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

You're STILL a data analyst, even if...

- Excel is your main tool
- SQL syntax slips your mind
- You didn't ace that important interview
- Programming isn't your strong suit
- You made a mistake in your analysis
- You can't recall the exact function name
- You need to Google how to redo something simple
- You're NOT a data analyst when...

You decide to quit
SO DON'T QUIT! KEEP PUSHING FORWARD!

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

I am excited to share an essential resource: Structured Query Language (SQL) by Fang Ying, Sham!

This comprehensive guide covers fundamental SQL concepts and commands, making it perfect for students, professionals, and anyone looking to deepen their understanding of SQL and database management.

Key topics include:
- Creating and using databases and tables
- Inserting, updating, and deleting records
- Basic SQL queries: SELECT, WHERE, ORDER BY, DISTINCT
- Advanced SQL operations include JOINs, UNIONS, INTERSECT, and EXCEPT.
- Working with views, temporary tables, and common table expressions (CTEs)
- String functions, subqueries, and stored procedures
- SQL ranking functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE
- Handling NULL values and using CASE statements
- Aggregation functions: COUNT, SUM, AVG, MAX, MIN
- Data type conversions and partitioning

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

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The award of this certificate is in recognition of my participation in the SEEP 2019 conference held between 18 - 21 November 2019 at the University of Sharjah, United Arab Emirates where I delivered two (2) papers.

Certificate of Participation

The certificate is a result of my participation in the 5th International Conference on Material Science & Smart Materials where I presented a poster titled "Characterisation of Solar Materials and Electronic Devices using Cadmium Telluride (CdTe) and Zinc Sulphite (ZnS) as a Case Studies"

Certificate

The certificate is a result of my participation in the SEEP 2018 conference where I presented a paper titled "Effect of Membrane Thickness on the Performance of PEM Fuel Cell"

Certificate of National Service

I received this letter due to my invaluable contribution to the successful delivery of the first historic comprehensive training programme for the Volunteer Cadets in Nigeria, the OYES Volunteer Cadets' Training Course.  

Letter of Appreciation

The certificate is a recognition of my service to the club as the Vice-President (Business) in 2010.

Certificate of Fellowship

The award is a recognition of my service to the student society as General Secretary - 2007/2008, Vice-President - 2008/2009, President - 2009/2010 and National Vice-President (West) - 2010/2011.

Certificate of Service

This certificate is due to my participation in the 40th annual general meeting and conference of the society.

Certificate of Attendance

I received this certificate as a result of my participation in the international essay competition organised by the World Bank. The title is "How does youth unemployment affect you? How can you tackle youth unemployment through youth-led solutions"

Certificate of Merit

Marketing: Copywriting for Social Media

Developing CI/CD Solutions with Azure DevOps

Python Projects

Microsoft Certified: Azure Data Engineer Associate

Microsoft Certified: Azure AI Fundamentals

Microsoft Certified: Azure Data Fundamentals

Microsoft Certified: Azure Fundamentals

Data Engineering Foundations

[PCAP-31-03] PCAP – Certified Associate in Python Programming

Agile at Work: Building Your Agile Team

Scrum: The Basics

Stay Lean with Kanban

Machine Learning Essentials for Seismic Interpretation

Starting a Memorable Conversation

Artificial Intelligence Foundations: Neural Networks

Diversity, Inclusion, and Belonging

Machine Learning and AI Foundations: Classification Modeling

Body Language for Leaders

Industrial/Oilfield Health, Safety & Environmental Management Appreciation

Introduction to Auditing and Evaluating Behaviour Change (IEMA approved training)

International Association of Engineers

Institution of Chemical Engineers (IChemE)

The Institution of Engineering and Technology

Emmanuel is dedicated to helping you break into data science and AI with practical tips, real-world insights, and the latest trends.

Emmanuel Ogungbemi | Substack

We are delighted to be back for the second edition of ML Club Live!

This event is all about getting hands-on with GenAI, with some coding examples and also some discussions of interesting papers.

As always, this is community-driven, so if you want to come and speak about anything related to ML or AI, even for 10 minutes, please reach out to Emmanuel (bunmieogungbemi@gmail.com) or Andy (andrewpmcmahon629@gmail.com).

ML Club - July 2024 - Hands on with GenAI, Tue, Jul 2, 2024, 5:30 PM | Meetup

I coordinate Real Projects Talk for AFBE-UK Scotland

Organising Data Science and Engineering events
- To help newbies get into data science
- Organise boot camps to support learning and development
- Support working professionals by providing mentorship and advice
- https://www.meetup.com/ml-club/

SQL Commands II

I am excited to share an invaluable resource: Introduction to Programming with Python!

This comprehensive guide covers the fundamental concepts of programming using Python. 

Perfect for students, beginners, and anyone looking to enhance their programming skills, this document provides clear explanations and practical examples.

