Business Development Manager @Hyperlink Infosystem | I help people to start & scale their digital business | 39k+ Followers | Influencer- Collaboration | Software Development Consultant | Personal Branding

I am a Co-Founder and Business Head with PixelSoft, a leading software development company that helps clients to start and scale their digital businesses. With over 7+ years of experience in the IT services industry, I am passionate about technology and innovation, and I enjoy creating solutions that meet the needs and goals of our customers and partners.

In my current role, I am responsible for expanding the company's presence into new markets, drafting business proposals on various portals, and managing projects in an efficient manner. I have also developed and maintained sustainable relationships with decision makers, negotiated commercial and license agreements, and arranged for software demonstrations and after-sale support.

Visit our website for more details: https://pixelsoft.tech/
Email: shreyansh@pixelsoft.tech

Shreyansh Shah

Shift your mindset from “I can’t” to “I will.”
From “someday” to “today.”

Change the way you think, change the way you speak — and watch how your life begins to align with the energy you create. 

#MindsetMatters #PersonalGrowth #Manifestation #PositiveThinking #LeadershipDevelopment

Driving Innovation and Excellence at PixelSoft | Co-Founder & Business Head | Transforming Ideas into Digital Success | Software Development Consultant | Influencer

There was a time when I used to hold on to every connection, every client, every “opportunity” — even the ones that drained my energy.

One day, during a project discussion, a client casually said, “We can easily find someone cheaper.”
I smiled, wrapped up the meeting, and later that evening I asked myself a simple question:

“If they can’t see my value, why am I still trying to prove it?”

That moment changed a lot for me.
I stopped chasing validation.
I stopped negotiating with people who only saw cost, not expertise.
I stopped shrinking myself just to fit into someone’s expectations.

And the funny part?
The moment you let go of people who don’t recognise your worth, you make space for those who genuinely do.

If you’re reading this — remember:
When you know your value, letting go becomes easy.
And growth becomes natural.


#growth #mindset #business 


#Follow Shreyansh Shah

The future of enterprise AI is here — and it’s simpler than you think.

Imagine building and deploying powerful AI agents without writing a single line of code.

Now imagine connecting them to your existing systems securely, at scale, and in record time.

That’s exactly what Airia - Enterprise AI Simplified makes possible.

 A unified, enterprise-grade AI orchestration platform that helps organizations go from idea to AI reality — faster than ever.

💡 Why teams are switching to Airia:

 ✅ No-Code Agent Builder — build AI agents visually, no coding needed.
 ✅ Pre-Built Data Connectors — plug into your enterprise systems instantly.
 ✅ Enterprise-Grade Security & Governance — compliance you can trust.
 ✅ Model Agnostic & Flexible Deployment — your AI, your rules.
 ✅ Intelligent AI Operations — optimize performance, costs, and uptime.

Whether you’re a startup or an enterprise — Airia empowers you to transform your workflows, automate processes, and scale AI adoption with confidence.

Check out More: https://tryit.cc/jHdVRI 

🔥 The next wave of business innovation is AI-driven — and Airia is leading it.

 Are you ready to orchestrate your enterprise intelligence?


#AI #EnterpriseAI #Automation #NoCode #Innovation #Airia #ArtificialIntelligence #DigitalTransformation

The mindset shift that separates good salespeople from great ones

Most salespeople spend their entire career trying to “close more deals.”

Great salespeople focus on something else entirely :
They don’t chase the deal… they chase the truth.

Here’s what I mean:

A good salesperson prepares their pitch.
A great one prepares their questions.

A good salesperson talks confidently.
A great one listens intentionally.

A good salesperson tries to impress the client.
A great one tries to understand the client.

Somewhere in the journey, you stop selling and start solving.
And that’s the moment everything changes like your conversions, your reputation, and your relationships.

The real shift?

👉 Good salespeople push. Great salespeople align.
👉 Good salespeople sell products. Great salespeople sell clarity.
👉 Good salespeople want a signature. Great salespeople want a fit.

