Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

Excited to share an essential resource: SQL Commands prepared by Pandeeswari M!

This comprehensive guide covers all the fundamental SQL commands you need to master database management. 

From creating and modifying tables to complex queries and data manipulation, this document is a must-have for anyone working with SQL.

Key topics include:
- Fundamentals of SQL: CREATE, INSERT, SELECT
- Filtering commands: WHERE, AND/OR, BETWEEN, LIKE
- Ordering commands: ORDER BY, LIMIT
- Aggregate commands: COUNT, SUM, AVG, MIN, MAX
- Grouping commands: GROUP BY, HAVING
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, CROSS JOIN
- Subqueries and views
- String, mathematical, and date-time functions
- Pattern matching with regex
- Data type conversion functions


Enhance your SQL skills and streamline your database operations with this detailed guide!

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- Add your comment!

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SQL Commands

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

I'm excited to share an invaluable resource: the Data Engineer Interview Guide by Simplilearn!

Preparing for a data engineering interview can be daunting, but this comprehensive guide has got you covered. 

Whether you are a fresh graduate or an experienced professional, this document provides a detailed overview of the key questions and answers you can expect in a data engineering interview.

Key topics include:
- Understanding the role and responsibilities of a data engineer
- Differences between data engineers and data scientists
- Key skills required for data engineering, including SQL, Hadoop, and Python
- Essential frameworks and applications for data engineers
- Commonly asked interview questions and how to answer them
- Practical tips for handling job-related crises and real-world scenarios
- Insights into data modelling, ETL processes, and working with big data in cloud environments


Enhance your interview preparation and stand out from the competition with this detailed guide!

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Data Engineering Intro

SQL Introduction Handnotes

Excited to share an insightful resource: Introduction to Apache Spark by Slavko Žitnik and Marko Prelevikj from the University of Ljubljana!

This comprehensive guide provides a deep dive into Apache Spark, covering everything from the basics to advanced deployment techniques. 

Whether you're a beginner or an experienced professional, this document will enhance your understanding and use of Spark for big data processing.

Key topics include:
- Overview of Apache Spark and its use cases
- Comparison between Hadoop MapReduce and Apache Spark
- A detailed explanation of Spark's core components, including RDDs, DataFrames, and SparkSQL
- Execution modes and deployment on HPC using Slurm
- Hands-on tutorials with Jupyter notebooks
- Practical lab exercises for real-world applications


Unlock the power of Apache Spark for your data projects with this detailed guide!

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Introduction to Apache Spark

Excited to share a valuable resource: Power BI Interview Questions!

This comprehensive guide is perfect for anyone preparing for a Power BI interview.

It covers a wide range of questions and answers, from basic concepts to advanced techniques.

Whether you're a beginner or an experienced professional, this document will help you ace your interview and enhance your Power BI skills.

Key topics include:
- Optimizing calculations in DAX using the VAR function
- Differences between Star Schema and Snowflake Schema
- Understanding data flows in Power BI
- Different types of data refreshes: manual, scheduled, and incremental
- Challenges faced during report development and how to tackle them
- Field parameters and their usage in visualizations
- Maintaining default total sales values despite applied filters
- Implementing Row-Level Security (RLS) in Power BI
- Custom sorting in visualizations based on specific criteria

Enhance your interview preparation and Power BI knowledge with this detailed guide!

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- Add your comment!

Power BI Interview Questions

Excited to share an essential resource: DAX Query Plans by Alberto Ferrari!

This comprehensive paper introduces the performance analysis and optimization of DAX code through the use of DAX query plans. 

Ideal for BI professionals and data analysts, this document provides valuable insights into improving query performance by understanding and optimizing DAX queries.

Key topics include:
- Finding and interpreting DAX query plans
- Differences between logical and physical query plans
- Overview of the formula engine and storage engine
- Basic optimization techniques for DAX queries
- Practical examples using the Contoso and AdventureWorks databases


Enhance your DAX skills and improve query performance with this detailed guide!

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DAX Query Plans

Forget about becoming a data scientist – here's why data engineering is the real future of data careers.

Five years ago, everyone clamoured to become a data scientist.

The allure of working with cutting-edge algorithms and deriving insights from data was undeniable.

But there's a shift happening.

The gears of the data world are turning, and at the centre of this transformation is data engineering.

Here's why:
The Backbone of Data

Without robust data pipelines, data scientists can't do their magic.

