Databricks Scenario-Based Questions 2025

This article concerns real-time and knowledgeable Databricks Scenario-Based Questions 2025. It is drafted with the interview theme in mind to provide maximum support for your interview. Go through these Databricks Scenario-Based Questions 2025 to the end, as all scenarios have their importance and learning potential.

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Disclaimer:
These solutions are based on my experience and best effort. Actual results may vary depending on your setup. Codes may need some tweaking.

Question 1: How would you handle slow-performing ETL jobs caused by large joins in Databricks?

I’d first analyze the join plan to check for data skew or shuffle bottlenecks.
I’d prefer broadcast joins when one table is small enough to fit in memory.
I’d also try sorting or bucketing to reduce shuffle overhead.
Adaptive Query Execution (AQE) helps optimize joins dynamically at runtime.
Trade-off: broadcast joins help speed, but risk out-of-memory if not controlled.
In one project, repartitioning based on join keys reduced ETL job time by 60%.

Question 2: What’s your approach when schema evolution in Delta Lake breaks a downstream pipeline?

I’d trace the issue to identify which column change or addition caused the break.
I’d enable mergeSchema to allow safe evolution during writes.
For critical pipelines, I’d version schemas and validate in staging before prod.
Risk is high if downstream consumers aren’t schema-flexible, like BI tools.
Trade-off: too much flexibility may hide schema quality issues.
We avoided major failure once by setting alerts for schema drift before merges.

Question 3: How do you manage batch and streaming workloads on the same Databricks cluster?

I prefer separating critical streaming jobs from batch workloads using job clusters.
If shared, I assign workload tags and use cluster pools to prioritize.
Trade-off: isolated clusters add cost, but give stable SLAs.
In one project, real-time dashboards were delayed due to unoptimized shared clusters.
After moving streaming to its own cluster, latency dropped from 20s to 3s.
I also cap cluster size per job type to avoid hogging compute.

Question 4: How would you identify and prevent unexpected cost spikes in a Databricks environment?

I’d begin by checking for idle or long-running interactive clusters.
I’d implement auto-termination and enforce cluster pool usage.
Tagging clusters per team helps with cost attribution and accountability.
Trade-off: aggressive auto-termination can kill active dev sessions.
In our team, introducing 30-minute idle shutdown saved us $10K per quarter.
I also set up cost dashboards using Databricks REST API and alerts.

Question 5: What’s your solution when you observe severe data skew slowing down job execution?

I’d start with examining task runtimes to identify skewed partitions.
I’d apply key salting or custom partitioning logic to distribute data better.
Trade-off: salting complicates joins and post-processing logic.
In one case, a single customer ID had 80% of data — salting cut job time by 5x.
I also regularly monitor partition size histograms via Spark UI.
Lesson: catch skew in dev, not prod — logs don’t lie.

Question 6: How do you prevent data corruption when multiple jobs update a Delta table simultaneously?

I use Delta’s optimistic concurrency and enforce retries with exponential backoff.
I avoid overlapping writes on the same keys or partitions.
Trade-off: retries can increase latency or fail under high contention.
We once avoided serious corruption by wrapping merges in retry-safe workflows.
I also use orchestration tools like Jobs API to serialize critical operations.
For high-throughput updates, I batch inserts and use ACID merge strategies.

Question 7: When would you recommend using the Photon engine over standard Databricks runtime?

I’d suggest Photon for SQL-heavy analytics, especially large aggregations.
It offers vectorized execution and better CPU efficiency.
Trade-off: not all workloads benefit — UDFs or streaming don’t gain much.
On one dashboard workload, Photon reduced runtime from 35 mins to 9 mins.
I’d test Photon on a staging job first before adopting org-wide.
Photon is ideal when CPU bottlenecks, not IO, are your main issue.

Question 8: How do you ensure secure and scalable data governance across workspaces?

I’d use Unity Catalog to define centralized access controls and lineage tracking.
Trade-off: requires early planning around metastore and schema structuring.
We reduced data leak risk by switching from legacy ACLs to catalog-level permissions.
I also automate permission audits via Terraform or Databricks APIs.
One mistake to avoid: giving blanket schema-level access without field-level filters.
Unity Catalog really shines in multi-tenant or regulated setups.

Question 9: What’s the best way to manage notebook collaboration in large teams?