Key topics include:
- Programming basics: source code, syntax, and output
- Compiling and interpreting Python code
- Working with expressions, arithmetic operators, and precedence
- Handling integer and real number operations
- Using variables and assignment statements
- Printing output and reading user input
- Control flow: loops (for, while) and conditionals (if/else)
- Data types: strings, lists, tuples, sets, and dictionaries
- Functions and higher-order functions
- Object-oriented programming: classes, objects, methods, and inheritance
- File processing and text manipulation


Enhance your Python programming knowledge and build a solid foundation with this detailed guide!

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Introduction to Programming with Python

Excited to share an invaluable resource: Top 50 Machine Learning Interview Questions & Answers!

This comprehensive guide covers the most frequently asked questions in machine learning interviews. Perfect for students, professionals, and anyone preparing for a machine learning interview, this document provides detailed answers and explanations to help you succeed.

Key topics include:
- Basics of Machine Learning: Definition, Differences between Data Mining and Machine Learning, and Key Algorithms.
- Overfitting: causes, avoidance techniques, and cross-validation.
- Types of Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement learning.
- Common Algorithms: Decision Trees, Neural Networks, Support Vector Machines, and More.
- Model Evaluation: Training and test sets, model building, testing, and application.
- Algorithm Techniques: Inductive Learning, Probabilistic Networks, and Clustering.
- Advanced Topics: Genetic Programming, Bayesian Networks, PCA, ICA, and Ensemble Methods.
- Practical Applications: Real-world use cases in computer vision, speech recognition, and recommendation systems.


Enhance your interview preparation and machine learning knowledge with this detailed guide!

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Microsoft Azure - AZ 900 Introduction

Excited to share an essential resource: Model Evaluation and Selection Cheatsheet by Waleed Mousa!

This comprehensive cheatsheet covers crucial techniques and best practices for evaluating and selecting machine learning models. 

Perfect for data scientists, ML engineers, and anyone working with machine learning, this document provides quick references to help you optimize your models.

Key topics include:
- Data Splitting: Techniques for splitting datasets into training, test, and validation sets.
- Model Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC AUC, MSE, MAE, R2 score, and more.
- Cross-validation: K-fold, stratified K-fold, leave-one-out, and multi-metric cross-validation.
- Hyperparameter Tuning: Grid search CV, randomized search CV, and Bayesian optimization.
- Model Selection: Comparing classifiers, feature importance, and pipeline creation.
- Ensemble Methods: Random Forest, Gradient Boosting, AdaBoost, and stacking classifiers.
- Dimensionality Reduction: PCA, t-SNE, and feature selection.
- Data Preprocessing: Scaling, encoding, imputing, and generating polynomial features.
- Advanced Model Evaluation: ROC and precision-recall curves, confusion matrix visualization, adjusted R2.
- Handling Imbalanced Datasets: Under-sampling, over-sampling, SMOTE, and class weights.
- Advanced Feature Selection: Recursive feature elimination, mutual information, SelectKBest.
- Model Interpretability: Permutation importance, SHAP values, LIME.
- Time Series Analysis: Rolling and expanding window features, time series cross-validation.
- Working with Text Data: CountVectorizer, TF-IDF, and word embeddings.
- Neural Networks: Keras/TensorFlow, early stopping, custom loss functions, fine-tuning pre-trained models.


Enhance your model evaluation and selection process with this detailed cheat sheet!

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ML - Model evaluation and selection

I am excited to share an essential resource: Big Data Engineering Interview Questions & Answers!

This comprehensive guide is perfect for anyone preparing for big data engineering interviews. 

It covers a wide range of topics, from SQL and Scala programming to tools like Sqoop, Hive, and Spark, providing valuable insights and detailed answers to help you ace your interviews.

Key topics include:
- SQL Interview Questions: Understanding query execution sequence, normalization, database objects, constraints, transactions, and various SQL operations.
- Scala Programming: Basics of Scala, Tail-Recursion, Traits, Case Classes, and Higher-Order Functions.
- Sqoop Interview Questions: Data Import/Export, Connectors, and Performance Tuning.
Hive Interview Questions: Hive architecture, data types, partitions, and optimization techniques.
- Spark Interview Questions: RDDs, DataFrames, Spark SQL, and Performance Tuning.
Top 250+ Interview Questions: Covering a broad spectrum of big data tools and technologies.
- Miscellaneous Interview Questions: Addressing various aspects of big data engineering, including data modelling, ETL processes, and big data ecosystems.


Enhance your interview preparation and big data engineering skills with this detailed guide!

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Big Data Engineering Interview Questions and Answers

When I first entered the world of data, I discovered the power of SQL and databases to transform raw data into meaningful insights.

The challenge? 

Many professionals struggle to grasp the foundational concepts and practical applications of SQL.

