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: Linear Regression!

This comprehensive guide delves into the fundamentals and advanced techniques of linear regression. 

Perfect for students, data scientists, and anyone interested in understanding this crucial statistical method, this document provides detailed explanations and practical examples.

Key topics include:
- Introduction to Linear Regression: Understanding the basics and importance of linear regression in data analysis.
- Mathematical Foundations: A detailed explanation of the linear regression model, including equations and assumptions.
- Data Visualization: The Importance of Visualizing Data and Interpreting Linear Regression Results.
- Model Evaluation: Techniques for evaluating the performance of linear regression models.
- Practical Applications: Real-world examples and applications of linear regression in various fields.


Enhance your understanding of linear regression and apply this knowledge to your data analysis projects with this detailed guide!

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Dr Emmanuel O.

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

Linear Regression

Excited to share an essential resource: Python Machine Learning!

This comprehensive guide delves into the fundamentals and advanced techniques of machine learning using Python. 

Perfect for students, data scientists, and anyone looking to enhance their understanding of machine learning, this document provides detailed explanations and practical examples.

Key topics include:
- Getting Started with Python Machine Learning: Introduction to machine learning and Python, essential libraries, and the basics of data handling.
- Classification with Real-world Examples: Using the Iris dataset, visualization techniques, building and evaluating classification models, and cross-validation.
- Clustering: Finding related posts, preprocessing text data, using bag-of-words and KMeans clustering.
- Topic Modeling: Latent Dirichlet Allocation (LDA), building and evaluating topic models, and applying them to real-world datasets.
- Regression and Recommendations: Building recommendation systems, using regression models, and improving recommendations with advanced techniques.
- Computer Vision and Pattern Recognition: Introduction to image processing, pattern recognition, and building classifiers for image data.
- Dimensionality Reduction: Techniques for feature selection and extraction, principal component analysis (PCA), and other dimensionality reduction methods.
- Handling Big Data: Strategies for processing large datasets, using tools like Jug and Amazon Web Services (AWS).


Enhance your machine-learning skills and apply this knowledge to real-world projects with this detailed guide!

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Building Machine Learning Systems with Python

I am excited to share an essential resource: Exam Ref 70-761, Querying Data with Transact-SQL by Itzik Ben-Gan!

This comprehensive guide is perfect for anyone preparing for the Microsoft Exam 70-761, covering key topics in T-SQL querying and programming constructs. 

Whether you're a developer, DBA, or data analyst, this book provides the knowledge and practice you need to succeed.

Key topics include:
- Creating Transact-SQL SELECT Queries: Understanding logical query processing, filtering data, sorting data, and combining sets with set operators.
- Querying Multiple Tables Using Joins: Cross joins, inner joins, outer joins, and composite joins.
- Implementing Functions and Aggregating Data: Type conversion functions, date and time functions, character functions, CASE expressions, and aggregate functions.
- Modifying Data: Inserting, updating, deleting data, merging data, and using the OUTPUT option.
- Querying Data with Advanced T-SQL Components: Subqueries, APPLY operator, table expressions, pivoting, and window functions.
- Programming Databases Using Transact-SQL: Creating views, user-defined functions, stored procedures, error handling, transactions, and handling NULLs.


Enhance your T-SQL skills and prepare effectively for your certification with this detailed guide!

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Querying Data with Transact-SQL

Understanding SQL: How We Write It vs. How It's Executed

Have you ever wondered why your SQL queries don't always run as expected? 
- There's a difference between how we write SQL and how the database processes it. 

Let's break it down:

When we write SQL queries, we typically follow this sequence:

- SELECT
- TOP
- DISTINCT
- FROM
- WHERE
- GROUP BY
- HAVING
- ORDER BY
- LIMIT

Execution Order:
However, the database engine processes these commands in a different order:

- FROM - Identifies the data source.
- WHERE - Filter records based on conditions.
- GROUP BY - Aggregates data into groups.
- HAVING - Filter groups based on conditions.
- SELECT - Retrieves specified columns.
- ORDER BY - Sorts the result set.
- TOP/LIMIT - Limits the number of rows in the result set.

Why This Matters:
- Misunderstanding the execution order can lead to unexpected results or inefficient queries.
- Knowing the execution order helps you write more effective and optimized SQL queries.
- Improved query performance and more accurate data retrieval.

Tips for Writing Efficient SQL:
- Always start by understanding your data and defining clear requirements.
- Use the execution order to optimize your queries for better performance.
- Practice writing and analyzing different queries to see how changes affect execution.

Mastering the execution order of SQL not only makes your queries more efficient and enhances your ability to troubleshoot and optimize complex data operations.