I always push for Git integration and PR-based notebook development.
Trade-off: Git adds process overhead, but improves accountability.
We established naming conventions and folder structures for smoother reviews.
Version control helped rollback a faulty model that otherwise would’ve gone to prod.
I also set up CI checks to lint notebooks for quality.
Lesson: shared notebooks are great for ideation, but Git is a must for prod workflows.

Question 10: How do you deal with frequent out-of-memory (OOM) issues in Spark jobs?

I look for broadcast joins and excessive caching — main culprits.
I adjust shuffle partitions and limit caching to key stages.
Trade-off: less caching means more recompute, but safer memory.
We halved OOMs by partitioning based on data volume, not default settings.
Also, I monitor “Spill” metrics in Spark UI to tune memory per executor.
If needed, I downsample or work with test data slices first.

Question 11: What would you do if your Spark job keeps failing due to driver memory issues?

I’d check if large collect or toPandas calls are overloading the driver.
Trade-off: keeping data in driver is fast for debugging, but crashes with big volumes.
We avoided this in one project by writing intermediate outputs to Delta instead.
Also, I increase driver memory only after eliminating inefficient code patterns.
Memory profiling tools like Ganglia or Spark UI help trace bloated operations.
Lesson: driver is for coordination, not data crunching — keep it lean.

Question 12: How would you approach a scenario where data engineers and data scientists are clashing over notebook workflows?

I’d push for clearer role-based environments — prod pipelines vs exploration.
Git branches with dev/prod segregation help avoid conflict.
Trade-off: data scientists want agility, engineers want control — balance is key.
In one case, we created “experimentation clusters” with looser governance.
Meanwhile, critical pipelines ran under stricter jobs framework.
Communication + branching strategy = peaceful coexistence.

Question 13: How do you handle performance issues caused by over-caching in Databricks?

I’d audit all .cache() and .persist() calls to identify misuse.
Over-caching fills up memory and triggers disk spills or job retries.
Trade-off: caching speeds up reuse, but not if it crashes the cluster.
In one use case, removing unused cached DataFrames cut job duration by 40%.
I set cache limits and educate devs on when caching is truly helpful.
Always profile before caching — not all stages are worth it.

Question 14: What would you do if Delta table reads suddenly slow down in production?

First, I’d check for file explosion or small file problems.
I’d consider optimizing using OPTIMIZE with ZORDER on high-read columns.
Trade-off: optimize adds cost but drastically improves read latency.
We once reduced report load time from 90s to 15s by ZORDERing on customer_id.
I also validate partition pruning is working as expected.
Read patterns change — tables need periodic optimization tuning.

Question 15: How would you decide when to use Databricks SQL vs Spark notebooks?

I use Databricks SQL for dashboards, BI reports, and analysts with SQL skills.
Spark notebooks are better for complex ETL, ML, and dynamic workflows.
Trade-off: SQL UI is fast but less flexible than programmatic notebooks.
In one project, migrating static reporting to Databricks SQL reduced notebook sprawl.
I also consider user type — SQL Analysts vs Engineers need different tools.
Right tool = right user = cleaner platform.

Question 16: What if your Delta Lake merge operation keeps getting slower over time?

I’d suspect growing data files and lack of vacuum or compaction.
Trade-off: frequent merges without file management increases job time.
I set up scheduled OPTIMIZE and VACUUM to maintain table health.
One project improved merge speed by 3× after compaction routines.
I also verify that merge keys are indexed or ZORDERed when needed.
Delta performance isn’t just about logic — physical layout matters.

Question 17: How do you decide between using Auto Loader vs traditional file ingestion?

I use Auto Loader when files land frequently and latency matters.
For one-time or batch-heavy loads, traditional copy jobs work fine.
Trade-off: Auto Loader is event-driven but adds orchestration overhead.
In one retail use case, Auto Loader reduced data lag from 30 mins to under 5.
It also handles schema evolution better for streaming sources.
Pick Auto Loader when freshness = business value.

Question 18: What steps do you take when multiple teams dump data into the same Lakehouse zone?

I enforce folder-level ownership and catalog access boundaries.
I tag data by producer team and set Unity Catalog policies per zone.
Trade-off: strong governance adds friction, but saves long-term chaos.
We had a case where overlapping writes caused inconsistent KPIs.
Since then, we moved to separate staging zones with CI triggers to prod.
Shared lake is good, but rules are better.

