partition on high-cardinality filter columns, target roughly 128–200 MB per partition, and run `OPTIMIZE` with Z-Ordering on frequently queried columns. Enable Photon for compute-heavy transformations. Use broadcast joins for small dimension tables. Cache intermediate results only when they're reused multiple times in the same job. Then check the Spark UI for shuffle spills and skewed tasks — those are the two most common performance problems.

Q21: What is Adaptive Query Execution (AQE) and what does it fix?

AQE re-optimizes the query plan at runtime based on actual data statistics collected during execution. It can coalesce small shuffle partitions, convert sort-merge joins to broadcast joins when one side turns out to be small, and handle skewed joins by splitting large partitions. It's enabled by default in recent DBR versions and fixes the problem of static query plans making bad decisions based on stale or missing statistics.

Q22: How do you handle data skew in Spark?

Data skew means a few partitions hold disproportionately more data than others, causing some tasks to run much longer. Solutions: salting the skewed key (append a random suffix, join on the salted key, then aggregate), using AQE's skew join optimization, repartitioning before the join, or broadcasting the smaller table. The right fix depends on the join size and the degree of skew.

Q23: What causes shuffle and how do you reduce it?

Shuffle happens when Spark needs to redistribute data across partitions — triggered by joins, `groupBy`, `distinct`, and repartitioning. It's expensive because it involves disk I/O and network transfer. Reduce it by using broadcast joins when one side fits in memory, pre-partitioning data on the join key, avoiding unnecessary `repartition()` calls, and designing your pipeline so that wide transformations happen as late as possible.

Q24: What is dynamic file pruning and how does it speed up queries?

Dynamic file pruning skips reading files that can't contain matching rows based on join predicates. During a join, Databricks uses the filter values from the smaller side to prune files on the larger side at runtime. It's most effective on Delta tables with Z-Ordering or partitioning aligned to the join/filter columns. It happens automatically — you don't need to enable it, but you need the right data layout for it to help.

Q25: How do you diagnose performance issues using the Spark UI?

Check the Stages tab for tasks with high shuffle read/write, spill to disk, or uneven task durations (a sign of skew). The SQL tab shows the physical plan and where time is spent. Look for full table scans where file pruning should apply, large shuffles from non-broadcast joins, and GC overhead from memory pressure. The Storage tab tells you whether cached data is actually being used. Start with the slowest stage and work backward.

Databricks interview questions — workflows, security & system design

These questions test production maturity and architectural thinking.

Q26: What are Databricks Workflows and how do they compare to Apache Airflow?

Workflows is Databricks' native orchestration service for scheduling and managing multi-task jobs. It handles dependency management, retries, alerting, and cluster lifecycle within the Databricks environment. Compared to Airflow, Workflows is simpler to set up for Databricks-native pipelines and doesn't require managing a separate orchestration server. Airflow is more flexible for cross-platform orchestration involving non-Databricks systems.

Q27: How do you implement CI/CD for Databricks notebooks and jobs?

Use Databricks Repos for Git integration — connect your workspace to GitHub, GitLab, or Azure DevOps. Store notebooks and job definitions as code. Use a CI pipeline to run tests on pull requests and a CD pipeline to deploy to staging and production workspaces. Job configurations can be managed through the Databricks CLI or Terraform. The goal: no manual change in the UI reaches production without going through version control.

Q28: How does Databricks handle secrets and PII data?

Secrets are stored in Databricks secret scopes (backed by Databricks or an external vault like Azure Key Vault or AWS Secrets Manager) and accessed at runtime with `dbutils.secrets.get()`. They're never displayed in notebook output. For PII, use Unity Catalog column-level access controls, dynamic views that mask sensitive fields based on the user's group, and row-level security where needed. Secrets stay out of code; PII access is governed by policy.

Q29: What is Delta Sharing and what use case does it serve?

Delta Sharing is an open protocol for securely sharing live data across organizations without copying it. The data provider publishes a share; the recipient reads it using any client that supports the protocol — Databricks, Spark, pandas, or other tools. It's useful for B2B data exchange, cross-org analytics, and scenarios where you need to give a partner access to a live, governed dataset without moving data to their environment.

Q30: Walk me through how you'd design an end to end streaming pipeline in Databricks.

Ingest from a message queue (Kafka, Event Hubs, Kinesis) using Structured Streaming or Auto Loader into a Bronze Delta table — raw, append-only. Apply cleaning and deduplication in a Silver layer using streaming with watermarks for late-arriving data. Aggregate into Gold tables for dashboards and ML features. Use DLT for orchestration and data quality expectations at each layer. Monitor with Databricks Workflows alerts and the Spark Streaming UI. Set checkpointing at every stage so the pipeline can recover from failures without reprocessing.

How to prepare for a Databricks interview

• Run the platform hands-on. Spin up a Databricks Community Edition cluster. Build a DLT pipeline. Run `OPTIMIZE` and `VACUUM` on a Delta table. Read the Spark UI on a real job. There is no substitute for having done it.
• Practice explaining trade-offs out loud. "I'd use job clusters here because…" is worth more than a perfect definition of what a job cluster is.
• Know Unity Catalog. It comes up in senior-level rounds. Understand the governance model, access control hierarchy, and how it differs from workspace-scoped security.
• Prepare system design scenarios. A streaming ingestion pipeline with Medallion layers. A batch pipeline with SCD Type 2 handling. Be ready to draw the architecture and explain every choice.
• Review Workflows vs. Airflow. Know when Databricks-native orchestration is enough and when you'd bring in an external tool.

If you want to practice answering these questions under realistic conditions, Verve AI's Interview Copilot lets you run mock Databricks interviews with instant feedback on your answers — try it free at vervecopilot.com.

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These 30 questions cover the core of what Databricks interviews test in 2026. The pattern across all of them: interviewers want to hear how you think about trade-offs, not just what you know. Hands-on practice beats memorization every time. Run the platform, explain your reasoning out loud, and you'll walk in ready.

Verve AI's Mock Interview tool can help you rehearse these exact scenarios with real-time AI feedback — so the first time you answer under pressure isn't the interview itself.