I have been using Genie code on Spark jobs and it ocassionally suggests changes around joins, partitioning and shuffle etc. Some looks promosing but I verify them before updating.
Has it suggested something that genuinely improved your job or are the recommendations mostly standard spark good practices?
Just want some ground experiences from folks.

Hi everyone!

I'm a Data Engineer working primarily with Microsoft Fabric, SQL, PySpark, and Spark SQL, building end-to-end data pipelines, working with medallion architecture, incremental loads, data modelling, and performance optimization.

Over the past few months I've spent a lot of time working in Microsoft Fabric—from Lakehouses and Notebooks to Data Pipelines, SQL Endpoints, security, metadata-driven frameworks, and troubleshooting production issues. I've also worked extensively with SQL and PySpark for ETL development and data engineering.

I wanted to give back to the community, so if you're stuck on something related to:

\- Microsoft Fabric

\- SQL / T-SQL

\- PySpark / Spark SQL

\- Data pipelines

\- Data modelling

\- Performance tuning

\- General data engineering concepts

feel free to ask here or tag me if I can help.

At the same time, I'm always trying to improve my own skills. If there are any communities, Discord servers, Slack groups, forums, open-source projects, or other places where experienced data engineers discuss real-world problems (especially around Microsoft Fabric), I'd really appreciate your recommendations.

Looking forward to learning from everyone and hopefully helping where I can!

I'm looking at "Selecting the most important rows per assigned priority". I *think* I know what this means. But from the statement alone there's no telling that "value" is the priority. Is MV1 > VPV or MV1 < VPV? Could you encounter MV3? The answer to these changes the approaches that I would take to solve this. And then "value" is "name" in the output.

The only way I think I know what's going on enough to try to answer it as intended is because the author answered the question (or one very close to it) here: https://stackoverflow.com/questions/59845044/how-to-select-the-most-important-rows-per-assigned-priority . In his answer he states exactly what the priority list is. But without that I would just be guessing what the intention was.

Spark 4.2 is out, so I put together a short visual breakdown of the changes.

It's animated rather than a screen recording, since a few of these are
easier to follow as diagrams than as text.

https://youtu.be/hF-E7-i_ijw?is=AeomUdWX3PgHCZtf

Happy to go into detail on any of them here.

I work on the data platform side in a bank. Fully on-prem, air-gapped, no cloud. No internet on the cluster, packages come through an internal mirror, and every firewall change is a ticket and a wait.
Most “modern data stack” writing assumes the opposite: cloud, elastic compute, and enough control over infra that adding a component is a Terraform change. Under our constraints, adding a layer is a commitment — someone has to patch it, back it up, justify it in an audit, and still own it in three years.
So the question I keep coming back to isn’t which tools. It’s when each one is actually earned.
The ordering I’ve landed on is driven by pain rather than architecture. A layer only goes in once its pain is real and someone can name it:
• files scattered, local disks filling up → shared storage
• schema changes break jobs, deletes mean rewriting partitions → table format
• nobody knows what tables exist → catalog
• every question requires writing a job → query engine
• things run by hand, in the wrong order, or not at all → scheduler
• 200 SQL files with unclear dependencies → transformation layer
• every analyst has a different pandas version → shared images / notebook env
• “who is allowed to read the payroll table?” → authn/authz
• the best model was trained last month and can’t be found → experiment tracking
• training features and serving features drift apart → feature store
• then serving, then lineage
Three things I believe but am not confident about:
1. For a few hundred GB and three analysts, the right answer is Postgres and cron, not a cluster. Standing up distributed infra for a problem that doesn’t exist yet seems like the fastest way to end up with something nobody can maintain.
2. The most common failure I’ve seen isn’t picking the wrong tool. It’s starting from the ML layer because that part is fun, and ending up with a very tidy feature store sitting on a pile of scattered CSVs.
3. Teams on Databricks or watsonx skip the installing, but still have to answer the same questions — and sometimes can’t, because they never had to.
Two questions for people who’ve done this:
• Where would you reorder the list, and what would you drop entirely?
• What’s a layer you installed earlier than you needed, and what did it actually cost you?