Question 19: What are the risks of not cleaning up old checkpoint data in structured streaming?

Checkpoint folders can grow endlessly, leading to slow restarts or failures.
Trade-off: long retention aids recovery, but bloats metadata and storage.
I set TTL policies or compact the metadata manually for long-running jobs.
We once hit 500k files in a checkpoint folder — restart time was 15 mins.
After cleaning up, latency dropped, and stability improved.
Lesson: stream state isn’t forever — manage it like temp files.

Question 20: How would you troubleshoot a Databricks Job that intermittently fails with no clear error?

I check for dependency drift — external libraries or data sources changing.
I also inspect logs from multiple attempts to spot non-deterministic behavior.
Trade-off: intermittent failures are hardest — you need multiple layers of logs.
In one case, a flaky API call caused 1/10 job retries — fixed with retry logic.
I add checkpointing and alerting to catch anomalies early.
Consistency = observability + validation.

Question 21: What would you do if a Databricks cluster keeps hitting autoscaling limits and jobs still lag?

I’d check whether autoscaling is actually hitting the right worker types or stuck.
I’d assess if the job logic is inefficient, causing artificial pressure.
Trade-off: scaling adds nodes, but bad code still drags everything.
In one project, setting max workers wasn’t enough — we rewrote joins to cut resource load.
I also review autoscaling logs and tweak job parallelism if needed.
Scaling works only if jobs are scale-friendly.

Question 22: How do you handle a stakeholder asking for near real-time data from a daily batch pipeline?

I evaluate latency need vs cost — is hourly mini-batch enough?
Trade-off: full real-time is expensive and complex for some use cases.
I often propose micro-batches using structured streaming with Auto Loader.
In a fintech use case, switching to 15-min batch gave 90% benefit with 30% of cost.
We added CDC flags and partial updates instead of full refreshes.
Always question the “real-time” ask — it’s often negotiable.

Question 23: What if you notice that a Delta table has grown to millions of files?

I’d first run DESCRIBE DETAIL to get file counts and table size.
I’d implement OPTIMIZE regularly to compact files and improve reads.
Trade-off: more frequent writes cause fragmentation, but delaying cleanup hurts performance.
We ran into 2-minute query latency — after compaction, it dropped to 15s.
File growth also bloats metadata; I monitor that separately.
Delta is powerful, but file hygiene is key.

Question 24: How do you approach setting up a cost-efficient dev environment in Databricks?

I use job clusters with auto-termination and smallest instance types.
I avoid interactive clusters unless needed, and apply cluster policies.
Trade-off: cheaper clusters mean longer startup or slower processing.
One dev team overspent $5K/month until we enforced idle limits and tagging.
I also disable photon and GPUs unless absolutely necessary in dev.
Cost-efficiency starts with access boundaries.

Question 25: What are the risks of relying solely on Delta Time Travel for data recovery?

Time Travel is great for short-term rollbacks but not a replacement for backups.
Trade-off: it increases storage cost based on retention duration.
In one case, a table had 30 days’ retention but rollback was needed for 45-day-old data.
We now use versioned exports to cloud storage for long-term safety.
I also educate teams: time travel ≠ archival strategy.
Use it wisely, not blindly.

Question 26: How would you handle a team using too many different cluster configs across jobs?

I’d analyze cluster usage patterns and group them by workload types.
Then, I define reusable cluster policies with limits and defaults.
Trade-off: some devs resist standardization — education is needed.
We saw a 40% cost drop just by enforcing two cluster templates org-wide.
I also review outlier clusters monthly and retire unused ones.
Simplicity scales — chaos costs.

Question 27: What if multiple teams are running overlapping pipelines writing to the same Delta table?

I introduce a governance layer using job orchestration and write isolation.
Trade-off: isolation adds complexity, but prevents overwrite conflicts.
In one setup, staggered write windows and merge conditions reduced collisions.
I also use column-based merge filters to avoid whole-table contention.
Versioning and lineage tracking via Unity Catalog help too.
Delta isn’t transactional across writers — plan for that.

Question 28: How do you handle a situation where users complain about slow dashboard refreshes from Databricks?

I check query plan for full table scans or missing ZORDER.
I also ensure dashboard uses cached or pre-aggregated tables.
Trade-off: pre-agg improves speed but adds ETL maintenance.
One team saw refresh drop from 3 mins to 12 seconds using materialized views.
I recommend not running heavy joins on live dashboards.
Dashboards need pre-digested data, not raw Delta reads.

