NEW — Know Exactly What's Slowing You Down

Your Spark jobs are leaving performance on the table

AI scans your notebooks for config gaps, code anti-patterns, and Delta Lake issues. A Dual Microsoft MVP delivers the fix plan. One audit, one price.

Get Started — $3,500
10
Anti-Patterns Scanned
5
Score Dimensions
3
Workload Profiles
½ day
Delivery Time

What we audit

Five dimensions scored 0-100. Every finding comes with a specific fix, not just a warning.

⚙️

Spark Configuration

We compare your spark.conf.set() calls against Microsoft's recommended configs for write-heavy, balanced, and read-heavy workloads.

🔍

Code Quality

10 anti-pattern detectors scan every code cell: .collect(), blind repartitions, cross joins, schema inference, missing write modes, and more.

📊

Delta Lake Health

OPTIMIZE/VACUUM frequency, partition strategy, Z-Ordering on filtered columns, small file consolidation, and table statistics.

💰

Resource Efficiency

Idle Livy sessions, driver/executor memory sizing, pool type (Starter vs Workspace), and capacity utilization.

🏗️

Architecture

Medallion layer detection, error handling coverage, logging practices, Variable Library usage, and lakehouse binding.

Anti-patterns we catch

These are the performance killers hiding in your notebooks. We find every instance and tell you how to fix it.

HIGH .collect() on DataFrame

Pulls entire dataset to driver memory. Use .take(N) or .limit(N).toPandas() instead.

HIGH Cross Join

Cartesian products explode row counts. We verify intent and suggest bounded alternatives.

MEDIUM Python UDFs

Row-by-row Python execution kills parallelism. We identify replacements with built-in Spark SQL functions.

MEDIUM Schema Inference

inferSchema=True triggers an extra scan pass. We generate explicit StructType schemas from your data.

MEDIUM Hardcoded ABFSS Paths

Breaks across environments. We convert to 3-part naming or Variable Library parameterization.

MEDIUM Blind Repartition

Fixed partition counts waste resources. We recommend .coalesce() or adaptive execution instead.

+ 4 more patterns including .toPandas() on large frames, missing write modes, .cache() without reuse, and broadcast join opportunities

How it works

Three steps. Half a day. Clear action plan.

Connect

15-min call. We get read access to your workspace and understand which notebooks matter most.

Scan

AI reads every notebook via Fabric REST API. Inspects Spark configs, scans code cells, checks lakehouse structure.

Deliver

Scored report with prioritized fixes, recommended Spark configs per notebook, and a 2-hour walkthrough call with an MVP.

Simple pricing

Every notebook in your workspace, audited. One flat fee.

Spark Job Optimization Audit

$3,500 one-time
  • All notebooks in workspace scanned
  • Spark config audit per workload type
  • 10 code anti-pattern detectors
  • Delta Lake health check
  • Resource efficiency analysis
  • Architecture & best practices review
  • Scored report (0-100 across 5 dimensions)
  • Recommended Spark configs per notebook
  • Prioritized fix list with code examples
  • 2-hour walkthrough with MVP
  • 30-day support (1 follow-up call)

Bundle & Save

Semantic Model Audit ($2,500) + Spark Optimization ($3,500)

$5,000 save $1,000

Audit your Power BI models and Spark notebooks together.

Common questions

How many notebooks can you audit?
No limit. We scan every notebook in the workspace you point us at. For large workspaces (50+ notebooks), we prioritize by execution frequency and job duration.
Do you need write access to my workspace?
No — read-only access is sufficient for the audit. We use the Fabric REST API to read notebook content and Livy session metadata. We never modify your notebooks.
What if I'm using Databricks, not Fabric Spark?
This audit is specifically for Microsoft Fabric Spark (notebooks, Lakehouses, Livy sessions). If you're migrating from Databricks, we can assess your notebooks for Fabric compatibility as part of the engagement.
Will you actually fix the issues or just report them?
The audit includes a scored report with specific code fixes for every finding. During the 2-hour walkthrough, we implement the highest-impact fixes together. For larger remediation, we offer follow-up engagements.
What's the difference between this and the Semantic Model Audit?
The Semantic Model Audit ($2,500) focuses on Power BI datasets — DAX measures, relationships, storage. This Spark audit focuses on PySpark notebooks — code patterns, Spark configs, Delta Lake. Different layers of the stack, both important.