Fireartistic Creative Agency

I help businesses in :
✴️Branding - Logo design & Brand Consultation
✴️Digital Marketing Plan to sky rocket your business
✴️Linkedin Organic Growth
✴️Content Creation for organic growth and boosting sales

Founder

Personal Branding | ✅9+ years of expertise in branding and logo designing | 🚀Dm now to elevate your branding game! | Help you to elevate your Linkedin Game

With over 9 years of experience as a Founder at Fireartistic Creative Agency and a LinkedIn growth specialist & personal branding consultant, I have helped more than 1,000 clients across various industries and niches to boost their online presence, generate more sales, and create unique brand identities. I have designed over 500 logos for both local and international brands, and I have a proven track record of increasing user retention, engagement, and conversions on LinkedIn.

My mission is to help founders, small businesses, and creators leverage the power of social media and digital marketing to grow their businesses and achieve their goals. I offer a range of services, including logo design and graphics, website design, social media marketing strategy, hourly consultation calls, and workshops for freelancers. I am passionate about creating high-quality content, delivering exceptional results, and building long-term relationships with my clients. If you are looking for a reliable, creative, and professional partner to take your business to the next level, feel free to message me or email me at rajatjain1107@gmail.com.

Rajat Jain

Ford Credit

● Led data modernization project with GCP for Europe region at Ford Credit, ensuring alignment with regulatory restrictions.
● Developed data ingestion, analytical domain, and data mart for presentation layer, enhancing data accessibility and insights.
● Implemented solutions to optimize data processing and storage, improving efficiency and scalability.

Senior Data Engineer

Just Eat Takeaway.com

● Developed and implemented a Security Data Insights Project on GCP, facilitating real-time queries and resulting in a 30% reduction in response time for security threat mitigation.
● Collaborated cross-functionally with InfoSec teams to architect a scalable data infrastructure, leading to a 50% increase in data processing efficiency.
● Orchestrated the delivery of a dynamic dashboard and automated ETL pipeline, optimising vulnerability management processes and achieving a 40% improvement in data accuracy

Data Engineer

bigspark

● Led a team of 5 to create an MVP for a Business-Screening Project, resulting in a 20% increase in client satisfaction and a 30% reduction in time-to-market.
● Managed a team of 6 and collaborated with 20 pipeline owner teams to migrate 140 pipelines from Kafka Cloudera to Kafka Confluent, achieving a 50% reduction in data processing costs.
● Designed and constructed an end-to-end ingestion and analytics platform in Microsoft Azure for a Real Estate client, resulting in a 25% increase in data accessibility and a 15% improvement in data analysis speed.

PayU

● Automated and fine-tuned a critical machine learning model using Python, AWS EMR, Apache Spark, and Apache Airflow, resulting in a 30% improvement in model accuracy and a 20% reduction in model training time.
● Streamlined data processing by developing efficient data loading and aggregation frameworks using Apache Airflow, leading to a 40% decrease in data processing errors.
● Revitalised data and pipeline architecture, resulting in a 60% increase in business productivity and a 25% reduction in infrastructure costs.

● Collaborated with clients to identify process improvements for consumer payment options, resulting in a 15% increase in transaction success rates and a $10,000 reduction in payment processing costs.
● Reduced payment processing failure rates, saving $10,000 in costs.
● Devised and executed solutions that streamlined workflows and maximised resource utilisation, resulting in a 20% increase in team efficiency.

Senior Data Analyst

● Achieved up to a 23% improvement in the success rate of statistical analysis models on large data sets, leading to more accurate business insights.
● Revamped reporting dashboards and planning tools, reducing data processing time by 95% and enabling faster decision-making.
● Systematized data pipelines, reducing processing time by 40% and enhancing data accessibility for stakeholders.

Data Analyst

SAMRAT FORGINGS LIMITED

● Devised and deployed a modernised payroll management system utilising SQL and PHP, resulting in a 20% reduction in processing time for the HR team.