Question 29: How would you decide whether to use Unity Catalog or legacy Hive metastore?

I use Unity Catalog for enterprise-grade governance and RBAC control.
Trade-off: it needs cleaner structure upfront, but scales better.
In regulated environments, Unity Catalog made audits and lineage trace much simpler.
Hive metastore might be faster for simple dev setups, but lacks fine control.
Once we migrated, cross-workspace data access was way easier.
Unity is future-proof — Hive is legacy.

Question 30: What would you do if your streaming job keeps falling behind during peak loads?

I look at processing rate vs input rate — that gap causes lag.
I scale cluster or increase micro-batch interval to absorb spikes.
Trade-off: larger batches reduce frequency but increase latency.
In one case, switching from 1-sec to 10-sec batches stabilized processing.
I also review schema inference, joins, and shuffles that slow down processing.
Streams need constant tuning — not fire-and-forget.

Question 31: How do you handle a business team asking for instant rollback after a bad data update in Delta Lake?

I use Delta’s time travel to rollback to a stable version instantly.
Trade-off: rollback fixes data but won’t undo downstream effects like reports.
In one incident, we restored a 2TB table to version N–1 within minutes.
I also add data quality checks to avoid such rollbacks in the first place.
Teams now validate in staging before touching prod.
Time travel is fast — but prevention is faster.

Question 32: What would you do if a stakeholder insists on using Excel with Databricks tables?

I recommend connecting Excel via Databricks SQL ODBC connector.
Trade-off: it works well, but not ideal for large datasets or real-time refreshes.
I also guide them on caching strategies to avoid heavy live queries.
In one case, we moved Excel reports to Power BI for better governance.
Educating stakeholders on limitations helps transition smoothly.
Meet them where they are, then guide them forward.

Question 33: How do you manage inconsistent data definitions across different Databricks teams?

I set up centralized metadata in Unity Catalog with standard naming conventions.
Trade-off: enforcing standards takes effort, but prevents chaos later.
We created a “data contract” model between producers and consumers.
Weekly schema reviews helped align teams and prevent overlaps.
I also lock down production schemas to prevent random changes.
Shared language = smooth delivery.

Question 34: What steps do you take if one long-running notebook affects overall cluster stability?

I first isolate it to its own job cluster if it’s resource-heavy.
Trade-off: isolation improves stability but increases cost.
I use cluster events to check memory leaks, retry patterns, or large shuffles.
In one case, disabling auto-cache and adjusting partitioning fixed the issue.
Long jobs need predictability — not shared runtime.
One bad notebook shouldn’t break everyone.

Question 35: What if your ML model training job on Databricks becomes inconsistent in results?

I check for randomness in data splits, feature transformations, or seed values.
Trade-off: consistent training needs deterministic logic, which can slow tests.
In one real case, inconsistent joins led to duplicate rows across model runs.
I fixed it by enforcing unique keys and setting random seeds across all stages.
Stable models = stable pipelines + stable data logic.
Training must be repeatable — not guesswork.

Question 36: How do you troubleshoot a sudden spike in job failure rate in Databricks?

I review cluster event logs, task failures, and dependency changes first.
Trade-off: quick fixes may mask deeper problems — I go for root cause.
We once traced failures to expired DB credentials used by JDBC connector.
I added secrets rotation via key vault and set pre-check hooks.
Automation helps, but alerting saves the day.
Patterns in failures point to weak links.

Question 37: What would you do if table reads are working fine but writes are consistently slow?

I check for small files accumulation, write amplification, or too many partitions.
Trade-off: faster reads can mean fragmented writes.
In one pipeline, switching to coalesce() before write improved job speed 3×.
I avoid dynamic partition overwrite unless absolutely needed.
Good writers don’t just read fast — they write smart too.
Tune writes like you tune reads.

Question 38: How do you handle a client pushing to migrate everything to Databricks in one go?

I propose a phased migration — start with ETL or BI layer first.
Trade-off: big bang = high risk; phased = stable, testable moves.
One migration began with daily batch jobs → streaming → ML.
We created compatibility layers to avoid downtime during transition.
Migrations fail when rushed — success comes in chunks.
Crawl → walk → run is the only way.

Question 39: How would you deal with a Databricks workspace that’s become unorganized and chaotic?

