What belongs in a POC checklist for evaluating a Spark platform?

Summary

• Test your own workloads at production-like data volumes, not demos: representative batch jobs, SQL queries and streams.
• Check Spark compatibility first: the Apache Spark and Scala versions, ANSI SQL behavior, and any RDD or SparkContext code that Spark Connect clients can't run.
• Prove operations: failure diagnosis in the Spark UI and event logs, streaming restarts from checkpoints, and scaling up and back down.
• Verify your scheduler, BI tools, catalog and identity provider, and measure cost per workload on your own data.
• Write success criteria and a go/no-go rule before the POC starts.

What belongs in a POC checklist for evaluating a Spark platform?

A Spark platform POC should prove four things on your own workloads: your code runs with known changes, the platform is operable when jobs fail, it fits your tools and team, and cost grows acceptably with data. Use representative production jobs and queries rather than vendor demos. As the Spark tuning guide puts it, Spark programs "can be bottlenecked by any resource in the cluster: CPU, network bandwidth, or memory," and which one depends on your data and code.

Is your code compatible with the platform's Spark?

Item	What to check	How to test it
Spark version	Which Apache Spark release it runs (the newest upstream version is 4.2.0, as of October 2026)	Run your test suite on that version and read the release notes for every version you skip
Scala builds	"Spark 4 is pre-built with Scala 2.13, and support for Scala 2.12 has been officially dropped"	Rebuild JARs and dependencies for Scala 2.13 before testing on Spark 4
SQL behavior	spark.sql.ansi.enabled defaults to true, so invalid inputs throw runtime exceptions instead of returning null	Run production SQL and compare both results and errors
Client APIs	Spark Connect, added in Spark 3.4, doesn't support the RDD API or SparkContext	Find RDD and SparkContext calls in your code and test them on the platform's client path
Streaming	Structured Streaming "ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write-Ahead Logs"	Stop and restart a query from its checkpoint; check the sink for duplicates or gaps
Connectivity	Readers and writers for your storage, table formats and queues, such as Apache Kafka	Run end-to-end reads and writes on real data

Can you operate it when jobs fail or load spikes?

Item	What to check	How to test it
Monitoring	The Spark web UI and history server; upstream, viewing the UI after a job ends needs spark.eventLog.enabled set before it starts	Fail a job on purpose; find the failed stage, task logs and stack trace afterward
Scaling	Upstream dynamic allocation is "disabled by default" and needs spark.dynamicAllocation.enabled plus a shuffle option such as shuffle tracking or an external shuffle service	Run a bursty workload; confirm executors are added and released
Concurrency	How parallel jobs share resources, such as the FAIR scheduler (spark.scheduler.mode set to FAIR)	Run parallel queries for several users; record queue times
Recovery	How tables and metadata are backed up and restored	Delete a test table; time the restore against your RPO and RTO
Isolation	Separation of compute, data and permissions between teams	Give two teams separate access; confirm neither can read the other's data

Does it fit your tools and team?

Item	What to check	How to test it
Orchestration	Your scheduler, such as Apache Airflow or dbt Core	Trigger a Spark job from it; check logs and failure handling
BI and catalog	BI connectivity, lineage and access controls	Build a dashboard on a Spark table; verify lineage and grants end to end
Developer tools	Notebooks, IDEs and packaging for your languages	Write, debug and deploy one pipeline from your own IDE
Identity	SSO through OIDC or SAML	Sign in with company SSO; confirm role-based access holds
Docs and support	Accuracy of docs and quality of answers	Answer common questions from the docs; file a test support ticket

How do you judge cost and scale?

• Run the same workloads at increasing data volumes and plot cost per workload, in your own units.
• Confirm what's metered and whether you can set spending alerts or limits.
• Load-test concurrency with simulated users before projecting production cost.

What success criteria should you set?

Write these down before the POC starts:

• Performance and cost targets for each test workload.
• Integration targets: scheduler, BI and catalog working without custom glue code.
• A team target, such as each engineer shipping one test pipeline.
• A go/no-go rule that says which results lead to production, more testing or rejection.

How Databricks supports POC evaluation

Databricks offers a trial "designed for organizations and professionals evaluating Databricks for business use." Serverless compute is "a Databricks-managed service" for notebooks, jobs and Lakeflow Pipelines that lets you "run workloads without provisioning any compute resources in your cloud account." Billable usage is recorded in the system.billing.usage system table, which Databricks documents for monitoring costs. Databricks Connect, "built on open-source Spark Connect," runs code from your IDE against Databricks compute, and the Databricks Runtime release notes list the Apache Spark version in each runtime.

FAQs

How long should a POC take?

Long enough to run every test workload end to end, including a failure, a restart and a scaling test, and to review the results against your criteria. Fix the end date when you set the criteria.

Should we use production data?

Use a copy or a representative sample with production-like volumes and schemas, and leave production systems untouched.

What if the POC fails some criteria?

Rank the gaps. A missing IDE feature may be acceptable if performance and cost targets are met; no workable recovery plan is usually a blocker.