Here's how I mastered SQL and databases and advanced my career:

1. Learning the Basics
I started with the core concepts of relational databases and understanding tables, relationships, and critical constraints.

2. Hands-On Practice
Wrote optimised SQL queries to extract, manipulate, and analyse data across various projects.

3. Complex Problem Solving: 
Designed and implemented efficient database schemas and complex queries to support large-scale data platforms.

4. Continuous Improvement
Stayed updated with the latest database technologies and best practices through courses, certifications, and community involvement.

Now, let's talk about you.

Are you looking to enhance your skills in SQL and databases but don't know where to start?

Here's a structured approach to help you:

1. Understand the Fundamentals
- Learn about relational database concepts and structures.

2. Get familiar with SQL syntax and basic queries (SELECT, INSERT, UPDATE, DELETE).

3. Practical Application
- Practice writing SQL queries to solve real-world problems.
- Use database management systems like MySQL, PostgreSQL, and Microsoft SQL Server.

4. Advanced Techniques:
- Explore advanced SQL topics such as joins, subqueries, indexing, and transaction management.
- Learn how to denormalise database schemas for efficient data storage and retrieval.

5. Continuous Learning and Networking
- Join SQL and database forums to ask questions and share knowledge.
- Attend webinars and workshops to stay updated with industry trends.

I've created a comprehensive Data Analyst Roadmap to support your learning journey with essential steps and resources for mastering SQL and databases.

To get a link to download your free Data Analyst Roadmap, do this:
1. Repost 
2. Comment "share".

You have the potential to excel in SQL and database management, and I'm here to help you achieve your goals.

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

Excited to share an invaluable resource: Machine Learning and Deep Learning by Sven-Patrik Hallsjö!

This comprehensive guide delves into the fundamental concepts and advanced techniques of machine learning and deep learning. 

Perfect for students, professionals, and enthusiasts, this document provides a thorough overview of key topics, supported by practical examples and insights from leading resources.

Key topics include:
- Introduction to Machine Learning: Supervised vs. Unsupervised Learning, Linear Regression, and Motivating Deep Learning.
- Deep Learning: Forward Feed, Backfitting, Learning vs. Pure Optimization, and Convolutional Neural Networks (CNN).
- Real-life Applications and Current Research: Practical applications and ongoing research in the field.
- Neurons and Perceptrons: Understanding the basics of neural networks and their implementation.
- Universal Approximation: Theoretical foundations and practical implications of deep learning models.
- Backpropagation: Detailed explanation of the backpropagation algorithm and its importance in training neural networks.
- Convolutional Networks (CNN): Specialized neural networks for processing grid-like data, such as images and time-series data.
- Online Applications and Resources: Practical tutorials and resources for applying machine learning and deep learning in real-world scenarios.
- Further Learning Resources: A curated list of courses, tutorials, and lectures from top institutions and platforms.


Enhance your understanding of machine learning and deep learning with this detailed guide and take your skills to the next level!

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Machine Learning - Deep Learning

Job rejections are tough.

But here's why you shouldn't take them personally.

Many factors can be beyond your control:

- The company may have cancelled the role
- The hiring manager could have left the company
- The role might have been filled internally
- Unconscious bias might have influenced the decision
- Another candidate might have more experience than you
- Your application might have arrived after they shortlisted candidates

Instead of dwelling on uncontrollable factors, focus on what you can control:

- Create relevant portfolio projects to demonstrate your technical skills
- Ask for feedback on your applications and interview performance
- Participate in mock interviews to improve your interview skills
- Network and build relationships within your industry
- Enhance your LinkedIn profile and online presence
- Practice coding regularly

The job market seems to be improving daily.

If you're searching for a job, I'm cheering you on. 

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I am excited to share an insightful resource: Apache Spark (CS240A) by T. Yang!

This comprehensive guide explores the powerful capabilities of Apache Spark for distributed computing. 

Ideal for data scientists, engineers, and anyone working with big data, this document provides a deep dive into Spark's architecture, APIs, and applications.

Key topics include:
- Introduction to Hadoop and MapReduce
- Overview of Spark and its advantages over MapReduce
- Python examples and list comprehension
- Key concepts of RDDs (Resilient Distributed Datasets)
- Language support for Python, Scala, and Java
- Creating RDDs and Spark transformations
- Basic actions and working with key-value pairs
- Detailed explanation of the DAG scheduler
- Setting the level of parallelism
- Interactive shell and standalone application usage
- Example application: PageRank algorithm using MapReduce


Enhance your understanding of distributed computing and harness the full power of Apache Spark with this detailed guide!

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Apache Spark

Microsoft Azure - AZ 104 Introduction

The worst part of working in SQL? Waiting for your query to run.

Here are 9 tips for optimizing your SQL queries 

- Reduce Unnecessary Data Processing

Delay using ORDER BY until the final SELECT statement. Subqueries and CTEs usually don�t need ordering.