Intern

Security Data Engineer @Just Eat Takeaway| Ex-bigspark, PayU| Mentor @ Learnbay| Women Health | Mental Health

As the Senior Data Engineer at Ford Credit, my role encompasses leveraging advanced cloud technologies to drive innovation and efficiency in data management. Harnessing a solid foundation from Dr. A.P.J. Abdul Kalam Technical University in Computer Science, I've translated academic concepts into tangible results, such as implementing a Security Data Insights Project that significantly reduced threat response times. 

My commitment extends beyond technical skills to championing diversity, equity, and inclusion in the tech industry. As a mentor and advocate for Women in Tech, we work towards creating inclusive environments where every voice is heard and valued. This holistic approach to technology and society underscores my role as a leader and change-maker in the realm of data engineering and FinTech.

I actively engage in thought leadership and speaking opportunities, focusing on how inclusive practices can drive innovation in tech. Whether through keynote speeches, panel discussions, or collaborative projects, I aim to forge impactful partnerships that align with my commitment to innovation, DEI, and wellness.

I am eager to collaborate with forward-thinking organizations that share my vision for a more inclusive tech industry. Let’s connect to explore how we can work together to foster meaningful change and drive success through collaborative efforts.

Skills & Expertise:
Technical Skills: Data Engineering, Information Security, Cloud Data Architecture (Azure, AWS, GCP), Kafka, Apache Airflow, Python, SQL
Advocacy & Leadership: DEI Advocacy, Women’s Health, Mental Health, Public Speaking, Mentorship, Technical Project Leadership

Disha Mukherjee

The more advanced your AI work becomes, 
The more security stops being optional.

As datasets grow more sensitive and models more powerful, where computation happens starts to matter as much as how it happens.

After years of building data systems in regulated and high-stakes environments, one pattern is consistent:

Mature teams do not choose between cloud or local compute.
They design for balance.

Here is why on-device AI workflows play a critical role in security and governance.

→ Dataset sensitivity
Not all data should travel. Customer data, internal signals, proprietary features, and regulated datasets introduce risk the moment they leave controlled environments. Local compute allows early experimentation, preprocessing, and validation without unnecessary exposure.

→ Governance starts before training
Most governance failures happen upstream. Feature leakage, undocumented transformations, and untracked data access rarely originate during model training. Running preprocessing and feature engineering locally makes lineage, ownership, and intent clearer from day one.

→ Reduced attack surface
Every cloud upload, credential, and integration expands the blast radius. On-device workflows reduce dependency chains during the most exploratory phases, when controls are often weakest.

→ Compliance without friction
Audits care about access, movement, and control. Local environments simplify this by limiting who can see what, when, and why. That clarity saves time when compliance questions inevitably arise.

→ Better engineering judgment
Experienced engineers know that speed without control creates future debt. Local compute allows fast iteration while keeping governance intact, instead of retrofitting controls after things break.

This is not about rejecting the cloud.
It is about using it intentionally.

On-device compute handles sensitive experimentation, early validation, and controlled pipelines.
Cloud compute handles scale, collaboration, and production workloads.

That balance is what sustainable AI systems are built on.

If you work on AI or data platforms, where do you currently draw the line between local and cloud compute?

Logan Lawler Cindy Olivo Crystal Cook

#DellProMax #DellTech #NVIDIA

After working across data engineering and ML systems for years, one thing has become very clear to me.
The right hardware does not just make things faster. It changes how you think, test, and build.

That is why the Dell Pro Max Plus 16 stands out for data engineers and AI or ML engineers who work end to end, not just at one layer of the stack.

Here is what makes the difference in real workflows.

Data engineering workloads rarely stay “just data engineering”
→ Ingestion turns into heavy preprocessing
→ Feature engineering grows more complex
→ Experiments start overlapping with model work
→ Pipelines need to behave closer to production
The Dell Pro Max handles this shift without forcing you to change how you work.

