Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

As an AI engineer, LLM Twin is an important architecture to understand.

An LLM Twin refers to a system where a language model is designed to represent the knowledge, writing patterns, and decision logic of a specific person, team, or organization. The goal is consistency and reliability, not novelty.

At its core, an LLM Twin is built around how information flows. Think of it as 4 steps:

1️⃣ First, content is collected from real sources: blogs, internal documents, posts, code repositories, or knowledge bases. This data reflects how the person or organization actually thinks and communicates.

2️⃣ Next, the data is structured for two purposes.
One path prepares instruction-style examples that help the model learn how to respond.
Another path prepares retrieval data so the system can pull relevant context at inference time. This ensures responses stay grounded in real material.

3️⃣ Then comes training and evaluation. Models are fine-tuned where appropriate, tested against clear criteria, and versioned before deployment. This step looks very similar to traditional ML workflows, just adapted for language models.

4️⃣ Finally, at inference time, user requests are combined with retrieved context and passed through the deployed model. Monitoring is critical here: quality, latency, and cost all need to be observed continuously.

Where is this used in practice?

✦ Personal or creator assistants that help scale writing and communication
✦ Company-wide assistants that maintain a consistent product or brand voice
✦ Internal tools that answer questions based on code, docs, or prior decisions
✦ Support systems that reflect how experienced engineers respond to issues

Why should AI Engineers understand this?
This architecture reflects how GenAI systems are actually built and maintained. It forces you to think beyond prompts and focus on data quality, retrieval design, evaluation, and long-term system behavior.

My 2 cents 🤌
This is a GREAT topic to build a portfolio project around!! 

Credit: LLM Engineer’s Handbook by Paul Iusztin  and Maxime Labonne. I really like how this book breaks down AI engineering clearly and goes deep into different GenAI system architectures without hand-waving.

Over the past few months, Arvind and I've delivered seven deep technical workshops across agentic AI, RAG, context engineering, AI evals, MCP, vibe coding, and more.

One theme kept coming up: you want something more structured. Not just standalone sessions, but a clear end-to-end roadmap from A to Z.

So that’s what we’re building.

A structured, comprehensive course for:
- Hands-on builders: software, AI, and ML engineers
- AI product managers
- Tech leaders and CXOs looking to upskill meaningfully

Before we finalize it, I’d love your input. I’ve created a short survey to understand where you are, what you want to learn, and your career goals.

It takes under 10 minutes.

As a thank you, you’ll receive an additional 10% discount when we launch the course soon 🫶

Survey: https://lnkd.in/gQYeBCRP

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Here’s my honest pattern with almost every AI tool I try.

I end up using a lot of tools, usually early access. After repeating this cycle enough times, I realized my reactions are almost predictable.

👉First hour
There’s real excitement.
I’m clicking around, testing odd prompts, trying things that used to fail. No strategy yet, just my curiosity and momentum.

👉Next few hours
I start comparing.
Where would this actually fit in my work?
What would I stop using if I kept this?
Is this clearly better, or just new?

👉Later that day
I experiment without guidance.
I skip the docs and examples on purpose. I try incomplete ideas, messy workflows, prompts I would never put in a demo. This tells me more than any tutorial ever could.

👉Day two
Then I look at the examples the team shared.
They usually work smoothly. They’re polished. They show the tool at its best, which is exactly what they’re meant to do.

👉After a few days
This is where the real understanding kicks in.
I start tweaking those examples. Changing assumptions. Using it in ways that feel realistic, not ideal. That’s when the boundaries show up. Not frustration. Just clarity.

Here’s the takeaway I’ve learned the hard way:
If you haven’t noticed limitations yet, you probably haven’t pushed the tool very far.

And honestly, if I use something consistently for three days and still can’t find a meaningful flaw, that’s rare. That earns real respect.
I haven’t experienced that yet.

Curious how others see this.
Are there tools you’ve used deeply where the limits still haven’t shown up?

Just a couple of weeks ago, Arvind and I hosted an in-person AI meetup with The Hub Bengaluru.

Over 300 people showed up in person.

Every single one of them was either already working in AI or actively trying to break into the space. The energy in the room was unreal. It genuinely felt like a room full of builders, practitioners, and people who are curious enough to go deep.

During the event, we rolled out a small challenge. We asked attendees to share a recap of what they learned, and we said we would pick one winner.

Really excited to share that Suraj V is that lucky winner 🎉
Suraj gets free access to all the The Gen Academy workshops we have hosted so far.