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Querying data with SQL

Understanding the key terms in data analysis is essential for anyone working with data. 

This concise guide breaks down fundamental concepts, making it easier for you to navigate the world of data. 

Whether you're a beginner or looking to refresh your knowledge, this resource is perfect for you.

Key terms include:
- Data Profiling: Examining and summarizing data characteristics
- Data Interpretation: Making sense of data and deriving insights
- Data Manipulation: Transforming data for analysis
- Exploratory Data Analysis (EDA): Visualizing data to uncover patterns
- Data Mining: Discovering patterns in large datasets
- Data Storytelling: Conveying insights in a compelling narrative
- Data Visualization: Graphical representation for better understanding
- Data Validation: Ensuring data accuracy and reliability

Master these terms and enhance your data analysis skills!

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Data Analysis Vocabulary

Excited to share an invaluable resource: Docker Tutorial by Anthony Baire!

This comprehensive tutorial covers everything you need to know about Docker, from the basics of managing containers to advanced security and ecosystem considerations. 

Whether you're new to Docker or looking to enhance your skills, this guide provides clear and detailed insights into effective containerization practices.

Key topics include:
- Introduction to Docker and its components
- Managing Docker containers: creation, interaction, and lifecycle
- Handling inputs/outputs and data volumes
- Building and managing Docker images
- Security considerations and best practices
- Exploring the Docker ecosystem and future trends


Unlock the full potential of Docker for your projects with this detailed tutorial!

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Docker Tutorial

Struggling with Data Management? Discover SQL!

Managing and analyzing data effectively is crucial. Are you finding handling large datasets challenging or generating meaningful insights from your data?

Here’s a powerful tool that can help: SQL.

1. What is SQL?
- Structured Query Language (SQL) is a standard language for accessing and manipulating databases.
- It lets you retrieve, update, and manage data in a relational database.
- SQL is widely used across various industries for data analysis, reporting, and business intelligence.

2. Why Learn SQL?
- Efficient Data Handling: Quickly query and manipulate large datasets.
- Versatile Skills: SQL is used in various fields, from web development to finance.
- Career Growth: High demand for SQL skills in the job market.

3. Getting Started with SQL
- Basics First:
Learn fundamental commands like SELECT, INSERT, UPDATE, and DELETE.
Understand how to create and manage database tables.

- Practice with Real Data:
Use sample databases to practice your queries.
Explore platforms like Kaggle for datasets to work on.

- Advanced Techniques:
Dive into complex queries with JOIN, GROUP BY, and SUBQUERIES.
Learn about database optimization and indexing.

4. Why You Should Care:
- Problem: Handling data manually is time-consuming and prone to errors.
- Solution: SQL provides a systematic approach to data management, making it easier to analyze and derive insights.
- Value: Mastering SQL can streamline your data processes, improve accuracy, and enhance your decision-making capabilities.

SQL is an essential tool for anyone dealing with data.
Start learning today to unlock its full potential and take your data management skills to the next level.

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Basic SQL

Excited to share an invaluable resource: Comprehensive Python Cheatsheet by Jure Šorn!

This extensive cheat sheet covers the fundamental concepts and advanced techniques of Python programming. 

Perfect for beginners and experienced developers, this document provides a detailed reference for a wide range of Python functionalities.

Key topics include:
- Collections: Lists, dictionaries, sets, tuples, ranges, and more.
- Types: Strings, regular expressions, numbers, and datetime.
- Syntax: Arguments, inline functions, closures, decorators, and classes.
- System Operations: Exit, print, input, command-line arguments, file handling, and OS commands.
- Data Handling: JSON, Pickle, CSV, SQLite, bytes, and memory views.
- Advanced Topics: Threading, operators, metaprogramming, evaluation, and coroutines.
- Libraries: Progress bars, plotting, tables, web scraping, profiling, NumPy, and more.


Enhance your Python programming skills and keep this comprehensive cheatsheet as a quick reference 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/

Comprehensive Python Cheatsheet

Are you struggling to manage and process large volumes of data efficiently? 

Understanding the foundation of data engineering is key to building robust data systems that can handle today's data challenges.

These are essentials in Data Engineering;

1. Data Collection
- Collecting data from diverse sources can be chaotic and inconsistent.
- Use ETL (Extract, Transform, Load) processes to streamline data collection.
- Ensures that data is gathered in a structured and reliable manner.

2. Data Storage
- Storing vast amounts of data securely and accessibly can be daunting.
- Implement scalable data storage solutions like data lakes and warehouses.
- Provides a central repository that supports high availability and durability.

3. Data Processing
- Processing large datasets can be time-consuming and resource-intensive.
- Utilize distributed processing frameworks such as Apache Spark.
- Enables efficient and quick data processing, even with large volumes of data.