Local compute enables better pipeline design
→ Faster iteration on transformations
→ Honest debugging under realistic loads
→ Fewer shortcuts taken because something is “too slow to test locally”
This is where many pipeline issues surface early instead of in production.

The NVIDIA AI Platform matters more than people realise
It is not just about the GPU.
It is the combination of NVIDIA hardware and the AI software stack that enables efficient data processing, feature engineering, and model experimentation on the same system.
That full stack support is what makes local experimentation practical.

GPU acceleration is not only for training
→ Preprocessing large datasets
→ Feature generation at scale
→ Validation and profiling
→ Stress testing data logic
The NVIDIA RTX PRO 5000 Blackwell GPU gives enough headroom to treat these steps seriously instead of cutting corners.

One system that supports the entire workflow
→ Data ingestion and transformation
→ Feature engineering and validation
→ Model training and tuning
→ Debugging and experimentation
This reduces context switching between environments and keeps the feedback loop tight.

For data engineers moving closer to ML systems, and for ML engineers who care about data quality, this kind of setup matters.
It supports how modern systems are actually built, not how job descriptions describe them.

If you work across data pipelines and ML workflows, what part of your setup limits you the most today?

Logan Lawler Cindy Olivo Crystal Cook


#DellProMax #DellTech #NVIDIA

Airflow Is Overused.
Here’s the challenge: stop using it for everything, especially when a simple cron job would do the job better.

Somehow, Airflow became the default answer for every automation problem in data teams.
New pipeline? Airflow.
Small daily job? Airflow.
Move a CSV once a day? Airflow.
Trigger a script? Airflow.

But let’s be honest: most teams don’t need a DAG, a scheduler, a metadata DB, workers, retries, logs, plugins, and a full orchestration layer…
to run a 20-line Python script at midnight.

Airflow is powerfu, but it’s also heavy.
It introduces overhead, maintenance, infra cost, and operational complexity many teams aren’t ready for.

Here’s where Airflow is the wrong choice:
→ When your task has no dependencies
→ When you just need a daily/hourly schedule
→ When your pipeline has 2–3 steps max
→ When the environment is simple and the job is stable
→ When you want low cost, low noise, low overhead
→ When your team doesn’t have strong ops skills

Here’s where cron (or lightweight schedulers) win:
→ Simplicity
→ Speed
→ Zero infrastructure
→ No DAG orchestration needed
→ Easy debugging
→ Minimal failure points

Airflow is meant for orchestrating complex, interdependent workflows, not for replacing common sense.

So here’s the real challenge:
Before you create your next DAG, ask yourself 
Is this truly orchestration… or am I avoiding the elegance of simplicity?

Great engineers don’t choose the fanciest tool.
They choose the tool that makes the system easier, not harder.

Follow Disha Mukherjee For Data And More!

Airflow Is Overused.

Iteration speed is not about being faster.
It is about thinking better.

In AI engineering, the biggest gains rarely come from a single breakthrough.
They come from the quality of decisions you make across dozens of iterations.

And that quality depends heavily on how fast you can test, observe, and adjust.

What slow iteration really costs you
→ You settle for “good enough” architectures
→ You avoid testing edge cases because it is expensive
→ You skip ablation studies that would reveal hidden issues
→ You trust intuition over evidence because running one more experiment takes too long

Over time, these compromises compound.

What faster iteration changes
→ You explore multiple model and pipeline designs instead of locking into the first one
→ You notice data leakage and distribution shifts earlier
→ You understand why something works, not just that it works
→ You develop stronger system intuition through repeated feedback loops

This is where local compute changes the nature of the work.

Why local GPU matters in practice
→ No waiting in cloud queues
→ No cost anxiety tied to every experiment
→ No hesitation to rerun, compare, or stress test
With the Dell Pro Max Plus 16 and the NVIDIA AI Platform, experimentation becomes part of daily thinking, not a scheduled activity.