Huge congratulations, Suraj V!! 

Building The Gen Academy has always come from a very real observation that Arvind and I see every single day in our work. The adoption of generative AI and LLMs is moving incredibly fast, but the depth of AI engineering skills needed to build reliable systems is not keeping up.

There is a very real skill gap in AI engineering right now! There are so many curious souls who want to become AI engineers- but they aren't able to get those opportunities; and there are so many companies out there who  want to hire, but aren't finding the right talent. 

Our mission is to bridge that skill-gap!

As people who are deeply involved in this space, we felt a responsibility to help bridge that gap. Not just for people who are already experienced, but also for entry-level folks who want to build strong foundations the right way.

That is why our workshops are intentionally designed to be both intuitive and practical. We focus on theory that actually helps you reason about systems, and we pair that with hands-on demos where you build real things using modern toolkits and frameworks.

So far, we have hosted 6 workshops, and the response has been incredibly encouraging. We have trained 1500+ people already ❤️ 

Our next workshop is coming up on February 15th, and it will be focused on AI evaluations (link in comments)

Do follow The Gen Academy to stay updated on all our upcoming workshops.

Six months ago, Arvind and I started The Gen Academy with one simple goal- to create workshops that are deeply-technical, practical, cuts through the ton of noise out there, and that provides people clarity on building productive-grade AI systems.

So far we have trained 2100+ people. We’ve hosted 7 workshops, and consistently we’ve had 300–400 builders join us live.

The audience has been incredible.
- Software engineers moving into AI.
- AI engineers building LLM systems in production.
- Product leads thinking about reliability and evaluation.
- Directors and VPs from top tech and Fortune 500 companies who want to truly understand what’s under the hood.
- Even people from non-tech background who wanted to strengthen their technical fundamentals 

Recently, we hosted a workshop on AI Evaluations.

And this one felt especially important.

Because evaluating non-deterministic generative systems is fundamentally different from traditional ML evaluation.

We covered:

✦ How to evaluate non-deterministic generative AI systems
✦ How to think about benchmarks and what they actually tell you
✦ How to design your own evaluation dataset
✦ How to choose the right metrics for qualitative and system-level evaluation
✦ How to combine human evals, code-based evals, and LLM-as-a-judge
✦ A hands-on walkthrough so this isn’t just theory

If you’re building LLM applications, RAG pipelines, or agentic systems, evaluation is not optional. It is the difference between a demo and a reliable product.

If you missed the live session, we’ve shared the recording here: https://lnkd.in/gzMdbfWH

We’ll continue building these workshops for engineers who want to go beyond experimentation and into production.

More coming soon, so do follow us & The Gen Academy to get updates on our next workshops.

5 days to go! 
Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15.

It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. You’ll walk away having built and reasoned through real evals yourself. 

If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical.

Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle.

LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows.

Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain.

Why learning evals matters:
✦ Evals turn LLM development from guesswork into engineering
✦ They help catch regressions as models, prompts, or tools evolve
✦ They are essential for scaling RAG systems and agentic workflows
✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review

Evals are no longer optional. They are a core AI skill.

In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition.

This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace.

If you want to level up your AI skills for 2026, register here:
https://lnkd.in/dQSR7yhU

Evaluation Resources

Fine-tuning is going to be one of the biggest focus areas for AI engineering teams this year.

We’re past the stage where calling an API is enough. The teams creating real leverage are the ones adapting models to their own data, workflows, and edge cases. Out-of-the-box models are powerful, but they are generic. Fine-tuning is where enterprises unlock meaningful efficiency gains and build real differentiation.

If you want to upskill in AI engineering, this is a topic you cannot skip.
Arvind and I are hosting a deep, hands-on workshop on Fine-Tuning LLMs.

In 90 minutes, you’ll learn:
✦ When fine-tuning makes sense and when it does not
✦ How SFT, RLHF, and DPO fit into real-world pipelines
✦ An end-to-end, code-based demo from raw data to a specialized model
✦ How to evaluate true performance improvements and avoid production pitfalls

This session is designed for software engineers, AI engineers, and data scientists, and it is equally valuable for product managers, solutions architects, tech leads, and executives working closely with model ecosystems.

If you want to move from using LLMs to actually optimizing them, this workshop will give you a practical framework to do it: https://lnkd.in/g-5gHrsY

In 2026, AI isn’t judged by a wow factor anymore.
It’s judged by whether it actually improves your workflows.