Use DISTINCT sparingly. It can be computationally expensive, especially on large datasets.

Opt for UNION ALL instead of UNION when duplicates are acceptable or unlikely.

SELECT only the specific columns you need instead of using SELECT *.

- Take Advantage of Indexes on Your Tables

Filter on the index key whenever possible to minimise the data size processed.

Limit the use of OR conditions, as they can lead to inefficient full table scans.

Avoid using functions in WHERE clauses, as they can interfere with indexing.

- Reduce Cardinality of JOINS

Use a CTE or subquery to filter and reduce table sizes before performing a JOIN.

Apply filter conditions in the ON clause rather than the WHERE clause to reduce rows before the JOIN operation.

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10 Data Science Managers said this about their WORST interview candidates.

Here's what they said

- Lacked Self-Awareness and Were Not Self-Critical
Some candidates couldn't reflect on a time when a project went wrong or discuss what they learned and what they would do differently.
They showed no evidence of self-improvement or reflection when asked about their gaps.
A major red flag is when a candidate insists a project went perfectly and claims there's nothing they would change in hindsight.

- Overconfident and Un-Coachable
"One candidate was overconfident, didn't listen carefully to questions, and refused to answer them directly."
A significant concern is when a candidate struggles to articulate their thought process. This indicates potential difficulties in coaching and development.

- Not Genuinely Interested in the Company
"They asked very basic questions that showed no research on the role or the company."
When given a chance to ask questions, one candidate only asked about the quality of the food at the company.

- Unstructured in Their Communication & Thought Process
Scattered thinking, repeating themselves, and switching between topics makes conversation disjointed and hard to follow.
Providing overly detailed answers beyond what was asked leaves less time to complete the interview.

- Not a Team Player
A red flag is when a candidate tries to take solo credit for an entire project, suggesting a lack of collaboration and team spirit.

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Data Science is NOT All About Coding. 
 
 
When I first entered the data science arena armed with solid coding skills, I thought I was fully equipped.  
 
But as I navigated real-world data challenges, I quickly realized that my coding prowess was just one piece of the puzzle.  
 
The journey into data science demanded much more! 
 
Data science is indeed a mosaic of skills, not a monolith built solely on coding. It thrives on a blend of technical skills and strategic thinking. 
 
DOs: 
 
- Embrace Statistics 
 
Master statistical methods to extract meaningful insights from raw data. 
 
- Cultivate Domain Expertise 
 
Deepen your understanding of the industry you're serving to make your analyses more impactful. 
  
- Enhance Problem-Solving Skills 
 
Engage in projects that push your analytical boundaries and require novel solutions. 
 
DON'Ts: 
 
- Don't Focus Solely on Coding 
 
While important, don't neglect the power of analytical reasoning and contextual knowledge. 
 
- Don't Ignore Soft Skills 
 
Communication and teamwork are crucial in translating data insights into actionable business decisions. 
 
- Don't Stop Learning 
 
The field is evolving rapidly; staying updated is key to staying relevant. 
 
Did you ever encounter a moment in your data science journey where coding alone wasn't enough?  
 
What other skills have you found essential? 
 
If this resonates with you, like, comment, and share your thoughts!  
 
How do you balance data science's technical and strategic demands in your career? 
 
Stay tuned for more myths busted and tips shared.  
 
Follow me, and let's connect to explore the multifaceted world of data science together!

Interviews are only 50% about getting the right answer.

The other 50%? Effective communication.

The main reason for poor communication: not understanding your audience.

We often focus solely on what we want to say.

Let's shift our perspective.

Focus on what matters to your audience.

Instead of: How do I list my past professional experience?
Ask yourself: What does the recruiter look for when screening candidates?

Instead of: How do I explain my current job duties?
Ask yourself: Who does a manager want to hire to make their job easier?

Instead of: How do I solve this SQL question?
Ask yourself: What will a business partner want to know from this data point?

Instead of: How do I show I�m a good culture fit for this company?
Ask yourself: Based on my conversations, what behaviors does this company value?

Instead of: How do I stand out from other candidates?
Ask yourself: What unique value can I bring that aligns with the company's goals?