Better iteration leads to better architecture
When training cycles are short, you design with clarity.
You validate assumptions early.
You build systems that are explainable, repeatable, and resilient.

This is not about speed for its own sake.
It is about removing friction from learning.

And in AI engineering, learning speed often defines outcome quality.

If you reflect on your current workflow, what slows down your ability to experiment the most today?

Logan Lawler Cindy Olivo Crystal Cook


#DellProMax #DellTech #NVIDIA

Most AI efforts don’t fail because the strategy was wrong.

They fail because the systems underneath could not sustain it.

The roadmap looks ambitious.
The intent is clear.
The outcomes stall anyway.

Why?

Because AI does not operate in isolation.
It inherits the strengths and weaknesses of everything it touches.

A brittle pipeline becomes a brittle model.
A tightly coupled system becomes an unscalable experiment.
Undocumented workflows turn into hidden risks no model can reason around.

What separates teams that extract long-term value from AI from those that keep restarting?

They treat AI as a stress test for their foundations.

They ask:

→ Can our data evolve without breaking consumers?

→ Can interfaces absorb change without cascading failures?

→ Do teams actually know where responsibility starts and ends?

→ Does our infrastructure reward learning, not just launches?

When these questions are answered well, AI accelerates progress.

When they are ignored, AI simply exposes the cracks faster.

The real work isn’t pushing intelligence higher.

It’s reinforcing the layers that must carry it.

So before refining the strategy again, it’s worth reflecting:

Are you building for visibility,
or for durability?

#AIStrategy #EnterpriseAI #DataEngineering #DataFoundation #EnterpriseArchitecture #DigitalTransformation

Three Python libraries more engineers should know about (but often don’t)

Most Python projects rely on the same familiar stack.
But there are a few libraries that quietly solve very hard problems with far less code than you’d expect.

Here are three that consistently stand out.

→ QuantLib
An open-source library built for quantitative finance.

It handles complex financial mathematics that would otherwise take hundreds of lines of custom code.
Pricing models, interest rate curves, Monte Carlo simulations, all supported out of the box.

If you work anywhere near risk, pricing, or financial modeling, this one saves serious time.

→ PyFolio
Designed for analyzing and comparing investment strategies and portfolios.

Yes, it includes standard metrics like Sharpe ratio, alpha, and beta.
But where it really shines is visualization.

Comparing multiple strategies over time becomes intuitive, not painful.
If you think visually when reasoning about performance, this library makes analysis much clearer.

→ pmdarima (Auto-ARIMA / SARIMA)
Time series forecasting without manual model selection.

It automates ARIMA and SARIMA configuration, which makes it especially useful for beginners, but still valuable for experienced engineers when prototyping.

Works well for:
→ Stock prices
→ Crypto trends
→ Business metrics and demand forecasting

The takeaway:
Good libraries don’t just reduce code.
They reduce cognitive load and that’s often the bigger win.

What’s one Python library you’ve used that quietly changed how you work?

Comment "Library" for Links.

Follow and Repost to share with others.

Most AI systems don’t fail at scale. 
They fail during everyday workloads.

Most performance claims are made in isolation.
Synthetic datasets. Perfect conditions. One-off runs.

That’s rarely how data and AI systems behave in practice.

What actually matters is how a system performs when you run the same workloads you deal with every day.

Over the past weeks, I’ve been running real preprocessing and experimentation workloads locally on the Dell Pro Max Plus 16, powered by the NVIDIA AI platform, including the NVIDIA RTX PRO 5000 Blackwell GPU and the NVIDIA AI software stack.

Not lab benchmarks.
Actual engineering work.

Here’s what stood out.

→ Preprocessing large datasets
Parsing, validation, joins, and feature preparation are usually where pipelines slow down. Running these steps locally with GPU acceleration made iterative checks noticeably faster and more stable. More importantly, I could rerun experiments without hesitation, which changed how carefully I validated data assumptions.