I’d start with catalog and workspace audits to identify orphaned assets.
Trade-off: cleanup takes time, but clutter kills productivity.
We implemented workspace naming rules, archiving policies, and access tiers.
In one cleanup, 400+ unused notebooks and 20 stale clusters were retired.
Clean data zones = clean mindsets.
Messy workspaces lead to messy decisions.

Question 40: What would you suggest if a pipeline constantly breaks due to external API rate limits?

I’d implement rate-limiting logic with retries, backoffs, and failover fallbacks.
Trade-off: retry adds latency, but prevents job failures.
In one fintech case, calling an API 1000× in parallel caused consistent 429s.
We grouped calls, added queueing, and reduced retries to only critical paths.
External APIs don’t care about your SLA — plan around that.
Respect the pipe, or it’ll choke you.

Question 41: How do you ensure data pipelines built on Databricks stay maintainable over time?

I follow modular pipeline design using jobs, not hardcoded notebooks.
Trade-off: more files to manage, but easier to update individual steps.
In one enterprise setup, we split logic into bronze, silver, and gold jobs.
We version each pipeline and run regression tests before promoting changes.
Maintainable pipelines = predictable output + reusable logic.
Future-proofing beats firefighting.

Question 42: What would you do if analysts complain about outdated or inconsistent datasets?

I introduce data freshness SLAs and update monitoring in all pipelines.
Trade-off: stricter SLAs need better infra and alerting.
We once added a “last updated” column and dashboard to every gold table.
I also educate analysts on upstream dependencies for better understanding.
Transparency in data builds trust — not just freshness.
What’s visible gets fixed.

Question 43: How do you handle a Databricks workspace used by both technical and non-technical users?

I separate environments — technical users get full access; analysts get SQL endpoints.
Trade-off: managing access roles adds overhead, but avoids accidents.
Unity Catalog helps define clear permissions per group or persona.
In one case, an analyst accidentally deleted a notebook in shared workspace — we learned fast.
Tools are for people — make them safe to use.
Access control is empowerment, not restriction.

Question 44: What if a job randomly fails on weekends but runs fine on weekdays?

I look for environment variables, schedule-based data drops, or weekend API outages.
Trade-off: weekend support often has blind spots.
In one retail client setup, API tokens expired over weekends due to misaligned cron jobs.
We fixed it with pre-run validation and health-checks.
Weekend errors often point to lazy assumptions in logic.
Schedule-aware design matters.

Question 45: How do you deal with a client requesting lineage tracking for every pipeline?

I integrate Unity Catalog’s built-in lineage with external metadata trackers.
Trade-off: detailed lineage adds overhead, but boosts trust and auditability.
In one audit, lineage reports helped explain KPIs back to source columns.
We also added metadata tags like owner, freshness, and sensitivity.
If data flows matter — track it like code.
What you can’t trace, you can’t trust.

Question 46: What would you do if your Delta table updates are too slow during peak business hours?

I avoid running heavy merges or upserts in real-time hours.
Trade-off: delaying updates ensures performance, but may add freshness lag.
In one case, we moved updates to off-peak hours and pre-aggregated results.
I also use change flags and partial updates instead of full table rewrites.
Smart timing saves compute and improves user experience.
Don’t compete with the business for compute.

Question 47: How would you manage platform-level changes without breaking running jobs?

I document platform updates and communicate change windows in advance.
Trade-off: controlled rollout takes time, but avoids chaos.
We maintain a staging workspace to test changes like runtime upgrades.
I also version dependencies using environment files or MLflow artifacts.
Change control is part of maturity — not just process.
Break less. Communicate more.

Question 48: What if your team has no visibility into job-level errors across projects?

I set up centralized logging with Databricks jobs API + webhook alerts.
Trade-off: centralization adds infra, but improves monitoring.
One team built a shared “job dashboard” for error tracking and retry history.
We tagged every job with owner, SLA, and purpose to filter fast.
Visibility is step one in reliability.
If it fails silently, it’ll fail forever.

Question 49: How do you plan for disaster recovery in a Databricks Lakehouse environment?

I back up Delta tables externally using versioned exports to blob or S3.
Trade-off: backups cost storage, but protect from corruption or loss.
We test DR by simulating region outage and full table restore quarterly.
I also version config files, notebooks, and catalog entries in Git.
DR isn’t optional — it’s insurance.
Plan for worst, deliver the best.