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Some Power BI interview questions for an analyst interview :

1. What is Power BI, and how do you use it?

2. How do you connect to data sources in Power BI?

3. What is a measure in Power BI, and how do you create one?

4. What are the differences between calculated columns and measures in Power BI?

5. Can you explain the concept of drill-through in Power BI and give an example?

6. How do you create a dashboard in Power BI, and what are the best practices?

7. How do you use Power Query in Power BI to clean and transform data?

8. What is DAX, and how do you use it in Power BI?

9. Can you explain the difference between a table and a matrix visual in Power BI?

10. How do you use Power BI to create a report that updates automatically based on new dataset? 

11. How do you share your reports and dashboards with others in Power BI?

12. How do you handle large datasets in Power BI?

13. Can you give an example of a complex data model you have built in Power BI?

14. How do you handle data refreshes in Power BI, and what options are available?

15. How do you handle security and permissions in Power BI?

16. What are some limitations of Power BI, and how would you work around them?

17. Can you explain the difference between calculated columns and measures in Power BI?

18. How do you create a drill-through report in Power BI?

19. How do you create a custom visualization in Power BI?

20. How do you optimize the performance of a Power BI report?

21. Can you explain the difference between DirectQuery and Import modes in Power BI?

Analysts do more than write code and make dashboards.

In reality, it's a very collaborative role.

Here's an overview of what to expect:

1. Gathering the Data:

This often means meetings.

Depending on the team and project scope, it could be a lot of meetings.

This is necessary to gather requirements for the project.

2. Analyzing the data:

Here's where the heavy lifting often begins.

- Make sure you have access to the data.
- Establish data connections
- Begin data manipulation

If you don't have access to the data, get ready for more meetings to make it happen.

3. Presenting the data:

After you've validated the data, you can begin creating your dashboards/reports.

What about data quality errors?

This happens often. Here comes another meeting with your stakeholders and other team members.

Once validated...

- Complete report
- Seek feedback
- Iterate if necessary

The collaborative nature of the role is why soft skills are stressed so much.

Yes, you need to be technically saavy...

But you also need to be a well-rounded teammate.

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10 Advanced SQL Interview Questions

Hint: Proficiency with Window functions is essential to tackle these.

Q1: You have two tables: 'response_times' (request_id, response_time_ms, device_type_id) and 'device_types' (device_type_id, device_name, manufacturer). Write a query to find the 95th percentile of response times for each device manufacturer.

Q2: For the table 'daily_visits' (visit_date, visit_count), create a query to compute the 7-day moving average of daily visits for each date.

Q3: Given 'stock_prices' (date, stock_symbol, closing_price), write a query to determine the cumulative change in stock price relative to the year's starting price.

Q4: You have 'products' (product_id, product_name, category_id, price) and 'categories' (category_id, category_name). Write a query to find the price difference between each product and the next most expensive product within the same category.

Q5: For the table 'customer_spending' (customer_id, total_spend), write a query to divide customers into 10 deciles based on their total spending.

Q6: Using 'daily_active_users' (activity_date, user_count), write a query to calculate the day-over-day change in user count and the growth rate.

Q7: Given 'sales' (sale_id, sale_date, amount), write a query to compute the total sales amount for each day of the current month, along with a running total of month-to-date sales.

Q8: You have 'employee_sales' (employee_id, department_id, sales_amount) and �employees� (employee_id, employee_name). Write a query to identify the top 5 employees by sales amount in each department.

Q9: Using 'employee_positions' (employee_id, position, start_date, end_date), write a query to find employees who have been promoted (i.e., changed to a different position) within 6 months of their initial hire date.

Q10: You have 'customer_transactions' (customer_id, transaction_date, transaction_amount) and 'customer_info' (customer_id, customer_name, signup_date). Write a query to calculate the moving average of transaction amounts for each customer over their last 3 transactions, only including customers who have been signed up for more than a year.

Which question did you find the most challenging?

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Common ETL Pipeline Mistakes Every Data Engineer Should Avoid! 

As a seasoned vet in data engineering, I've noticed that even the pros sometimes stumble over the same hurdles when crafting ETL pipelines.

Here's how to avoid those pitfalls and elevate your data workflows:

1. Data Quality Checks: DON'T SKIP!
Missing these can seriously affect your results. To maintain its accuracy, validate your data relentlessly.

2. Scalability is Key
Failing to plan for growth will eventually clog up your pipeline. Always design with expansion in mind to keep things running smoothly.

3. Monitoring & Alerting:
Implement monitoring tools and set alerts to catch and squash bugs as soon as possible.

4. Keep Transformations Simple
Complications aren't always better. Aim for sleek, simple transformations to avoid unnecessary headaches.

5. Documentation is your Best Friend
Don't leave your team guessing. Clear, comprehensive documentation saves time and sanity.

By dodging these common errors, you're on your way to creating top-notch, robust ETL pipelines that stand the test of time.

Remember, perfection is a journey. Keep refining your craft and embracing the ever-changing data landscape.

Your thoughts? What other blunders should we watch out for in ETL development?

Let's share our wisdom and grow together!

I’m Chris Steffens, and I’ve built a career as a brand and business development consultant, bringing over 14 years of experience to the table. Right now, I’m actively involved with LinkedIn Local Harford & Cecil Counties, where I focus on connecting professionals, fostering community engagement, and helping members build meaningful personal and business brands. 

My work centers on helping individuals and organizations build authority, expand reach, and engage their audiences more effectively.