→ Vector storage and embedding tests
Indexing and querying embeddings locally allowed me to test dimensionality choices, batch sizes, and update strategies without waiting on remote infrastructure. The system stayed responsive even as datasets grew, which made experimentation feel controlled instead of risky.

→ Training and fine-tuning cycles
Even short training runs benefit from predictable performance. What mattered here wasn’t peak speed, but consistency. The NVIDIA AI stack delivered stable behavior across repeated runs, which is critical when comparing results and understanding what actually changed.

→ System reliability under load
Running multiple workloads back to back, preprocessing, feature generation, and training, exposed where systems usually struggle. The Dell Pro Max handled this without the intermittent slowdowns or resource contention that often derail local experimentation.

The real takeaway for me wasn’t a single performance number.

It was confidence.

Confidence that results were reproducible.
Confidence that bottlenecks were real, not infrastructure noise.
Confidence that local experimentation reflected production behavior closely enough to trust decisions.

That’s what practical benchmarks should give you.

If you’re running AI workloads today, what do you trust more:
cloud benchmarks or results from your own real pipelines?

Logan Lawler Cindy Olivo Crystal Cook

#DellProMax #DellTech #NVIDIA

Running Airflow locally for a few days
feels very different from running it in production.

It’s painful.

Local Airflow is controlled.
Few DAGs.
Predictable schedules.
Clear ownership.

Production Airflow is a different system altogether.

Thousands of DAGs.
Multiple teams.
Mixed priorities.
Backfills colliding with SLAs.
Retries hiding deeper data issues.
And one small change triggering a chain reaction no one expected.

This is where many teams get surprised.

Airflow doesn’t fail because it’s a bad tool.
It struggles because we underestimate what production orchestration really means.

What changes at scale:

→ DAG count becomes a design constraint
→ Scheduler performance matters more than DAG logic
→ Ownership and isolation are no longer optional
→ Observability is as important as correctness
→ Governance decides whether things stay stable or slowly degrade

Running Airflow in production is not about writing DAGs.
It’s about operating a distributed system with human behavior baked in.

If your Airflow setup feels fragile, the issue is rarely the code.
It’s usually architecture, boundaries, or expectations.

Curious to hear from others who’ve been here.
What was the first thing that broke when your Airflow scaled?

If data were a product, 
Most teams would fail their first release.

We often talk about "data as a product" as if it is just another framework. 
In reality, it is a mindset shift.

When I first heard the term, 
I thought it was a buzzword.

Over time, I realized treating data like a product changes everything about how teams build, maintain, and communicate.
Here’s what that looks like in practice:

1. Ownership
In a real product, you know who is responsible.
In data, ownership often gets blurry.
When no one owns the table, everyone assumes someone else does, until something breaks.

2. Usability
A good product delights its users.
Most data assets are designed for the producers, not the consumers.
Documentation, discoverability, and feedback loops are not nice-to-haves; they are part of the experience.

3. Quality and Trust
Products have quality checks before they ship.
Data pipelines should too.
A schema change should feel like a version update, not a production bug.

4. Feedback
Real products evolve with user input.
Data should too.
The best data teams treat their stakeholders like customers: listening, iterating, improving.

"Data as a product" is not about tools or technology.
It is about empathy, accountability, and purpose.

Because at the end of the day, if your data does not solve a problem for someone, it is just another asset sitting in storage.

Takeaway:
Treat your data like a living product with ownership, feedback, and intent.

#DataEngineering #DataAsAProduct #DataCulture #DataEvangelism #ModernDataStack #LeadershipInTech

If data were a product.

Have you ever tried training a model locally and thought, 
“This will be quick”? 
And then ten minutes later you are staring at your screen thinking, 
“Why is the preprocessing step louder than the model?”

Anyone who has done real ML work knows the secret:
Local model training is not a neat, cinematic moment.
It is a full engineering workout.

