Databricks Interview Questions and Answers (2026)

Databricks interviews test whether you understand the lakehouse: Delta Lake, Apache Spark, clusters, notebooks, Unity Catalog, and the medallion architecture. This guide gives you the real questions with short, correct answers you can revise the night before — and when you want to rehearse them out loud, Book A Sloth lets you book a mock-interview slot with an expert in a single link.

Most loops move through a recruiter screen, a technical or SQL round, a Spark deep dive, and a behavioural round. The interview process typically runs across five or six stages over four to seven weeks — usually a 30-minute recruiter screen, two or three coding rounds, an onsite of four to five back-to-back interviews, and a hiring-manager round — so breadth matters as much as depth. When the recruiter's invite lands, here is how to acknowledge an interview email.

Databricks basics interview questions

Start here: interviewers open with what Databricks is and why the lakehouse exists. Databricks is a unified, cloud-based analytics platform built on Apache Spark that brings data engineering, data science, machine learning, and SQL analytics onto one platform.

**Q: What is Databricks?** A managed platform, founded in 2013 by the original authors of Apache Spark, that runs on AWS, Azure, and GCP. It provides an optimised Spark runtime, collaborative notebooks, managed clusters, and governance in one place, so teams do not have to stitch those pieces together themselves.

**Q: What is a data lakehouse?** An architecture that combines the low cost and flexibility of a data lake with the reliability and performance of a data warehouse. It lets you run BI and ML on the same copy of data, avoiding the old split between a lake for raw data and a warehouse for reporting.

**Q: What is the difference between Apache Spark and Databricks?** Spark is the open-source distributed compute engine. Databricks is the commercial platform around it — a tuned runtime, notebooks, cluster management, Delta Lake, and Unity Catalog. Spark is the engine; Databricks is the car built around it.

Apache Spark interview questions

Spark questions check whether you understand distributed processing, not just the syntax. The core idea: Spark splits work across a cluster and runs it in parallel using lazy evaluation, and by keeping data in memory it can run workloads up to 100 times faster than traditional MapReduce.

**Q: What is the difference between a transformation and an action?** Transformations (like `map`, `filter`, `select`) are lazy — they build a plan but do not run. Actions (like `count`, `collect`, `write`) trigger execution of that plan. Nothing computes until an action is called.

**Q: What are narrow and wide transformations?** Narrow transformations (`map`, `filter`) need data from only one partition, so no data moves between nodes. Wide transformations (`groupBy`, `join`, `distinct`) require a shuffle, moving data across the cluster — which is where most performance problems live.

**Q: RDD vs DataFrame vs Dataset — when do you use each?** In Databricks, reach for DataFrames by default. The Catalyst optimiser and Tungsten execution make them fast without hand-tuning.

Delta Lake interview questions

Delta Lake is the heart of most Databricks answers, so know it cold. Delta Lake is an open-source storage layer that adds ACID transactions, schema enforcement, and time travel to data stored in a data lake.

**Q: How does Delta Lake actually work?** It stores data as Parquet files plus a transaction log (the `_delta_log` folder). Every write is recorded as an atomic commit in the log, which is how Delta guarantees ACID transactions on top of cheap cloud object storage.

**Q: What is time travel?** Time travel lets you query an earlier version of a Delta table by version number or timestamp — for example, `SELECT * FROM table VERSION AS OF 5`. It is useful for audits, reproducing a report, or rolling back a bad write.

**Q: What do OPTIMIZE and Z-ORDER do?** `OPTIMIZE` compacts many small files into fewer large ones to speed up reads. `ZORDER BY` co-locates related data in the same files so Spark can skip files it does not need. Together they fix the "small files problem" and cut scan time.

**Q: What is Auto Loader?** Auto Loader (`cloudFiles`) incrementally and efficiently ingests new files from cloud storage as they arrive, tracking what it has already processed. It is the standard way to build a streaming ingestion layer without reprocessing everything.

Clusters and notebooks interview questions

Cluster questions test cost-awareness and how you run code day to day. Databricks clusters are managed groups of compute that run your Spark workloads, and choosing the right type controls both speed and spend.

**Q: All-purpose vs job clusters — what is the difference?** All-purpose clusters are interactive and shared, used for development and exploration in notebooks. Job clusters are created for a single automated job and terminate when it finishes. Job clusters are cheaper for production because they do not sit idle.

**Q: What is autoscaling?** Autoscaling adds or removes worker nodes based on load, so you pay for capacity only when the workload needs it. It is a default lever for controlling cost on variable pipelines.

**Q: How do notebooks support multiple languages?** A Databricks notebook can mix Python, SQL, Scala, and R using magic commands like `%sql` or `%python` per cell. This lets a team explore in SQL and productionise in Python in the same document.

Unity Catalog and governance questions

Unity Catalog questions have become standard, so do not skip them. Unity Catalog is Databricks' centralised governance layer for all data and AI assets across every workspace in an account.

**Q: What problem does Unity Catalog solve?** It replaces per-workspace, inconsistent permissions with one governance model. You get fine-grained access control (down to rows and columns), automatic data lineage, auditing, and a searchable data catalogue — managed once, applied everywhere.

**Q: Explain the three-level namespace.** Unity Catalog uses `catalog.schema.table`. The catalog is the top container, the schema (database) groups related tables, and the table holds the data. This structure is how it isolates and organises data across teams and environments.

**Q: Managed vs external tables?** For managed tables, Unity Catalog controls both the metadata and the underlying files, and dropping the table deletes the data. For external tables, it manages the metadata while the files live in a location you control, so dropping the table leaves the data in place.

Medallion architecture and pipelines

The medallion architecture is a near-guaranteed question, so have a crisp answer ready. It is a design pattern that organises data into three progressively refined layers — Bronze, Silver, and Gold.

• **Bronze** — raw data ingested as-is from source systems. No cleaning. Full history.
• **Silver** — cleaned, filtered, and conformed data, joined and deduplicated into usable tables.
• **Gold** — business-level aggregates ready for reporting, dashboards, and ML features.

Data flows Bronze to Silver to Gold, getting more trustworthy at each step.

**Q: What are Delta Live Tables (DLT)?** DLT is a declarative framework for building reliable pipelines. You define the transformations and the data-quality rules ("expectations"), and Databricks manages the dependencies, orchestration, and error handling. It differs from plain Jobs by being declarative rather than a set of manually ordered tasks.

**Q: What is the Photon engine?** Photon is a native vectorised query engine written in C++ that speeds up SQL and DataFrame workloads on Databricks. It runs the same code faster, with no query rewrite needed — you just enable it on the cluster.

Reading answers is not the same as saying them out loud

You can memorise every answer above and still freeze when an engineer asks, "So walk me through how you'd design the Silver layer." Interviews are won in the saying, not the reading. And if you have already cleared a loop like this, you can become a paid mentor for the next candidate.

The fix is reps. Practise these out loud, then do a mock round with an expert who has actually cleared a Databricks loop and can push back on a vague answer. A short message to line up that practice call is all it takes to start, and Book A Sloth makes booking that expert calm: one scheduling link, one payment, one slot on the calendar. The craft is yours; the machinery is ours.