Chris Steffens

Make sure you measure your environment before making a leaping commitment this snowed-in start of this January 26th workweek.

As we head into this new workweek, businesses across the U.S. are navigating the significant aftermath of this past weekend's historic snow and ice event.

 With over 190 million Americans under weather alerts and "Winter Storm Fern" impacting a 2,000-mile stretch, the ripple effects on commerce and operations will be felt for days.

Here is a quick look at how the storm is shaping the business landscape this week:

Logistics & Supply Chain: Over 15,000 flights have been delayed or cancelled since Saturday. Major hubs in the South and Northeast are still clearing backlogs, while icy conditions on critical arteries like I-40 and I-95 are causing significant delays in last-mile deliveries.

Infrastructure & Power: More than 1 million homes and businesses lost power, particularly in Tennessee, Louisiana, and Mississippi. Restoration efforts are hampered by a "historically long" cold spell that is preventing ice melt.

Workforce & Operations: Federal offices in D.C. are closed today, and many private sector employees in the Mid-Atlantic and South are shifting to mandatory remote work to avoid hazardous commutes. 

Major manufacturers in the South have already issued "stay home" orders through Tuesday.

Retail Shifts: While essential retailers like Kroger and Target have remained open with adjusted hours, non-essential retail and dining saw widespread closures over the weekend, shifting consumer demand further toward digital channels.

The Bottom Line: This week is about resilience and flexibility. Leaders are prioritizing safety and leveraging remote-work infrastructure while logistics teams scramble to reroute shipments around the deep freeze.
Stay safe out there, and check in on your teams.

#BusinessContinuity #SupplyChain #WinterStorm2026 #RemoteWork #Logistics #Leadership

“Think Local but be Global” - Brand and Business Development - LinkedIn Local Consultant - LinkedIn Page Development - Quick Promos - Lead Generation using Sales Navigator, Employee of the Day! Text 518-859-1156 for CB

Sort of going for a little sparkle in our Snowy and Icy Sunday.

The Great Freeze: What Your Flight Status Isn't Telling You If you were planning on flying this week, the "Ice-pocalypse" might have other ideas. With a storm of this magnitude testing the nation's power grids and affecting nearly 200 million people, airlines are preemptively pulling the plug on thousands of flights.

The Silver Lining: The "Big Five" (American, Delta, JetBlue, Southwest, and United) are actually being helpful for once—waiving change fees so you can avoid a terminal floor sleepover.

The Reality Check: We haven’t seen the "Cancelled" boards look this red since the government shutdown last year.

#WinterStorm
#TravelAlert
#FlightCancellations
#EastCoastWeather

As you cycle through some of the videos here on LinkedIn, this should wheely get your attention.

2026 is officially the year that recycling in the USA moves from "aspiration" to "industrial execution."

If you feel like the recycling conversation has been stuck in neutral for the last decade, you aren’t alone. But as of January 2026, a massive shift in technology and policy is finally closing the loop.

Here are the four innovations and trends defining the recycling landscape this year:

1. The AI Sorting Revolution
We have officially reached a tipping point where Artificial Intelligence is more accurate than human eyes. Facilities across the U.S. are reporting efficiency gains of up to 60% thanks to deep-learning vision systems.
The 95% Benchmark: New AI-driven optical sorters can now identify polymers, colors, and even specific brands with 95%+ purity.
The Result: This virtually eliminates the "contamination" problem that previously sent entire batches of recyclables to the landfill.

2. Molecular & Enzymatic Recycling
Traditional mechanical recycling (shredding and melting) often degrades plastic quality. In 2026, molecular recycling (also known as advanced or chemical recycling) is scaling to an industrial level.
Breaking the Bonds: Technologies like pyrolysis and depolymerization break plastic down into its original building blocks (monomers), allowing it to be recycled infinitely without losing strength.
Nature’s Helpers: We are seeing the first commercial-scale applications of enzyme-based recycling, where engineered bacteria "eat" PET plastic under moderate temperatures, turning waste into high-quality raw materials.

3. Policy Meets the Pavement: EPR Laws
2026 marks the "go-live" phase for several Extended Producer Responsibility (EPR) programs.
States Leading the Way: States like Oregon and Colorado have transitioned into full operational phases. Producers are now 100% financially responsible for the lifecycle of their packaging.
The Cost of Waste: We’re seeing a shift in household logistics, with new mandatory recycling fees and larger "smart bins" becoming the norm to ensure higher recovery rates for paper and plastic.

4. Digital Product Passports (DPP)
The "Smart Packaging" trend has officially gone mainstream.
The QR Code Shift: Many brands are now using serialized QR codes or RFID tags that act as a "digital passport."
End-to-End Traceability: These tags tell recyclers exactly what chemicals are in the plastic and how it should be processed. For consumers, it provides real-time data on where their specific item was recycled.