Working with the Dell Pro Max Plus 16 has made that workout far more enjoyable, but the reality of the workflow is still honest.
Here is what local training actually looks like.

You discover bottlenecks you did not know you had
 → Your loader quietly becomes the villain
 → Your preprocessing decides to take a lunch break
 → Your feature pipeline shows you who is really in charge
The NVIDIA RTX PRO 5000 Blackwell GPU finally gives enough headroom for these truths to show up.

You adjust hyperparameters far more often than you admit
 → Batch size that “should work” does not
 → Learning rate needs a personality change
 → Model depth suddenly feels ambitious

The advantage of local GPU acceleration is simple.
You get to experiment before your coffee gets cold.

You start monitoring hardware like it is a stock market chart
VRAM usage spikes.
Data throughput dips.
CPU and GPU fight for attention.
Tools like nvidia-smi become a reflex.

This is where you realise model training is as much about engineering as it is about maths.

You meet every possible edge case
 → Strange samples
 → Schema surprises
 → Half-missing labels
 → Data that simply refuses to cooperate

 Running the entire pipeline locally reveals issues that distributed systems hide until it is too late.
You fail faster, which is a very good thing

That is the real power of local high-performance compute.
You learn quickly.
You fix quickly.
And your final model carries fewer surprises.

Local model training is not glamorous. 
It is messy, revealing, and incredibly educational.
And with the right hardware, it becomes something you can genuinely enjoy.

What about you?
What is the most unexpected challenge you have faced while training models locally?

Logan Lawler Cindy Olivo Crystal Cook

#DellProMax #DellTech #NVIDIA

7 IN-DEMAND DATA ANALYTICS SKILLS 🚀 

Want to thrive in data analytics? Master these key skills to stay ahead in the industry! 

📌 Sql – extract, manipulate, and manage data efficiently 
📌 Spreadsheets – excel & google sheets for data handling and analysis 
📌 Statistical programming – python, r, and sas for advanced analytics 
📌 Data visualization – tableau & power bi to present insights effectively 
📌 Machine learning – algorithms like linear regression, decision trees & random forest 
📌 Database management – mysql, postgresql, sql server for handling large datasets 
📌 Soft skills – communication, critical thinking & creativity for impactful storytelling 

Upgrade your skills and unlock new opportunities in data analytics! 📊✨ 

♻️ 𝙍𝙚𝙥𝙤𝙨𝙩 𝙩𝙝𝙞𝙨 𝙞𝙛 𝙮𝙤𝙪 𝙛𝙞𝙣𝙙 𝙞𝙩 𝙝𝙚𝙡𝙥𝙛𝙪𝙡.

Let’s talk about your career goals. Book a call with me today! Link is in the Comment below!👇🏻👇🏻👇🏻.



#dataanalytics #sql #machinelearning #datavisualization #careergrowth #datawithdisha

12 Essential Data Engineering Terms

In the ever-evolving world of data engineering, mastering key concepts is essential for building robust and scalable data systems. 

Whether you're extracting data, processing it in real-time, or ensuring its quality, understanding these terms forms the foundation for success.

From traditional ETL workflows to modern approaches like data mesh and streaming, each term carries unique significance in the data lifecycle. 

Here's a quick overview of 12 essential data engineering terms that every professional should know: 

1. ETL: Process of extracting, transforming, and loading data into target systems. 

2. Data Lake: Stores raw data in its native format for flexible processing. 

3. Data Warehouse: Centralized repository for structured and optimized data. 

4. Streaming: Real-time data processing for immediate insights. 

5. Pipeline: Automates data flow and transformation between systems. 

6. Batch Processing: Handles large volumes of data in periodic groups. 

7. Data Mart: Focused database for specific business functions. 

8. MPP: Efficiently processes data across multiple nodes. 

9. Data Mesh: Decentralized architecture treating data as a product. 

10. Data Quality: Ensures accuracy and reliability of data. 

11. CDC: Tracks and captures incremental database changes. 

12. Data Lineage: Tracks the origin and transformations of data. 

♻️ 𝙍𝙚𝙥𝙤𝙨𝙩 𝙩𝙝𝙞𝙨 𝙞𝙛 𝙮𝙤𝙪 𝙛𝙞𝙣𝙙 𝙞𝙩 𝙝𝙚𝙡𝙥𝙛𝙪𝙡.