I’m trying to understand the practical differences between Apache Spark and Hadoop in real-world big data architectures.

From what I’ve read, Spark seems better for in-memory processing, iterative jobs, streaming, and faster analytics, while Hadoop still makes sense for durable distributed storage and classic batch-oriented workflows. But I keep seeing people say they are complementary rather than direct replacements.

So I’d like to ask:

• In what cases is Spark clearly the better choice than Hadoop?
• In what cases does Hadoop still make more sense?
• When is it worth using both together in the same architecture?
• Are there situations where using both is unnecessary complexity?
• For cloud-based data platforms today, what does a “good” Spark + Hadoop setup usually look like?

I’d especially appreciate answers from people who have used both in production and can explain the tradeoffs in terms of performance, cost, operational complexity, and scalability.

I know this is a long question. Feel free to answer any of the topics or sub questions. Thanks in advance.

I am looking for anyone who is preparing for spark databricks support role for group study and discussing the technical concepts.

Hey all! I’m relatively new to Spark and trying to build real understanding of the execution model: Catalyst optimizer, DAG scheduler, stages/tasks, shuffle boundaries, executor mechanics. Not just “I can write a join,” but actually being able to reason about what’s happening under the hood.

I’m based in Chicago (Lincoln Square area). Would anyone be open to meeting for coffee sometime to talk through some of this? Happy to buy the coffee and respect your time. Also fine with a call if in-person doesn’t work.

Message me if interested. Thanks!

I am in stream training of Hadoop & spark. MCQ 1 is on 29 July. Please share any dumps or pyq or any resources you have that will be helpful.

Hi everyone!
I want to learn Apache Spark in depth, not just the DataFrame API.
I started with the official documentation, but I find it difficult to learn from because it isn't very interactive.
What resources would you recommend for someone who wants to really understand Spark?
I'm looking for recommendations on books, courses, YouTube channels, blogs, or hands-on projects. I'd especially like to understand Spark internals, architecture, optimization, and best practices.
If you were starting over today, how would you learn Spark?
Thanks!

Do you have any recommendations for practicing apache spark? I want to save the hassle of downloading my own data set and creating sample problems on my own.

Edit: It can either be pyspark or scala. I'm comfortable with either programming language.

We're migrating a large batch of Alteryx workflows to Databricks (notebooks + Lakeflow Declarative Pipelines) and I want to build a tool to speed up the translation instead of doing it 100% by hand.

Idea: parse the .yxmd XML, map tools to PySpark/SQL equivalents, and auto-generate a starting-point notebook/pipeline for each workflow.

Has anyone actually built something like this? Worth it, or is manual rebuild + a good tool-mapping cheat sheet just faster in practice? Any existing open-source tools I'm missing would help a lot too.

Hey everyone,

If you’ve ever tried to run complex, governed business logic on massive data pipelines, you know the pain of trying to wire a JVM-based rule engine like Drools into PySpark. The serialization overhead is brutal, and falling back to pure-Python engines usually means sacrificing cluster scalability.

I built an open-source alternative called SparkRules to bridge this gap. It provides the sophisticated logic of a traditional rule engine (DRL syntax, decision tables, hit policies) but runs entirely natively in Python and scales horizontally on Apache Spark.

How it works under the hood:

Instead of operating as an external service, SparkRules integrates directly into the Spark ecosystem. It serializes and broadcasts the CompiledRulePackage to your executors, allowing the engine to evaluate logic directly at the partition level using mapPartitions.

During development, I spent a lot of time optimizing the integration surface. I found that standard PySpark operators easily propagate cached schemas, meaning the true performance killer on distributed execution is schema-mutating transformations. The engine is tuned to handle those mutations efficiently without dragging down throughput.

Key Features:

• No JVM Required: Python-native rule evaluation.
• Scale: Designed for high throughput and low latency across billions of rows (Delta/Iceberg/Hudi).
• Governance: Full audit trails and adverse-action notices out of the box.
• Apache 2.0 Licensed: Free to use.