When you stop trying to “win the client” and start trying to “help the client win,”
you stop being a salesperson…
you become a partner.

That’s the mindset shift the top 1% live by.

What’s one shift that transformed your sales approach? 👇

#Share your thoughts 💭 

#Follow Shreyansh Shah for more posts .

Sometimes the biggest shift in our career or business doesn’t come from changing strategies…


It comes from a changing perspective.

The people who grow the fastest aren’t the ones with perfect circumstances — they’re the ones who’ve trained their mind to spot opportunities where others see roadblocks.

When you treat your mind as your strongest tool, everything else falls into place.

What opportunity can you create today just by thinking differently?

#follow Shreyansh Shah for more posts..

A few years ago, I met a founder who told me something simple but powerful.

He said, “People chase success, but success is usually just the final chapter. The real story is written way before that.”

I didn’t fully get it then.
But over time, I saw how it plays out in business, fitness, learning, sales, everything.

It always starts with focus.
Just deciding what truly matters.

That focus slowly shapes discipline showing up even when it’s not exciting.
Discipline turns into habits, the small routines that keep us moving.
And when those habits stack up, they create consistency, the one ingredient every success story quietly depends on.

The founder was right.
Success isn’t a surprise. It’s a result.

One decision.
One routine.
One consistent day at a time.

If you’re working on something today, keep going the process is already shaping the outcome.

#like and #share your thoughts 💭 💭 

#Follow Shreyansh Shah for more posts...

AI just leveled up again.


Sundar Pichai just announced Gemini 3, Google’s most advanced model yet and it’s not just an upgrade, it’s a shift in how we interact with AI.

What stands out to me isn’t just the model’s power or reasoning depth… it’s how AI is finally starting to “understand context” It's not just what we say, but why we say it.

That’s a huge leap for builders, creators, and businesses.

For those of us in software development, this means:

Faster idea-to-execution cycles
Smarter AI copilots for development
More “human” understanding in tools we use daily
New opportunities for agentic apps that act, not just answer


The pace of innovation is unreal — in two years, we’ve gone from AI that reads text… to AI that reads intent.

Excited to see what Gemini 3 unlocks for developers, enterprises, and creators everywhere.

 The future isn’t just automated — it’s becoming intuitively intelligent.

#AI #Gemini3 #Google #Innovation #ArtificialIntelligence #TechTrends

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

Most people trying to become data engineers spend months watching tutorials but never build anything end to end.

The truth is simple. 
You can’t learn data engineering by memorising tools. You learn it by shipping real pipelines.

If you want to grow fast in this field, pick projects that force you to deal with actual engineering problems: 
Broken data, 
Schema changes, 
Orchestration issues, 
Cloud setups,
Streaming delays, and scaling bottlenecks.

Here are seven project ideas that give you the closest experience to what real teams face every day:

If you complete even three of these, you won’t just understand data engineering. You will have a portfolio that proves you can solve real problems, not just write code.

The fastest way to level up in this field is simple:
Build systems, not tutorials.

Follow Disha Mukherjee for more!

7 Projects to Master Data Engineering

Most engineers learn ACID in theory, 
But its importance becomes clear only when something breaks in production. 

Here is a simple reading-friendly explanation of each property.
1. Atomicity
A transaction is all or nothing. 
No partial writes. 
No half-completed updates. 
If one step fails, the entire transaction is rolled back and the system returns to a safe state.

2. Consistency
Every transaction must move the database from one valid state to another valid state. 
All rules, constraints and relationships must remain intact. 
This is different from the CAP theorem definition. 
Here, consistency is about preserving data integrity at every step.

3. Isolation
Multiple transactions often run at the same time. Isolation ensures they do not interfere with each other. 
The ideal model is serializability, where each transaction behaves as if it were running alone. 
In practice, databases use weaker isolation levels for performance, but the principle remains: data should not be confused about what happened first.

4. Durability
Once a transaction commits, it must remain stored. 
Even if the system crashes or a node fails. 
Modern systems achieve this through persistent logs, replication and stable storage.