Let’s talk about your career goals. Book a call with me today! Link is in the Comment below!👇🏻👇🏻👇🏻.

#dataengineering #etlprocess #datapipeline #bigdata #datamanagement #datawithdisha

AI won’t replace data engineers, but mediocre thinking will

Every few months someone confidently says that AI is about to automate the entire data engineering role.
But the reality is different.

AI can generate SQL.
AI can clean logs.
AI can suggest models.
AI can accelerate execution.

What AI cannot do is think.

It cannot understand why a business metric is broken.
It cannot decide the right trade off between cost and latency.
It cannot design a system that ages well under real world chaos.
It cannot ask the questions that shape the right solution.

The engineers who will thrive in an AI driven world are not the ones who know the most tools.
They are the ones who think clearly about problems.

They understand the domain.
They simplify the architecture.
They prioritise what matters.
They question assumptions.
They design for reliability, not just delivery.

AI amplifies this kind of thinking.
It makes good engineers faster.
But it also makes weak thinking visible very quickly.

If the foundation is unclear, AI only accelerates the confusion.

The future of data engineering is not about competing with AI.
It is about working alongside it with stronger judgement, sharper reasoning, and better questions.

AI will not replace data engineers.
But engineers who outsource their thinking will replace themselves.


Follow Disha Mukherjee for Data Made Easy.

Will AI Take Data Engineering Jobs

The Dell Pro Max Plus 16 has completely changed how I approach the early stages of AI development.

Not during training.
Much earlier.
Inside the data pipeline where the real work begins.

Most conversations around AI still focus on the model.
But anyone who has built systems at scale knows this truth:

Your model is only as strong as the pipeline that feeds it.
And your pipeline is only as strong as your ability to 
→ Experiment, 
→Validate, and 
→ Refine without friction.

With the NVIDIA RTX PRO 5000 Blackwell GPU, I can treat preprocessing and feature engineering with the same seriousness as model training.

Here is the workflow I follow.
Start with the raw data, not assumptions
 → Pull the source exactly as it exists
 → Keep every inconsistency, missing value, and edge case
 → Let the dataset tell you how the system behaves
With the NVIDIA RTX PRO 5000 Blackwell GPU, repeated exploratory passes across large datasets become practical instead of painful.

Iterate on transformations in tight loops
 → Test encoding strategies
 → Compare normalization choices
 → Experiment with aggregations and text cleaning
 → Validate statistical transforms
GPU acceleration makes these operations fast enough that iteration becomes a natural part of thinking, not a blocker.

Validate outputs as deeply as the model
 → Look for distribution shifts
 → Test edge cases
 → Simulate heavier loads locally
 → Compare intermediate states
Most ML failures start here, not in the models. Local GPU compute lets me surface these issues early.

Package features only when the data is stable
 → Reproducible
 → Documented
 → Aligned with production behavior
 Once this foundation is stable, training becomes straightforward instead of reactive.

What this setup gives me is clarity.
Faster feedback loops.
Cleaner datasets.
And fewer surprises downstream.

If you work on feature engineering, I am curious:
Which step in your preprocessing pipeline takes the most time right now?

Logan Lawler Cindy Olivo Crystal Cook

#DellProMax #DellTech #NVIDIA

Become a Top Data Engineer in 2025 & Dominate 2026!🚀

Want to build a high-paying career in Cloud Data Engineering? 