The Bottom Line for 2026
Recycling is no longer a secondary "green" initiative; it is becoming a core industrial strategy. With major players like WM and Republic Services completing multi-billion dollar infrastructure upgrades this year, the "Circular Economy" is finally becoming a reality for American businesses.

#Recycling2026 #CircularEconomy #Innovation #SupplyChain #USARecycling

Sure, they are calling for a major Snowstorm this weekend, at least it's not raining. Look what else is dropping.

Intel’s stock just dropped 12% after-hours, proving that a solid "beat" isn't enough when the future looks foggy.

📊 The Q4 Beat vs. Q1 Miss
Q4 EPS: $0.15 (vs. $0.08 expected) — A massive beat.
Q4 Revenue: $13.7B (vs. $13.4B expected).
Q1 Guidance: Breakeven earnings on $12.2B revenue (both missing consensus).

📉 Why the Drop?

Wall Street gave Intel a "cold shower" for its forward-looking outlook:

Supply Crunch: CFO David Zinsner warned that available supply will bottom out in Q1.

Yield Hurdles: While the 18A process is ramping, manufacturing yields aren't yet "industry-leading."

High Bar: The stock was up 47% year-to-date; investors were priced for perfection and didn't get it.

The Verdict: The turnaround is a marathon, not a sprint. Manufacturing remains the bottleneck for Intel's AI ambitions.

#Intel #Earnings #Semiconductors #TechInvesting #AI

It only becomes an obstacle if you allow it to be this new work week of January 19th.

This week is a heavy-hitter for the US business landscape. Despite a quiet start with the market holiday on Monday, the back half of the week is packed with "catch-up" data following recent government scheduling shifts and the heart of Q4 earnings season.

Here is a brief breakdown of what to watch:

📉 Key Economic Indicators

Data agencies are still normalizing schedules, making this week's releases particularly critical for market sentiment:

GDP Update (Thursday, Jan 22): Expect a fresh look at growth figures. Analysts are watching to see if the 4.3% expansion pace holds steady.
Core PCE Price Index (Thursday, Jan 22): The Federal Reserve's preferred inflation gauge. A "softer" print could solidify hopes for interest rate cuts later this quarter.
S&P Global Flash PMIs (Friday, Jan 23): Real-time health checks on the Manufacturing and Services sectors to kick off 2026.
Consumer Sentiment (Friday, Jan 23): Final January figures from the University of Michigan will reveal how households feel about inflation and the job market.

💰 Earnings Season Hits Full Stride

The "old economy" meets high-tech this week. Keep an eye on:

Tuesday: Netflix (reporting after the bell; all eyes are on subscriber growth and the rumored Warner Bros. Discovery asset deal).
Thursday: Intel (key for the semiconductor outlook).
Throughout the week: Major reports from J&J, Visa, 3M, P&G, and GE Aerospace.

#BusinessNews #Economy2026 #EarningsSeason #GDP #Inflation #LinkedInBusiness #linkedinlocal #linkedinlocalhcc #influencermarketing
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Time to get a drop on your Humpday!

Speaking of drops, Yesterday, the U.S. stock market experienced its sharpest single-day Drop in months, leaving many investors wondering: Why the sudden "Sell America" trade?

The downturn, which followed the Martin Luther King Jr. Day holiday, saw all three major indexes finish deep in the red. Here is a breakdown of what drove the volatility and what it means for your portfolio.

📉 The Numbers at a Glance
Nasdaq Composite: 🔻 2.4% (22,954.32)
S&P 500: 🔻 2.1% (6,796.86) — its biggest drop since October.
Dow Jones: 🔻 1.8% (down 870 points to 48,488.59).

Yesterday, the U.S. stock market experienced its sharpest single-day decline in months, leaving many investors wondering: Why the sudden "Sell America" trade?
The downturn, which followed the Martin Luther King Jr. Day holiday, saw all three major indexes finish deep in the red. Here is a breakdown of what drove the volatility and what it means for your portfolio.
📉 The Numbers at a Glance
Nasdaq Composite: 🔻 2.4% (22,954.32)
S&P 500: 🔻 2.1% (6,796.86) — its biggest drop since October.
Dow Jones: 🔻 1.8% (down 870 points to 48,488.59).

🌍 The Catalyst: Geopolitical Tensions & "The Greenland Factor"

The primary driver of yesterday's sell-off was a sudden escalation in trade rhetoric. Over the weekend, President Trump threatened to impose new tariffs on eight European and NATO allies—including Denmark, France, and Germany—unless they support a U.S. bid to acquire Greenland.

Key Tariff Details:
10% import tax is proposed to start on February 1.
Potential increase to 25% by June 1.
Targets: Key trading partners whose combined imports to the U.S. exceed those of Mexico and China.