Why ACID matters
ACID is not an academic concept. 
It is the reason money does not disappear mid-transfer. 
It is the reason orders are not duplicated. 
It is the reason data systems remain predictable and trustworthy at scale.

Image credit to ByteByteGo

Most people think learning 
Data engineering is about collecting tools.
 SQL. Airflow. Spark. Snowflake.

But the truth is, 
The tools are the easiest part.
What really shapes a great data engineer is how they think.

When I look back at my own journey, 
The biggest breakthroughs didn’t come from adding another technology to my stack. 

They came from understanding how data moves, 
Where it breaks, and why it matters to the business in the first place.

The first mindset shift is context. 
Before touching a line of code, ask why the data exists. 

Is it powering decisions, insights, or machine learning? 
That single question changes how you design everything downstream.

The second is systems thinking. 
Anyone can build an ETL job, but few can visualize how each component connects to deliver trust and consistency end-to-end. 
Think in flows, not fragments.

The third is adaptability. 
Data systems live in motion. 
What works today might not scale tomorrow. 

Design with change in mind, with observability, versioning, and lineage built in from day one.

And finally, communication. 
The most underrated skill in engineering. 
The ability to explain complex systems simply, to translate between technical and business worlds, is what turns good engineers into leaders.

If I were starting out today, 
I wouldn’t start with a list of tools.
I’d start with a mindset.

Because frameworks evolve, but fundamentals compound.
What’s the one mindset that shaped your growth the most as an engineer?

#DataEngineering #TechLeadership #DataEvangelism #EngineeringMindset #WomenInTech #CareerInData

The 4 Mindsets of a Great Data Engineers have.

Today is International Mens Day.
A day that often gets far less conversation than it deserves.

We talk a lot about creating supportive workplaces, stronger communities, and healthier teams. But we rarely talk about the pressures many men carry silently while trying to meet those expectations.

In tech especially, I have seen incredibly talented men struggle quietly with:
 • performance pressure
 • fear of being judged for vulnerability
 • imposter syndrome in fast moving environments
 • the expectation to always appear strong or certain
 • work life imbalance they feel they "should not" talk about

The reality is simple.
Support is not gender specific.
Respect is not gender specific.
And growth definitely is not gender specific.

International Mens Day is a reminder to create spaces where everyone feels comfortable asking for help, admitting they are overwhelmed, or simply being human.

Celebrating the men who show up every day.
Who lead with integrity.
Who mentor, contribute, and hold things together even when it’s not easy.

Let’s build a culture where strength is not defined by silence, but by honesty and collaboration.

It is because the success of one person should inspire you to the success of another person, when your personal success causes you pride. It is evidence that victory is not only a possibility but also it is occurring next to you.

When life gives you rewards to cheer, it is not hard.
But it is hard to really rejoice when life favors somebody.

We usually are ambitious, hustlers and goal-getters. However, here is the secret nobody tells about:

Those who glorify others most? They are the ones that tend to win in the long term.
It is not due to chance or karma, but due to pure happiness being magnetic.

It is the same chemicals that the brain is releasing when you win when you are happy when someone wins. The optimism you send comes back in the form of energy, confidence and connection.

Result: Your co-worker gets the promotion that you desired. You continue to celebrate with them, be part of their experience and feel proud that you are a part of their ride.

Example: Your friend gets to buy his dream car as you save money to buy your dream car. You party them regardless of this just because their milestone demonstrates to you that you too can.

The thing is that you will not always be right. Other people will score more points than you will — and that is all right.
Since success is not an exclusive thing. There is no finish line that can be crossed by one individual.

Make yourself around individuals that celebrate your victories regardless of whether it is their time or not.
And be the man that does the same.

That’s how everyone rises.

𝙍𝙚𝙥𝙤𝙨𝙩 𝙩𝙝𝙞𝙨 𝙞𝙛 𝙞𝙩 𝙧𝙚𝙨𝙤𝙣𝙖𝙩𝙚𝙨.