One of the most common questions I get from founders and teams I advise is how to automate go-to-market and sales properly. Real GTM automation needs deep ICP research, clear definitions, personalized messaging, and credible sources. This isn’t a one-prompt problem.

Normally, building a solid GTM or sales report would take me several hours, even with existing AI tools helping with research.

I recently got early access to Airtable’s new Superagent, and what impressed me was the approach. It didn’t treat my request as a single task. It broke it into ~16 steps, ran multiple agents in parallel, did deep research per subtask, cited sources, and stitched everything together into one structured output called a Super Report.

I used it to build a technical, informational report on AI for GTM/Sales in minutes. No fluff. No hallucinations. Just deeply researched content I could actually use with business leaders.

If your work involves building reports, decks, or MVP websites that need accuracy and depth, this feels less like “AI that makes things pretty” and more like AI that does real work.

Do check it out here: https://lnkd.in/gDkfSSG2

PS: You can get 2 months free for @Superagent, using code: AISH2FREE

#SuperagentPartner, #ad, #SuperagentbyAirtable

I'm an Azure Data Engineer offering 8+ years of experience  with proven ability to deliver short or long-term projects in data engineering, data warehousing, machine learning, and business intelligence realm. My passion is to partner with my clients to deliver top-notch, scalable data solutions to provide immediate and lasting value. I have completed my engineering (B.Tech) from NIT RAIPUR.

I specialize in the following data solutions:
✔️ Builiding End to End ETL Pipeline Using Azure Cloud Tools.
✔️ Building the migration process from Hadoop cluster to Azure Databricks spark cluster
✔️ Building data warehouses using modern cloud platforms and technologies.
✔️ Creating and automating data pipelines, & ETL processes
✔️ Building highly intuitive, interactive dashboards.
✔️ Data Cleaning, Processing, and Machine Learning models.
✔️ Data strategy advisory & technology selection/recommendation

Technologies I most frequently work with are:
☁️ Cloud: Azure
☁️ Cloud Tools: Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Data Lake, Azure Analysis Service, Azure DevOps, Azure Key Vault, Azure Active Directory.
💬Language: SQL, Python, PySpark, SparkSQL, R, SAS, Dash.
👨‍💻 Databases: SQL Server, Azure Synapse, Azure SQL Database
⚙️ Data Integration/ETL: SAP HANA, Dyanmics 365, EPM Onyx, QAD
📊 BI/Visualization: PowerBI, Excel
🤖 Machine learning - Jupyter Notebook, Python, Pandas, Numpy, Statistics, Probablity.

Abhishek Agrawal

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣:

In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. 

Here’s a concise breakdown of key components across AWS, Azure, and GCP:

𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻:

AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows)
Azure: Event Hubs (real-time streaming), Data Factory (ETL)
GCP: Pub/Sub (real-time), Dataflow (batch & stream processing)

𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲:

AWS: S3 with Lake Formation for secure data lakes
Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse
GCP: Google Cloud Storage (GCS) with BigLake for unified data management

𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴

AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration)
Azure: Databricks (Spark-based analytics), HDInsight (Hadoop)
GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing)

𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴

AWS: Redshift – scalable, high-performance data warehousing
Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing
GCP: BigQuery – serverless, highly scalable, cost-effective analytics

𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻

AWS: QuickSight – scalable BI & reporting
Azure: Power BI – deeply integrated with Microsoft ecosystem
GCP: Looker – flexible data visualization & analytics

Each cloud provider has unique strengths. 

Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. 

Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs.

Image Credits : ByteByteGo 
Post Credits: Abhisek Sahu


Follow Abhishek Agrawal for updates on interview questions.
🔗 𝑱𝒐𝒊𝒏 𝑶𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝒂𝒑𝒑 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑷𝒍𝒂𝒕𝒇𝒐𝒓𝒎:  https://lnkd.in/dUuscrch  🚀

🚀 𝐘𝐨𝐮𝐫 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫🚀
𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫? 𝐇𝐞𝐫𝐞'𝐬 𝐲𝐨𝐮𝐫 𝐬𝐭𝐞𝐩-𝐛𝐲-𝐬𝐭𝐞𝐩 𝐠𝐮𝐢𝐝𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐧𝐚𝐯𝐢𝐠𝐚𝐭𝐞 𝐭𝐡𝐞 𝐩𝐚𝐭𝐡:
1) Master SQL Fundamentals 🛠️
2) Explore Azure Storage Solutions 🗄️
3) Get Hands-on with Azure Data Factory 🔧
4) Understand Azure DevOps Basics 🛠️
5) Learn Azure Key Vault for Secure Data 🔐
6) Grasp Data Warehousing Concepts 🏗️
7) Learn Python for Data Engineering 🐍
8) Dive into Databricks for Big Data 💻
9) Discover Delta Lake for Data Reliability 🌊
10) Master Apache Spark for Distributed Data Processing ⚡
11) Build End-to-End Projects 🔄
12) Polish Your Resume and Prepare for Interviews 🎯

