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

I am excited to share a comprehensive resource: Machine Learning, Deep Learning, and Artificial Intelligence Cheat Sheets compiled by Nikhil Yadav from Stanford University and MIT!

This collection of cheat sheets covers key concepts and techniques across machine learning, deep learning, and AI. 

Perfect for students, professionals, and enthusiasts, this document provides quick references and summaries of essential topics in these fields.

Key topics include:
- Probability and Statistics fundamentals
- Supervised and Unsupervised Learning algorithms
- Linear and Logistic Regression
- Support Vector Machines (SVM) and Generative Learning
- Tree-based and Ensemble Methods
- Principal Component Analysis (PCA) and other dimension reduction techniques
- Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
- Reinforcement Learning and Control
- Essential metrics for model evaluation and selection
- Tips and tricks for effective machine learning practice


Enhance your understanding and mastery of machine learning, deep learning, and AI with these detailed cheat sheets!

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- Add your comment!

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

ML, Deep Learning and AI Cheat Sheet

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

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

Excited to share an essential resource: DevOps Interview Questions!

This comprehensive guide covers a wide range of questions that are commonly asked in DevOps interviews. 

Whether you're a beginner or an experienced professional, this document provides valuable insights and answers to help you ace your interview and enhance your DevOps knowledge.

Key topics include:
- Understanding DevOps and its need in the industry
- Top DevOps tools and their uses
- Differences between DevOps and Agile/SDLC
- Core benefits and advantages of DevOps
- Key performance indicators (KPIs) for gauging DevOps success
- Role of configuration management and popular tools like Ansible
- Concepts like "Shift left to reduce failure," Blue/Green Deployment, and more
- In-depth look at DevOps methodologies and real-life use cases
- Detailed explanations of tools like Docker, Kubernetes, Jenkins, and AWS


Enhance your interview preparation and stay ahead in the competitive DevOps field with this detailed guide!

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DevOps Interview Questions

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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Power BI Interview Questions

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!

I'm excited to share an essential resource: Python Notes/Cheat Sheet by Mark Graph!

This comprehensive cheat sheet covers a wide range of Python concepts, providing quick references and summaries for both beginners and experienced developers. 

It's an invaluable resource for anyone looking to enhance their Python programming skills.

Key topics include:
- Basic syntax and naming conventions
- Data types: integers, floats, strings, lists, tuples, sets, and dictionaries
- Operators and their functionalities
- Flow control statements: if, for, while, and try/except
- Functions and lambda expressions
- Modules and import statements
- Object-oriented programming: classes, methods, and inheritance
- Commonly used libraries and modules: datetime, math, re, numpy, pandas, matplotlib
- Advanced concepts: iterators, generators, and decorators


Enhance your Python programming knowledge and boost your productivity with this detailed cheat sheet!

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Python Notes

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.

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.

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.

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?

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!

I was once told that learning Python was the key to unlocking a career in data.

But here's the truth: It's not just about the language.

It's about developing an analytical mindset.

When I transitioned from being a data analyst to a data engineer, I realised the importance of how I approached problems, not just the tools I used.

Here's why:
Understanding Context: Before diving into coding, understanding the problem is crucial.

What are you trying to solve?

What data do you need?

The ability to ask the right questions is far more valuable than knowing syntax.
Critical Thinking Over Code: Coding is a means to an end.

The real skill lies in analysing data and drawing meaningful conclusions.

Can you see patterns others miss?

Can you make sense of the chaos?
Data Literacy: It's not about how many programming languages you know.

It's about understanding data itself.

How do you clean it?

How do you ensure it's reliable?
Communication: Data insights are useless if they can't be communicated.

Can you translate complex data into simple narratives?

Can you make stakeholders understand the impact?
Adaptability: The world of data is ever-evolving.

New tools, new methods, new challenges.

Being open to learning and adapting is key.

So, if you're thinking of diving into data, remember this:

→ It's not just about Python or any other tool.

↳ It's about fostering an analytical mindset.

And that mindset is what will truly set you apart in the world of data.

What do you think?

Are you ready to start thinking differently?

The shocking truth: Your data pipeline is only as strong as the weakest intern who maintains it.

Sound alarming?

It should.

In the world of data engineering, where precision and reliability are nonnegotiable, the integrity of your pipeline rests heavily on every individual involved.

Yet, we often overlook the importance of nurturing and empowering those at the beginning of their journey—the interns.

Here's how to strengthen your pipeline by investing in your interns:
Comprehensive Onboarding

→ Ensure that interns receive thorough training on your systems.

↳ This isn't just about technical skills.

It's about understanding your organisation's data philosophy and goals.
Mentorship

→ Pair interns with experienced data engineers.

↳ Real-world guidance and feedback are invaluable.
Safe Learning Environment

→ Create a culture where mistakes are seen as learning opportunities, not failures.

↳ Encourage questions and exploration.
Clear Responsibility

→ Define their roles and responsibilities clearly.

↳ Clarity prevents errors and builds confidence.
Regular Check-ins

→ Schedule regular meetings to discuss progress and challenges.

↳ Continuous feedback loops help in identifying issues early.
Celebrate Achievements

→ Recognise and celebrate their contributions.

↳ Acknowledgement boosts morale and motivation.

By empowering your interns, you're not just securing your data pipeline.

You're investing in the future leaders of your data teams.

Remember, a robust pipeline isn't built on code alone.

It's built on a foundation of well-trained, engaged, and motivated individuals.

So, next time you review your data processes, ask yourself:

Have you given your interns the tools they need to succeed?

Your pipeline depends on it.

What strategies have you implemented to support your interns?

Share your thoughts!

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