Let’s connect on scaling your career. Book a call with me today! Link in the first comment.

#growthmindset #wintogether #leadershipmindset #supportothers #positivity #datawithdisha

Everyone loves to talk about data science.
Few talk about what makes it possible in the first place.

Data science is the headline.
Data engineering is the story beneath it.
One builds models that predict the future.

The other builds systems that make those predictions possible.
I’ve often seen these roles confused or even compared, as if one is “smarter” or “more technical.”

But they serve different purposes, 
And the best teams know how to make them complement each other.
Data scientists explore, experiment, and extract meaning.

They ask why things happen and what might happen next.
Data engineers build reliable, scalable foundations.

They make sure data is accessible, accurate, 
And ready for those experiments to even exist.

In a healthy data ecosystem, science drives curiosity and engineering drives consistency.

You can’t move fast without both.
So instead of asking “Which is better?”
Ask “How do we make them work better together?”

Because great insights need great infrastructure.
And in the end, that’s what turns data from an experiment into impact.

What have you seen, more tension or teamwork between data engineers and data scientists in your org?

Follow Disha Mukherjee for more!

#DataEngineering #DataScience #DataEvangelism #TechLeadership #Analytics #WomenInTech

Data Engineering vs Data Science

What I would like to see more women do in tech do.

My entry into the tech world made me feel very special because I was among a small group of females in the room. In the majority of cases I remained silent, not because I did not have ideas, but I did not know whether I had a good one to be vocalized. With time, I came to understand one thing, which is that confidence is gained when you choose to speak, despite the fact that your voice is shaky at the start.

I hope that in the future I get to witness more women in tech doing just that, being vocal, taking up their space and believing in their capabilities. It might be a meeting when one has to present an idea, or it can be a new project where one has to be the leader or it can be a simple comment on the internet that people can make; each voice is important. Many a time, bright women tend to wait till they are ready. But preparedness is a myth - growth starts as soon as you take the step even though you are doubtful.

It would also be a dream to see more women taking sides with other women. Tech can even be competitive but when it works together it becomes different. The culture of power changes to that of survival when we share opportunities, mentor other people and even celebrate small wins together.

Lastly, I hope more women will rediscover what leadership in technology should be, not by title, but by example, empathy, creativity, and sincerity. Leadership does not involve being loud, however, it is more of a person who picks people up on the way.

To all women in the technology who read this: do not seek permission. Build, lead, fail, learn, and continue to appear. The industry does not simply require additional women, additional women who believe in themselves and each other.

𝙍𝙚𝙥𝙤𝙨𝙩 𝙩𝙝𝙞𝙨 𝙞𝙛 𝙞𝙩 𝙧𝙚𝙨𝙤𝙣𝙖𝙩𝙚𝙨.

Let’s connect on scaling your career. Book a call with me today! Link in the first comment.

#womenintech #womenleadership #careergrowth #diversityintech #empowerment #datawithdisha

No alternative text description for this image

Everyone wants to break into Data Engineering.
Few know where to actually start.

When I started, I made the same mistake most beginners do.
I tried to learn everything at once.
Hadoop, Spark, Kafka, Airflow, Snowflake, AWS, Terraform.

By week two, 
I had more browser tabs than actual understanding.

The truth?
You don’t need to learn all the tools.
You need to learn how data moves.

Start small.
Start simple.
Start right.

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐈 𝐰𝐢𝐬𝐡 𝐈 𝐡𝐚𝐝 𝐰𝐡𝐞𝐧 𝐈 𝐛𝐞𝐠𝐚𝐧 👇

↳𝐋𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐝𝐚𝐭𝐚 𝐟𝐥𝐨𝐰𝐬
Understand how data is collected, stored, transformed, and used.
Concepts matter more than tools.
If you can explain a pipeline on a whiteboard, the tools will follow easily.

↳𝐌𝐚𝐬𝐭𝐞𝐫 𝐨𝐧𝐞 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞
Pick Python or SQL.
Don’t chase syntax. Learn how to think in queries and scripts.