Data engineers are the unsung heroes, ensuring data flows smoothly from source to destination.

They build the architecture that supports analytics, predictive modelling, and AI.
Scalability and Efficiency

As businesses grow, so does their data.

Data engineers design systems that can handle ever-increasing volumes of data without breaking a sweat.

It's about creating infrastructures that are both scalable and efficient.
Bridging the Gap

Data engineers act as a bridge between raw data and actionable insights.

They work closely with stakeholders, understanding business needs and translating them into technical solutions.

This role is pivotal in ensuring that data-driven decisions are both informed and impactful.
FutureProofing Businesses

With the rise of IoT and real-time analytics, the demand for skilled data engineers is skyrocketing.

They are at the forefront of integrating new technologies, ensuring businesses remain competitive in an ever-evolving landscape.
Educational Evolution

Educational institutions are now recognising the importance of data engineering.

Comprehensive curricula are being developed to equip the next generation with the necessary skills.

This is a testament to the growing recognition of data engineering's significance.

To those considering a career in data, understand this:

Data science is exciting, but data engineering is indispensable.

It's the foundation upon which all data-driven initiatives stand.

So, if you're passionate about making a real impact in the world of data, consider stepping into the realm of data engineering.

It's not just a career; it's the future.

What's your take on the shift towards data engineering?

Share your thoughts below!

Stop chasing the latest data trends.

Here's how going back to basics can actually accelerate your career in data.

I began my journey in data with a simple belief:
Understanding the fundamentals is more powerful than chasing every new trend.

In a world obsessed with buzzwords and the latest tech, it’s easy to lose sight of the core principles that truly matter.

When I started as a data analyst, I was overwhelmed.
The sheer volume of data and the pressure to adopt the latest tools is intense.

But then I paused and reflected.

I realised the key to success wasn't in mastering every new tool.
It was in deepening my understanding of the basics.

Here's how I refocused my approach:

1. Master Data Fundamentals
  ↳ I invested time in truly understanding data structures, SQL, and basic analytics.
  ↳ This solid foundation allowed me to adapt to any new tool with ease.

2. Prioritise Problem-Solving
  ↳ Instead of getting distracted by flashy technologies, I focused on solving real business problems.
  ↳ Understanding the problem at its core often led to simple yet effective solutions.

3. Embrace Simplicity
  ↳ Complexity can be enticing, but simplicity is often the most effective.
  ↳ I learned to strip away unnecessary processes and tools to deliver clear insights.

4. Stay Curious and Question
  ↳ I fostered a mindset of curiosity, always asking 'why' to understand the underlying issues.
  ↳ This questioning approach led to more innovative and impactful solutions.

5.  Build Strong Communication Skills
  ↳ Data is only as powerful as the story it tells.
  ↳ I honed my ability to communicate insights in a way that resonated with stakeholders.

These steps transformed my career.
I moved from a data analyst to leading the development of data pipelines and AI systems.

By focusing on the basics, I not only strengthened my technical skills but also became more adaptable and innovative.

For anyone feeling overwhelmed by the pace of change in data, remember:

Going back to basics doesn't mean stepping backwards.

It means laying a strong foundation that can support any future growth.

What basics are you revisiting in your career?

I'd love to hear your stories.

Let's inspire each other to focus on what truly matters.

From analysing data to leading an entire organisation.

Why should every CEO start as a data professional?

It’s a bold idea, but hear me out.

When I first started data engineering, I was overwhelmed.

Huge datasets, complex systems, and the pressing need for actionable insights.

Sounds familiar?

Most organisations face these challenges, and CEOs aren’t any different.

Starting in data engineering offers unique lessons:

1. Seeing the Bigger Picture: Data engineers work at the heart of organisations.

They connect dots, streamline processes, and harness data to drive decisions.

This experience gives a holistic view of how different departments align.

2. Problem Solving Mindset: In data, every day presents a new puzzle.

You learn to approach issues methodically and creatively.

A skill any CEO would find invaluable.

3. Embracing Change: The tech world evolves rapidly.

Data engineers must adapt quickly to new tools and methods.

This agility is crucial for leading a company through change.

4. Leadership Through Understanding: Knowing the intricacies of data can foster better communication.

CEOs can bridge gaps between tech teams and strategic decision-making.

5. Focusing on Results: Data engineering is about transforming numbers into actionable insights.

CEOs with this background know how to measure success effectively.

Imagine a world where every leader understands the power of data.

Where decisions aren’t just gut feelings but informed by solid evidence.