Question 50: How would you optimize Databricks jobs that work fine but take too long?

I profile job execution stages using Spark UI to find bottlenecks.
Trade-off: optimization can break working code if done blindly.
In one job, replacing nested loops with broadcast joins saved 80% run time.
I stagger workloads to avoid cluster saturation and enable AQE.
“Working” is not the same as “efficient”.
Never settle for “it runs”.

Question 51: How do you ensure consistent data quality across multiple Databricks pipelines?

I embed validation rules and null checks in bronze layer pipelines.
Trade-off: early validation slows ingestion, but saves debug time later.
We created reusable validation modules for every ingestion source.
I also track data quality metrics like completeness and uniqueness.
One pipeline flagged 30% duplicate rows early using quality thresholds.
Good data is built, not assumed.

Question 52: What would you do if your Delta merge jobs keep locking each other?

I stagger job schedules and avoid overlapping merges on same table.
Trade-off: sequencing delays freshness but ensures consistency.
We switched to partition-level merge logic to reduce lock contention.
Delta is atomic — not magic. You still need coordination.
In one finance project, job serialization stopped daily lock timeouts.
Time = locks. Control them.

Question 53: How do you help non-technical stakeholders understand the Lakehouse architecture?

I use visual diagrams showing bronze-silver-gold flow with real examples.
Trade-off: oversimplifying can miss important layers.
I connect concepts to business needs — raw → curated → analytics.
In one workshop, comparing Lakehouse to warehouse shelves clicked instantly.
Stakeholders don’t need tech — they need clarity.
Speak their language, not yours.

Question 54: What steps would you take when transitioning from a legacy Hadoop system to Databricks?

I start with assessment: workloads, formats, access patterns, and SLAs.
Trade-off: 1:1 migration is tempting but not ideal — rethink workflows.
We moved from Hive to Delta, and custom MapReduce to Spark SQL/MLlib.
I also replace schedulers with Databricks jobs or orchestration tools.
Migration = transformation, not just porting.
Leave legacy behind — don’t carry its weight.

Question 55: How would you handle excessive job retries that never actually fix the issue?

I review logs to spot root cause — retries often mask deeper problems.
Trade-off: too many retries cause delays and cloud costs.
One case involved schema drift — retries didn’t help until fixed upstream.
I cap retries and alert after a threshold breach.
Retry is a patch, not a cure.
If it fails thrice, fix it — don’t repeat it.

Question 56: What would you do if your organization starts scaling globally with multiple Databricks workspaces?

I define a central governance model using Unity Catalog across regions.
Trade-off: centralized control slows flexibility — balance is key.
We mirror critical tables using read-only catalogs and automate replication.
I also introduce naming and access conventions across workspaces.
Global scaling needs global rules.
Structure beats sprawl.

Question 57: How do you avoid overengineering simple data tasks in Databricks?

I ask: is Spark needed or would a simple script do?
Trade-off: Spark is powerful but overkill for small flat files.
In one scenario, using dbutils to move a file replaced 80 lines of Spark code.
I teach teams to think problem-first, not tool-first.
Simplicity isn’t laziness — it’s wisdom.
Don’t flex tech. Solve problems.

Question 58: What would you do if Databricks MLflow experiment tracking becomes cluttered?

I introduce naming conventions and tag metadata per run.
Trade-off: too many tags reduce clarity — choose wisely.
We archive stale runs monthly and clean up unused models.
One team had 1,200 models with no versioning — chaos until we set rules.
Tracking needs discipline, not just tooling.
Logs tell stories — make them readable.

Question 59: How do you handle a scenario where the client wants proof of Databricks ROI?

I showcase before-after metrics: job runtime, cost savings, latency reduction.
Trade-off: collecting ROI data needs planning from day one.
In a telco project, we showed $120K/year savings post migration from on-prem Hadoop.
I also quantify time saved in developer effort and pipeline failures.
Data wins arguments — track everything.
ROI isn’t assumed, it’s measured.

Question 60: What advice would you give to a team just starting with Databricks?

Start small — pick one pipeline, one use case, and do it right.
Avoid overloading with features — learn Delta, Spark, and jobs first.
Build governance and naming habits early.
Use notebooks for prototyping, but move to modular jobs quickly.
Embrace community and document what you learn.
Don’t aim to master it all — aim to master what matters.

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