Here’s the roadmap suggested by Education Ellipse
Core Skills to Master
→ 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 & 𝐐𝐮𝐞𝐫𝐲𝐢𝐧𝐠: SQL / Python
→ 𝐂𝐥𝐨𝐮𝐝 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬: Azure / AWS basics
→ 𝐂𝐥𝐨𝐮𝐝 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: Azure / AWS Storage, Key Vault / KMS
→ 𝐄𝐓𝐋 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Azure Data Factory / AWS Glue
→ 𝐃𝐞𝐯𝐎𝐩𝐬: Azure DevOps / AWS CodePipeline
→ 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠: Synapse / Redshift / Snowflake
→ 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Databricks / Spark / PySpark
→ 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Delta Lake & Lakehouse
→ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐒𝐤𝐢𝐥𝐥𝐬: End-to-End Project Implementation
→ 𝐂𝐚𝐫𝐞𝐞𝐫 𝐏𝐫𝐞𝐩: Resume Building & Interview Preparation

Now, if you want to become a top Cloud Data Engineer in 2025, Education Ellipse is conducting 𝐅𝐑𝐄𝐄 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬𝐞𝐬 on Azure, AWS and Snowflake Data Engineering 🌩️

Topic - How to become Highest Paid Cloud Data Engineer in 2026

𝐉𝐨𝐢𝐧 𝐨𝐮𝐫 𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 𝐆𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬: 👉 https://lnkd.in/dUc4uHvP

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Date: 11th Dec 2025
Time: 7:00 PM IST
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Date: 12th Dec 2025
Time: 7:00 PM IST
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Most teams use the words data lineage, 
Data provenance, and data governance interchangeably.

But if you’ve spent enough years inside real data systems, 
You know they are not the same thing.

I learned this the hard way early in my career, when a single metric looked off and no one could tell whether the issue came from the source, a transformation, or a policy gap.

That incident shaped how I think about reliability today.
Here is the simplest way I explain the difference when I mentor younger engineers.

Lineage is the path.
Provenance is the origin.
Governance is the discipline that keeps both trustworthy.

Data lineage helps you trace how data moves through your systems. 
It shows each hop, each transformation, and every point where something could go wrong. 
When a pipeline breaks at 2 AM, lineage is the first place you look.

Data provenance gives you the historical context behind the data itself. Who created it. When it changed. 
How it evolved. 
In regulated environments, this is often more important than the pipeline logic.

Data governance defines the rules you operate on. 
Access, quality, retention, compliance, ownership.
Without governance, lineage and provenance are just documentation. 
With governance, they become part of how you manage risk and scale safely.

This is the maturity shift I see across teams.
Juniors think of data as tables and pipelines.

Seniors think of data as an evolving asset that needs traceability, context, and structure.

How does your organization treat these three today?

Follow Disha Mukherjee for data and more!

Data Lineage Vs Data Provenance Vs Data Governance

Most people think the gap between a junior and senior data engineer is about tools, years of experience, or certifications.
It’s not.
The real gap is in how they think.

A junior focuses on getting the pipeline to run.
A senior focuses on why the pipeline exists, how it behaves under stress, and whether it should even be built.

A junior looks for the “best tool.”
A senior evaluates trade-offs, maintenance cost, team skills, and long-term impact.

A junior thinks in steps.
A senior thinks in systems.

A junior talks about what they built.
A senior talks about the problem it solved, the risks it addressed, and the constraints it respected.

A junior waits for tasks.
A senior takes ownership of outcomes.

The shift is not technical.
It’s mindset, clarity, reasoning, and the ability to reduce complexity instead of adding to it.

If you want to grow into a senior role, start looking beyond the code and into the system around it.
That’s where the real engineering happens.

What’s one mindset shift that changed how you work?

Follow Disha Mukherjee for Data And More!

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The Battle

Rajat Jain

Disha Mukherjee

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taplio

The Battle

Rajat Jain

Disha Mukherjee

Followers

Posts

Engagements

Likes

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);}Disha Mukherjee's LinkedIn Performance

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Performance ScoreDisha Mukherjee's LinkedIn Performance