CEOs who begin their careers in data engineering have a distinct advantage.

They’re not just leaders; they’re informed leaders.

So, if you're considering a career path or looking to pivot, think data engineering.

It might just be the foundation you need for extraordinary leadership.

What do you think?

Should CEOs learn data before leading?

I'd love to hear your thoughts.

The real secret to becoming a data professional isn't technical skills—it's storytelling.

Here's how narrative trumps numbers.

When I first entered the world of data, I was captivated by the endless possibilities that numbers could unlock.

But soon, I realised something crucial: data alone doesn't speak.

It's the story you weave around it that truly makes an impact.

Let me explain how you can harness the power of storytelling in data:

1. Understand Your Audience
  ↳ Tailor your narrative to resonate with the specific needs and interests of your audience.
  Whether you're speaking to executives or data enthusiasts, adjust your story accordingly.

2. Find the Human Element
  ↳ Data is ultimately about people.
  By highlighting the human impact, you make your insights relatable and actionable.

3. Create a Compelling Narrative Arc
  ↳ Like any good story, your data-driven narrative should have a clear beginning, middle, and end.
  Start with the problem, introduce the data, and conclude with actionable insights.

4. Simplify Complex Data
  ↳ Great storytelling involves breaking down complex data into understandable and engaging segments.
  Use visuals and analogies to make your points clear.

5. Inspire Action
  ↳ Your story should lead to a call to action, encouraging your audience to make informed decisions based on your insights.
  
Remember, data storytelling isn't just about presenting facts—it's about driving change.

By mastering the art of storytelling in data, you transform from a mere analyst to an influential data storyteller.

And the best part?

Anyone can learn this skill.

So, are you ready to craft stories that drive data-driven decisions?

Start weaving your narrative today.

Data degrees are great, but here’s why data engineering skills might just be the game-changer you need.

In a world overflowing with data, what’s the biggest challenge?

Turning it into something valuable.

That’s where a data engineer steps in.

Here’s why mastering data engineering skills can outshine a traditional data science degree in today’s job market:

1. Demand for Data Engineers is Soaring

→ Companies need robust data pipelines and clean data before they can even start analysing.

↳ Without data engineers, data scientists can’t do their jobs effectively.

2. HandsOn Problem Solving

→ Data engineering focuses on building and optimising data systems.

↳ You’ll deal with realworld data challenges and provide solutions that have immediate impacts.

3. Versatile Skill Set

→ From coding and database management to understanding cloud services, data engineering equips you with a wide array of skills.

↳ It’s about the whole data ecosystem, not just analysis.

4. Foundation for Advanced Data Exploration

→ Before models can be trained, data must be prepped and organised.

↳ This means your work sets the stage for any advanced data science application.

5. Bridge the Gap Between Theory and Practice

→ While data science often leans towards theoretical aspects, engineering is grounded in practical application.

↳ You’re not just predicting the future; you’re building the road to it.

6. CostEffective Learning

→ You can learn data engineering through online courses, handson projects, and bootcamps.

↳ No need for a hefty degree investment.

7. Innovation at the Core

→ Data engineers are the architects of the digital age, creating systems that allow companies to innovate and grow.

↳ You’re paving the way for future technologies.

So, if you’re weighing options between a degree and diving into data engineering, consider what aligns with the current industry needs.

The world needs more data engineers.

Are you ready to be one?

Engage with me in the comments if you’re considering a career shift or just starting out in data engineering.

What’s your next step?

Excited to share an insightful resource: MLlib: Scalable Machine Learning on Spark by Xiangrui Meng and collaborators!

This comprehensive guide explores the powerful capabilities of MLlib, Spark's scalable machine learning library.

It covers everything from basic algorithms to advanced machine learning techniques, making it an essential resource for data scientists and engineers looking to leverage Spark for large-scale data processing and analysis.

Key topics include:
- Overview of MLlib and its integration with Apache Spark
- Machine learning algorithms: classification, regression, clustering, and collaborative filtering
- Practical examples and implementations of logistic regression, k-means clustering, and ALS
- Performance comparisons and scaling properties of MLlib
- New features and updates in MLlib v1.x
- Integration with other Spark components like Spark SQL, Streaming, and GraphX


Unlock the full potential of machine learning with MLlib on Spark and take your data projects to the next level!

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Why I believe the future of data engineering lies not in new tech but in teaching empathy and communication.