🚀 Safe Havens vs. Risk Assets

As investors moved into "risk-off" mode, we saw a dramatic split in asset performance:
Tech Under Pressure: The "Magnificent Seven" (including Nvidia and Amazon) led the declines as investors rotated out of high-valuation growth stocks.

Gold & Silver Records: Gold prices surged 3.7% to hit fresh all-time highs ($4,765/oz), while silver soared nearly 7%.

Volatility Spike: The VIX (Wall Street’s "fear gauge") jumped over 30%, reaching its highest level since late November.

#StockMarket #Investing #Economy #FinancialNews

I thought I should throw this up on LinkedIn this Weekend.

❄️ Major Travel Alert: 170M+ Americans Under Winter Warnings

If you have travel plans this weekend, it’s time to check your flight status. A "monster" winter storm—now being called Winter Storm Fern—is sweeping across the U.S., causing massive disruptions from the Southern Rockies to New England.

Here is the current situation for travelers and professionals:
Massive Flight Disruptions: Over 8,000 flights have already been canceled for the weekend, including nearly 3,000 today (Saturday) alone. The Dallas-Fort Worth area is currently one of the hardest-hit hubs.

Waivers in Effect: To help mitigate the chaos, American Airlines, Delta Air Lines, and most other major carriers have proactively waived change and cancellation fees.

FEMA Shifts Gear: In a significant move, FEMA has paused the ongoing dismissal of disaster workers to ensure enough "boots on the ground" are available to assist with the 170 million people currently under weather alerts.

Historic Impact: With catastrophic ice predicted from Texas to North Carolina and up to a foot of snow expected in Northeast hubs like Boston and NYC, travel is expected to be treacherous through Sunday.

Pro Tip: If your flight is delayed by 3+ hours or canceled, check your airline’s app directly. Many are offering automatic rebooking and full refunds for the unflown portion of your ticket.

Stay safe, stay warm, and keep an eye on the National Weather Service updates if you are in the storm's path.

#TravelAlert #WinterStorm #AviationNews #SupplyChain #SafetyFirst #FEMA

You would think that this may stunt your growth there, Sport.

The World Economic Forum just released a sobering look at the future of sport.

A new report, "Sports for People and Planet" (Jan 2026), reveals an industry at a crossroads. While the global sports economy is booming—currently valued at $2.3 trillion and projected to hit $8.8 trillion by 2050—that growth is under serious threat.

The $1.6 Trillion Risk
Climate change and rising physical inactivity aren't just social issues; they are existential economic risks. The report warns that these factors could wipe out $1.6 trillion in annual revenue by mid-century.

Key Takeaways:

The Growth Gap: While revenues are set to surge by 2030, the "raw materials" of sport—healthy people and a stable climate—are depleting.

The Youth Crisis: Only 1 in 5 young people currently meet daily physical activity guidelines. This isn't just a health crisis; it’s the erosion of the future fan and participant base.

Environmental Disruption: Extreme heat and pollution are already rescheduling events and impacting athlete health. By 2040, it’s estimated that only 10 countries will have enough snow to host the Winter Olympics.

The Bottom Line

The sports industry contributes more than 2% of global GDP and supports 1 in 25 jobs. Failure to innovate and prioritize sustainability could stall this entire economic engine.

To thrive, the sector must move beyond being a bystander to the climate and health crises and become a primary driver of the solution.

#SportsBusiness #Sustainability #WorldEconomicForum #ClimateAction #GlobalEconomy #SportsInnovation

It's a slippery slope to an icy landing, as a major snowstorm has been forecast for this weekend in the USA.

200 million people. 2,000 miles. 1 massive storm. ❄️

Winter Storm Fern is officially arriving this weekend, and it's bringing everything from "powdery flakes" to "crippling ice."

To my friends and colleagues in the path:

Don't be the "Hero" on the road. The ice in the South and the heavy accumulation in the Mid-Atlantic are no joke.

Communicate early. If your Monday morning is looking buried, let your clients/team know now.

Check on your neighbors. Especially the elderly or those living alone.
Stay safe out there! Who’s expecting the most snow? I’m hearing Baltimore and DC are in the "Crosshairs" this time! 🎯

#WeatherAlert #USA #WinterStorm #ProfessionalNetworking #StaySafe

From 0 to 1,000 feet in seconds. 🚀

There is nothing quite like a winch launch to remind you of the importance of momentum. In a glider, you start on the grass, completely dependent on external energy. But once you gain that initial height, you’re free to find your own lift, navigate the thermals, and see the world from a completely different perspective.

Sometimes in our careers, we need that "jolt" of a new challenge or a sudden change to get us above the treeline. It can be intense, but the view from the top is always worth the climb.

Are you looking for a "winch launch" moment in your career right now, or are you currently enjoying the glide?

#Perspective #CareerGrowth #Gliding #Innovation #Focus

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