Following these steps will help you succeed as a Data Engineer. Start your journey today! 💪

🔗𝑱𝒐𝒊𝒏 𝒐𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝑨𝒑𝒑 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒖𝒑𝒅𝒂𝒕𝒆𝒅 𝒐𝒏 𝑨𝒛𝒖𝒓𝒆 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝒊𝒏𝒔𝒊𝒈𝒉𝒕𝒔 𝒂𝒏𝒅 𝒐𝒑𝒑𝒐𝒓𝒕𝒖𝒏𝒊𝒕𝒊𝒆𝒔!
 https://lnkd.in/ew4mp24d 📲

𝗔𝗗𝗙 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗱𝗼𝗻’𝘁 𝘀𝗲𝗻𝗱 𝗮𝗹𝗲𝗿𝘁𝘀 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁.
𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗹𝗲𝗿𝘁𝘀, 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝘀𝘁𝗮𝘆 𝗵𝗶𝗱𝗱𝗲𝗻.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗽𝗲𝗿 𝗲𝗺𝗮𝗶𝗹 𝗻𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘂𝘀𝗶𝗻𝗴 𝗟𝗼𝗴𝗶𝗰 𝗔𝗽𝗽𝘀.

📄 𝗥𝗲𝗳𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝗣𝗗𝗙 𝗳𝗼𝗿 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲

Follow Abhishek Agrawal for updates on interview questions.
🔗 𝑱𝒐𝒊𝒏 𝑶𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝒂𝒑𝒑 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑷𝒍𝒂𝒕𝒇𝒐𝒓𝒎:  https://lnkd.in/dUuscrch  🚀

logic application abhishek

𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 𝘁𝗼 𝗙𝗮𝗯𝗿𝗶𝗰 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗹𝗶𝗳𝘁-𝗮𝗻𝗱-𝘀𝗵𝗶𝗳𝘁 𝗲𝘅𝗲𝗿𝗰𝗶𝘀𝗲.
It’s a chance to simplify governance, standardize integration patterns, and clean up years of pipeline debt.
Not every ADF pipeline maps 1:1. Custom logic, data flows, runtimes, and security all need a clear plan.
The teams that succeed don’t rush the migration — they inventory, prioritize, modernize, and move in phases.
Fabric rewards clean design. Migration is where that design finally gets enforced.

Follow Abhishek Agrawal for more such content.
🔗 𝑱𝒐𝒊𝒏 𝒐𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝑨𝒑𝒑 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒖𝒑𝒅𝒂𝒕𝒆𝒅 𝒐𝒏 𝑨𝒛𝒖𝒓𝒆 𝑫𝒂𝒕𝒂𝒃𝒓𝒊𝒄𝒌𝒔 𝒂𝒏𝒅 𝑺𝒑𝒂𝒓𝒌:
 https://lnkd.in/ew4mp24d

linkedin migration datafactory

🚀 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐒𝐩𝐚𝐫𝐤 𝐉𝐨𝐛𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬! 🚀
Struggling with Spark job failures? Learn how to:
 ✅ Use Spark UI for monitoring execution & performance
 ✅ Analyze logs to debug memory issues, slow shuffles & errors
 ✅ Enable structured logging for better troubleshooting
 ✅ Set up alerts to catch failures in real-time
🔗 Check out the full guide in the PDF!

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🔗 𝑱𝒐𝒊𝒏 𝒐𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝑨𝒑𝒑 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒖𝒑𝒅𝒂𝒕𝒆𝒅 𝒐𝒏 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈!
 https://lnkd.in/dUuscrch  📲

#Databricks #Spark #BigData #Azure #DataEngineering #Debugging

spark jobs databricks

🔹𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐰𝐢𝐭𝐡 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜🔹
Learn how to design and implement a complete data engineering pipeline in Microsoft Fabric—from data ingestion to visualization! 🚀

Download the PDF to find out how! 
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🔗 𝑱𝒐𝒊𝒏 𝒐𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝑨𝒑𝒑 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒖𝒑𝒅𝒂𝒕𝒆𝒅 𝒐𝒏 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈!
 https://lnkd.in/dUuscrch  📲

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

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