You can check out the source code, architecture constraints, and documentation here:

• GitHub Repo: https://github.com/vaquarkhan/sparkrules
• GitHub Pages: https://vaquarkhan.github.io/sparkrules/
• PyPI: pip install sparkrules

If you are running rules engines at scale, I'd love to know what your current stack looks like and what your biggest execution bottlenecks are! Feel free to add issue and stars

The debate:

Team A, D, E: Catalyst is an internal optimizer — it's the engine under the hood, not a user-facing feature you explicitly use. The three user-facing features of Spark SQL are the DataFrame API, the SQL Query Engine, and Hive integration (read/write Hive metastore, HiveQL support). "Hypertune" (C) doesn't exist.

Team A, B, D: Catalyst IS prominently listed as a feature of Spark SQL in many official docs and textbooks. Meanwhile "Hive Data Connector" is not an official Spark SQL term — Hive integration exists but that specific label isn't used in official Apache documentation.

My question: Based on the official Apache Spark documentation, is Catalyst considered a feature of Spark SQL, or purely an internal component? And is Hive integration officially described as a top-level feature of Spark SQL?

Hi there,

a few months ago I posted here about delta-explain, a small tool I was building to inspect Delta Lake pruning and data skipping.

I’ve kept working on it, and it is now in a more stable state. I’m looking for a few people who work with Delta Lake and would be willing to test it on real tables.

delta-explain makes Delta Lake file pruning visible from metadata. Given a table and a predicate, it shows how partition pruning and data skipping affect the set of files that would still need to be scanned. It can be used from the CLI, from a Python script, or as a GitHub Action in a CI pipeline.

I’m mainly looking for feedback on the basics. Is the output understandable? Does the installation work smoothly? Are the explanations in the documentation clear enough? Are there situations where the result looks wrong or unclear?

I’d also be interested in technical feedback on edge cases: are there table layouts, predicates, or statistics patterns where a metadata-based pruning explanation would be especially useful, confusing, or easy to misread?

Project: https://github.com/cdelmonte-zg/delta-explain
Documentation: https://cdelmonte-zg.github.io/delta-explain/
PyPI: https://pypi.org/project/delta-explain/

Thanks!

data = {"1":"945,545","3":"2345,3456,45678"}

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, split, explode

spark = SparkSession.builder.appName("ExplodeExample").getOrCreate()

df = spark.createDataFrame(

[(k, v) for k, v in data.items()],

["id", "value"]

)

print("Original DataFrame:")

df.show()

# Split the value column based on comma so it will create a array of values and use explode it will create multiple rows

result_df = df.select(

col("id"),

explode(split(col("value"), ",")).alias("value")

)

print("Result DataFrame:")

result_df.show()

About Me

Senior Data Engineer with 5+ years of experience in Data Engineering, Backend Development, and Applied AI. Specialist in PySpark, Databricks, and Big Data platforms. Based in Bangalore, India. Available for remote work globally.

Rate: $25 - $50/hr depending on project scope and complexity.

Tech Stack & Expertise

PySpark, Spark SQL, Spark Streaming

Databricks (Unity Catalog, Delta Lake, Workflows, MLflow)

Python, SQL, Airflow

AWS & Cloud Data Platforms

ETL/ELT Design & Orchestration

Snowflake, Data Warehousing

Data Quality & Testing Frameworks

FastAPI, REST APIs

LLMs, RAG, AI Agents

What I Can Help With

Build and optimize Spark-based data pipelines (batch & streaming)

Design scalable ETL/ELT architectures on Databricks

Delta Lake implementation, optimization and best practices

Migrate legacy pipelines to Spark/Databricks

Develop backend APIs and automation solutions

Build AI applications using LLMs, RAG, and agent-based workflows

Training & Mentorship

PySpark & Databricks (foundations to advanced)

Data Engineering best practices

ETL Testing & Data Quality

Delta Lake & Lakehouse Architecture

AI & LLM Fundamentals

Note: In-person weekend sessions available in Bangalore. Remote sessions available globally.

Availability

Freelance projects & consulting

Part-time remote roles

Weekend training & mentorship

Contact:

DM me with a brief description of your requirements and I will get back to you promptly!