↳𝐆𝐞𝐭 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧
You don’t need a full-time job to start building.
Try projects like:
 • Building a mini ETL pipeline
 • Cleaning a dataset and publishing insights
 • Automating a small reporting workflow

↳𝐋𝐞𝐚𝐫𝐧 𝐭𝐨 𝐭𝐞𝐥𝐥 𝐭𝐡𝐞 𝐬𝐭𝐨𝐫𝐲
Data engineers who communicate well grow faster.
Document what you build. Write about it. Teach someone.
That’s how you go from engineer to trusted problem-solver.

↳𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬, 𝐧𝐨𝐭 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
Tech stacks change. 
Foundations don’t.
If you understand architecture, performance, and scalability, you’ll adapt to any tool in months.

You don’t get into data engineering.
You grow into it, one pipeline, one problem, one insight at a time.

If you’re starting your data engineering journey, what’s the one topic or challenge you’re stuck on right now?

Let’s talk in the comments.

Follow Disha Mukherjee fore Data Made Easy!

#DataEngineering #CareerGrowth #DataLearning #ETL #Python #SQL #WomenInTech

Ever opened a Data Engineer interview question sheet and thought-
 “Wait… they’re not just testing my tech skills, they’re testing how I think?”
You’re absolutely right. 

Here’s the truth:
Most data engineering interviews don’t just evaluate whether you can write SQL or build a data pipeline.

 They assess:
→ How you connect business problems to data systems
→ How you make design trade-offs
→ How you communicate your process

I’ve broken down the most common Data Engineer Interview Questions- not just with answers, but with the why behind them.

You’ll find:
 ↳ “General” questions that test your story and clarity
 ↳ “Process” questions that reveal your decision-making
 ↳ “Technical” questions that test depth, not just recall

Some highlights 
→ How to explain your “path to data engineering” without sounding like a
    CV
→ The right way to talk through a project from start to finish
→ What to say when they ask about schemas, unstructured data, and
   Hadoop (without jargon dumping)
→And the golden closer: how to ask them the right questions back.
Whether you’re just preparing for your first interview or aiming for a senior role, this guide will help you sound confident, structured, and human.

I’ve compiled the full breakdown (with examples, frameworks, and prep strategy) into a handy PDF:
 “The Ultimate Data Engineer Interview Prep Guide.”

#DataEngineering #CareerInData #TechInterviews #DataEngineer #SQL #WomenInTech #CareerGrowth

DE Interview Prep Guide.

Every modern data system begins with one simple question:
How will your data get in?

That’s where data ingestion comes in; 
The backbone of every pipeline.

It’s not glamorous, but it’s where scalability, reliability, and cost are decided long before any dashboard comes to life.
Most teams think ingestion is “just about connecting sources.”

In reality, it’s about handling variety, velocity, and volume; all at once.

Some of the tools leading this space:
→ Azure Event Hubs for high-speed event collection and real-time analytics
→ Google Cloud Pub/Sub for asynchronous messaging and streaming ingestion
→ Apache Kafka for large-scale, low-latency event pipelines
→ Apache Flume for distributed log and event collection

Each serves the same purpose; moving data, but each does it differently, depending on your architecture and scale.

Here’s the real challenge:
Picking the right tool is not about brand preference.
It’s about understanding your data flow, latency needs, and consumption pattern.

Because when ingestion goes wrong, everything downstream suffers; from processing to analytics to trust.

If you’re building or optimizing data pipelines right now, which ingestion tool have you found most reliable in production?

Follow Disha Mukherjeefor Data Made Easy!

#DataEngineering #StreamingData #SystemDesign #Kafka #CloudComputing #ETL #DataPipeline

Image Credit Simform.

Good data products don’t start with SQL. 
They start with questions.

I’ve seen teams jump straight into models, schemas, and pipelines because the requirement looked “simple.”

 A new metric.
 A new dashboard.
 A new table.