I started in data engineering with the belief that the latest tech would solve every problem.

That mindset shifted.

Here's why: it's not just about the tools.

It's about understanding the humans behind the data.

In my journey from data analyst to data engineering leader, I've learned that the hardest problems aren't technical.

They're human.

When I was tasked with transforming raw data into insights, I quickly realised that many organisations were drowning in fragmented data.

Not because they lacked technology, but because they lacked communication.

→ Data was siloed.

→ Teams didn't talk to each other.

→ Processes were inefficient.

So I focused on:

1. Building Bridges: I brought together diverse teams, ensuring everyone understood the data's value and purpose.

2. Teaching Empathy: I encouraged my team to see data not just as numbers but as stories with human impact.

3. Enhancing Communication: I led efforts to develop clear channels and languages for data discussions, making complex insights accessible to all.

It wasn’t just about algorithms.

It was about collaboration.

About understanding the fears and hopes of those who rely on the system.

When we embrace empathy and communication, we transform data from a static resource into a dynamic force for change.

We give it life.

The future of data engineering won't be about the flashiest tools.

It will be about the human touch.

So here's my challenge to you:

Focus on the human side of data.

Ask more questions.

Listen more.

Let's build a future where data brings us together, not keeps us apart.

What do you think?

How can we make data engineering more human?

Share your thoughts below.

The secret to a successful data career? It's not about the tools you know but the problems you solve.

When I first stepped into the world of data, my focus was solely on mastering the latest tools.

Python, SQL, Tableau—you name it.

But it wasn’t long before I realised that the true essence of a data career is not about the tools.

It’s about understanding the problem.

Here's what I learnt on my journey from a data analyst to leading data pipeline projects.

→ The ProblemFirst Approach: Before diving into any dataset, I always ask, “What problem am I solving?”

This approach ensures that the solutions I craft are not only data-driven but also impactful.

→ Building a Common Data Platform: This wasn’t just about the tech; it was about solving the issue of scattered data access.

→ Innovating at NATS: Contributing to the world’s first AI system for air traffic control taught me that leveraging digital twinning and machine learning was revolutionary.

But the real magic? Solving air traffic management problems.

→ Shaping Future Talent: At Multiverse, developing curricula for apprentices wasn’t just teaching tools.

It was about preparing the next generation to tackle real-world data challenges.

→ Consulting for Industry Giants: BP, Sword, Chaucer—they didn’t just need data insights.

They needed solutions to drive innovation and decision-making.

The tools were merely instruments to achieve that.

In data, tools will come and go.

What's constant is the ability to solve complex problems.

That’s the true measure of success in this field.

If you’re looking to build a career in data, focus on the problems first.

The tools will follow.

What challenges are you solving with your data skills?

Let’s discuss them in the comments!

Why data engineers are like the unsung heroes of the tech world – and why you should become one.

Ever wondered what makes the tech world tick?

It’s not just the flashy apps or cutting-edge AI.
It’s the data engineers behind the scenes, making everything work smoothly.

Here’s why they’re the real MVPs:

1. Foundation Builders

Data engineers lay the groundwork for any data-driven project.

Without them, data scientists and analysts would be flying blind.

They create robust data pipelines that ensure seamless data flow – the backbone of every tech solution.

2. Problem Solvers

Every day brings a new challenge.

From fragmented data sources to integrating complex systems, data engineers thrive on solving intricate puzzles.

Their work is crucial for turning raw data into actionable insights.

3. Innovation Enablers

The world’s first AI system for air traffic control?

That’s just one example.

Data engineers make groundbreaking innovations possible by ensuring data is clean, reliable, and ready for analysis.

4. Guardians of Data Integrity

Data is valuable, but only if it’s accurate.

Data engineers ensure quality and consistency, protecting organisations from costly errors.

They’re the gatekeepers of trustworthy data.

5. Educators and Mentors

Building the future doesn’t stop at technology.

Data engineers also shape the next generation of tech talent, sharing their knowledge and experience with aspiring professionals.

So, why should you consider becoming a data engineer?
→ It’s a role that combines creativity and technical skill.
→ There’s an evergrowing demand for data-driven solutions.
→ You’ll be at the forefront of innovation, where every project is an opportunity to make a significant impact.

Join the ranks of these unsung heroes and be part of a community that drives the tech world forward.

Are you ready to build the future?

Let’s discuss why data engineering should be your next step.

Drop a comment below to share your thoughts or ask questions about this exciting field.

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