But every time a data project struggles, the root cause is almost always the same:
No one slowed down to ask the right questions.

Before a single line of SQL is written, great data teams ask:
→What problem are we trying to solve?
 Not “what table should we build,” but “what decision will this enable?”

→ Who will use this?
 A data scientist, a product manager, a finance analyst — each needs a different kind of truth.

→ What does ‘correct’ look like?
 If the business doesn’t know the definition, the data team will guess, and that guess becomes debt.

→ What could go wrong later?
 A quiet discussion today prevents a loud incident tomorrow.

Once the questions are clear, SQL becomes the easy part.
The complexity drops.
The ownership becomes obvious.
The data product actually solves something.
Good data engineering is not about writing queries faster.
It’s about creating clarity before creating tables.

Takeaway:
 If you want better data products, start with alignment. The code follows naturally.

Follow Disha Mukherjee fore data made easy!

I am excited to announce that 
I have become a Dell Pro Max Ambassador.

As someone who has spent more than a decade building data systems and, more recently, working toward full-stack AI development, access to reliable high-performance compute has become a critical part of my workflow. 

Training models, running iterative experiments, and evaluating data-intensive pipelines require both speed and stability at the hardware and software level.

I have received the Dell Pro Max Plus 16, a system designed for professional-grade workloads. It is equipped with the NVIDIA RTX 5000 GPU built on the Blackwell architecture with 24 GB of GPU memory. 

This GPU is part of the NVIDIA AI Platform, which combines accelerated hardware with the NVIDIA AI software stack to support modern AI, simulation, and data workflows end to end.

Along with the system, 
I will be using the Dell UltraSharp Webcam, 
the Dell Pro Plus Compact Keyboard and Mouse (KM7120W), 
and Dell headphones to create detailed, workflow-driven content for the community.

Over the next few weeks, I will share practical, hands-on experiences around:
• Building and optimizing data pipelines
 • Training and fine-tuning models using local accelerated compute
 • Evaluating performance gains and iteration speed
 • Demonstrating how the NVIDIA AI Platform supports real AI development work
 • Offering guidance that other engineers and aspiring practitioners can apply directly

My goal is to produce content that is honest, useful, and grounded in real engineering practice, not theory.

Thank you to Dell Technologies for the opportunity to contribute to the program. 

More updates soon.

Logan Lawler Cindy Olivo Crystal Cook

 #DellProMax #DellTech #NVIDIA

The dev tool landscape is about to get a massive shake-up in 2025. 

AI-first platforms, cloud-native workflows, and integrated developer ecosystems are rewriting the rulebook and not every tool will survive.

From outdated CI/CD systems to static docs, the tools that once led innovation may soon become relics of the past.

Here are the Dev Tools That Won’t Survive 2025 and what’s replacing them.
Which one do you think will disappear first? 👇

𝙍𝙚𝙥𝙤𝙨𝙩 𝙩𝙝𝙞𝙨 𝙞𝙛 𝙞𝙩 𝙧𝙚𝙨𝙤𝙣𝙖𝙩𝙚𝙨.

Let’s connect on scaling your career. Book a call with me today! Link in the first comment.

#devtools #softwaredevelopment #ai #techtrends #developers #datawithdisha

Predictions 2025 The Dev Tools that won’t survive this yea

If you want to build a strong career in tech, especially Abroad, 
Focus on three things:
→ Personal branding.
→ Networking.
→ And asking the right questions.

Your personal brand helps people find you.
Your network helps people trust you.
And your questions help people remember you.

Too many skilled engineers stay invisible because they never show what they know.
Too many ambitious professionals connect without context, adding people, not building relationships.

Start small.
Share your perspective on the problems you solve.
Engage with others’ posts thoughtfully.

Reach out with intent- not to ask for a job, but to learn.
One thoughtful conversation can open more doors than ten cold applications.

And one consistent voice online can build more credibility than years of quiet expertise.
Your skills are your foundation.

Your brand and network are your amplifier.

#CareerInTech #DataEngineering #PersonalBrand #Networking #TechLeadership #UKTech #DataEvangelism

The cost of not deleting anything: data debt

Most data teams think their problems come from complex pipelines or slow queries.
But a huge part of the struggle comes from something much simpler.
Not deleting things.

Old tables that no one owns.
Metrics created for projects that ended long back.
Pipelines that still run even though no one knows why.
Dashboards that have not been opened in months.

All of this becomes data debt.
And data debt grows quietly.

You feel it during migrations.
Every change takes longer because you need to check what might break.
You feel it during outages.
Debugging becomes harder because no one knows which assets matter.
You feel it during onboarding.
New engineers spend more time decoding history than creating impact.

Most data debt starts with good intentions.
A quick table.
A temporary metric.
A one time pipeline.
The team moves fast.
But nothing gets retired.

The fix is not a one time cleanup.
The fix is a habit.
Review. Remove. Archive. Repeat.

If your data stack feels slow, cluttered, or risky, you may not need more tools.
You may need fewer unused assets.

Delete what no longer serves the work.
Your future team will thank you.

Most people prepare for Data Engineering interviews 
By memorizing answers.
But interviews aren’t about reciting definitions.

They’re about showing how you think, how you solve problems, and how well you understand the flow of data.

If you’re preparing for DE roles in 2025, 
There are a few patterns you’ll notice.

Companies want engineers who can design systems, 
Reason about trade-offs, and communicate clearly.

Tools matter, 
But clarity matters more.

I spent some time reviewing the most common interview questions asked across companies.

The themes are consistent: data modeling, pipeline design, orchestration, Python, SQL, scaling decisions, and how you collaborate across teams.

 And almost every interview includes one deeper question:
 “Walk me through a data project you designed end to end.”
Most candidates fail here because they talk about tools instead of the problem they solved.

If you want to stand out, focus on three things:
→Show how you think.
→Break down a problem. 
→Explain the constraints. 
→Share why you chose one approach over another.
→Show how data moves.

Interviewers want to see how you design reliable pipelines, ensure data quality, and handle failure scenarios.

Show your communication muscles.
A lot of data engineering is working with analysts, PMs, ML teams and explaining trade-offs in plain English.

To make preparation easier, Use interview prep guide covering the most asked DE questions:
HR screen, technical deep dives, SQL, Python, orchestration, big data, and system design.

What interview topic do you find the most challenging right now? 
Let’s discuss in the comments.

Follow Disha Mukherjee for Data Made Easy!

Data Engineering Interview Prep.

When I first started using dbt Labs, it felt like just another transformation tool.
Write SQL, run models, build tables- simple enough.

But over time, I realized something deeper.
dbt wasn’t just about transformations.
It was teaching data teams how to think like product engineers.

Here’s how:
1. 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐚𝐬 𝐚 𝐌𝐢𝐧𝐝𝐬𝐞𝐭
 Every model lives in Git. Every change is reviewed.
 That single shift turns data from a “shared folder of tables” into a product
 with lifecycle management.

2. 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐬 𝐚 𝐅𝐞𝐚𝐭𝐮𝐫𝐞, 𝐍𝐨𝐭 𝐚𝐧 𝐀𝐟𝐭𝐞𝐫𝐭𝐡𝐨𝐮𝐠𝐡𝐭
 In dbt, tests aren’t optional.
 They’re part of the build.
 Just like a product, your data ships only if it passes quality gates.

3. 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐬 𝐚 𝐅𝐢𝐫𝐬𝐭-𝐂𝐥𝐚𝐬𝐬 𝐂𝐢𝐭𝐢𝐳𝐞𝐧
 Auto-generated docs may sound simple, but they redefine ownership.
 When every model is documented in context, data stops being tribal 
 knowledge.

4. 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲 𝐚𝐬 𝐭𝐡𝐞 𝐃𝐞𝐟𝐚𝐮𝐥𝐭
 With lineage, every dependency is visible.
 Suddenly, consumers can see how data is built, not just trust that it works.

This is what “data as a product” looks like in action:
↳Ownership
↳Quality
↳Discoverability
↳Feedback

Tools like dbt Labs don’t just change how we engineer data.
They change how we think about data.
And that mindset shift is what modern data teams are built on.

Takeaway:
The best data tools don’t just solve technical problems, they shape better engineering behavior.

#DataEngineering #dbt #DataAsAProduct #ModernDataStack #DataCulture #DataEvangelism

When a dashboard breaks, the first question people ask is,
 “Who wrote this?”

But the better question is,
 “Who owns this?”

I’ve seen technically brilliant data projects collapse, 
not because the pipelines were fragile,
but because no one owned the truth behind the data.

Ownership in data isn’t about hierarchy.
It’s about accountability.
↳Who decides when a table is deprecated?
↳Who defines what “active user” means?
↳Who ensures the data still makes sense a month later?

When everyone assumes “someone else” owns it, the data slowly loses trust.
And once trust is gone, even perfect code can’t save the project.

The best data teams I’ve worked with treat ownership like a design principle
clearly defined, versioned, and communicated.

Because good data doesn’t just flow through systems.
It flows through people who take responsibility for it.

𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲:
You can’t automate ownership.
You have to design it into your data culture.


Follow Disha Mukherjee for Data Made Easy!

#DataEngineering #DataLeadership #DataGovernance #DataCulture #ModernDataStack #DataEvangelism

For years, I thought the only way to stand out in tech was to be louder, tougher, faster.
Turns out, the real power was in being clearer.

When I started in data engineering, 
I walked into rooms where I was often the only woman.

And in those moments, I used to think-
 “Maybe if I just work harder, they’ll see I belong here.”
So I built.
I delivered.
I stayed quiet and let my work speak for me.

But the truth?
Work doesn’t always speak.
People do.

It took me years to realize that being good at what you do isn’t enough.
You also need to be visible for what you stand for.

That shift changed everything.
I stopped trying to fit the “serious engineer” mold.
I started sharing what I learned- the wins, the mistakes, the messy middle.

And something incredible happened:
More women reached out.
More voices joined the conversation.
More teams started listening.

Because representation isn’t about perfection.
It’s about presence.

Today, I still build scalable systems and write production-grade code 
but I also make space for others to step in, speak up, and be seen.

Because the goal isn’t to be the only woman in the room.
It’s to make sure you’re not the last.

We don’t need more women proving they can code.
We need more women leading the conversation about why they do.

If you’re a woman in tech- what’s one thing you’d tell your younger self just starting out?

Or, if you lead a team, what's one way you create space for more voices at the table?

Follow Disha Mukherjee for more.

#WomenInTech #DataEngineering #Leadership #TechCommunity #DiversityInTech

Most businesses don’t collapse because of bad ideas.
They collapse because they stop paying attention to what their data is trying to say.

Every team leaves behind traces user clicks, transactions, delays, cancellations, costs. Together, they paint a story. But most leaders only look at that story when things start breaking.

By then, it’s usually too late.
Data isn’t just a report to read. It’s feedback in real time. It’s how your business tells you what’s healthy, what’s at risk, and what needs attention.

Think of it like your own health. Your body gives you signals long before something goes wrong. The same way, your business gives you signals long before revenue dips or customers churn.

The real problem isn’t a lack of data.
 It’s the habit of ignoring it until it hurts.
The companies that win are the ones that listen early and act fast.

Because in data and in business, precaution is always better than cure.
How to stay ahead:
→Build observability into your data systems not as an afterthought.
→Set early warning metrics for business health, not just performance dashboards.
→Make data reviews a part of decision-making, not a post-mortem activity.
→Create a culture where teams talk about what the data means, not just what it shows.

Your data always knows before you do. 
The question is are you listening?

Follow Disha Mukherjee for more! 

#DataEngineering #DataDrivenCulture #DataStrategy #Analytics #Leadership #DataObservability

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