4. Data Quality
- Poor data quality can lead to inaccurate insights and decisions.
- Establish data quality checks and validation procedures.
- Ensures that your data is accurate, complete, and reliable.

5. Data Integration
- Integrating data from various sources can create inconsistencies.
-  Use data integration tools to combine data into a cohesive format.
- Facilitates seamless data analysis and reporting.

6. Data Security
- Protecting sensitive data from breaches and unauthorised access is critical.
- Implement robust security measures, including encryption and access controls.
- Safeguards your data and maintains compliance with regulatory standards.

Why It Matters:
- Without a strong foundation in data engineering, organisations struggle to manage and leverage their data effectively.
- A solid foundation ensures data is collected, stored, processed, and secured properly.
- Leads to better data insights, improved decision-making, and a competitive edge.

Understanding and applying these data engineering fundamentals can transform how you handle data and unlock its full potential.

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Foundations of Data Engineering

I am excited to share an invaluable resource: Basic SQL!

This comprehensive guide provides a deep dive into SQL fundamentals, covering everything from data definition and constraints to basic retrieval queries and set operations. 

Perfect for students, professionals, and anyone looking to strengthen their SQL skills, this document offers detailed explanations and practical examples.

Key topics include:
- SQL Data Definition and Data Types: Creating tables, specifying data types, and understanding schemas.
- Specifying Constraints in SQL: Primary keys, unique keys, foreign keys, and check constraints.
- Basic Retrieval Queries in SQL: Using SELECT statements, WHERE clauses, and basic data retrieval techniques.
- Set Operations in SQL: UNION, EXCEPT, and INTERSECT operations.


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

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Understanding Git: Your Ultimate Version Control System 🚀

Ever wondered what makes Git so essential?

Let’s break it down for you:

1/ What is Git?
↳ Git is a version control system that tracks code changes.
↳ It’s a tool used by developers to collaborate on projects.
↳ With Git, you can revert to previous versions of your project anytime.

2/ Git vs. GitHub
↳ Git is NOT the same as GitHub.
↳ GitHub is a hosting service that makes use of Git.
↳ GitHub allows developers to collaborate globally.

3/ Why Git Matters
↳ Over 70% of developers use Git!
↳ It tracks every change made to your project.
↳ It allows multiple people to work on the same project from anywhere.

4/ Key Git Features
↳ Every time a file is changed, Git tracks it.
↳ You can see the entire history of changes (commits).
↳ Git allows you to revert back to earlier versions easily.

5/ Git Branching
↳ Create separate branches to work on different features.
↳ Merge branches when the feature is complete.
↳ Switch between branches without disrupting the main project.

6/ Git Commands to Know
↳ $ git init: Initializes a new Git repository.
↳ $ git add <filename>: Stages files for commit.
↳ $ git commit -m “message”: Commits changes with a message.

7/ Pushing to GitHub
↳ After committing changes locally, push them to GitHub.
↳ $ git push origin master: Sends your code to the master branch on GitHub.
↳ GitHub hosts your project so others can contribute or review it.

Git makes collaboration easier and code management more efficient. 
- Mastering Git is essential for every developer!
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I am excited to share an essential resource: Numpy, Numerical Python!

This comprehensive guide covers the powerful capabilities of Numpy for numerical computing in Python. 

Perfect for students, data scientists, and anyone working with data, this document provides detailed explanations and examples of Numpy's functionalities.

Key topics include:
- Basics of Numpy: Arrays, nd-arrays, and their advantages over lists
- Linear algebra operations with Numpy
- Fourier Transform for signal processing
- Matrix operations: addition, multiplication, transpose, and determinants
- Creating arrays: zeros, ones, full, identity, arrange, linspace, and logspace
- Random number generation and array initialization
- Array manipulation: reshaping, slicing, indexing, and broadcasting
- Statistical functions: mean, median, standard deviation, and variance
- Advanced topics: universal functions (ufuncs), complex arithmetic, and data type abbreviations


Enhance your numerical computing skills and streamline your data processing workflows with this detailed guide on Numpy!

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Understanding NumPy

How to Build Useful Dashboards!

Creating effective dashboards is key to making data-driven decisions. 

This guide provides step-by-step instructions and best practices to help you design dashboards that deliver clear, actionable insights. 

Whether you're new to dashboard design or looking to improve your skills, this resource is for you.

Key takeaways include:

- Principles of effective dashboard design
- Choosing the right visualization tools
- Best practices for data presentation
- Tips for ensuring user-friendly and interactive dashboards

Transform your data into meaningful insights with this comprehensive guide!

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How to Build dashboards

Excited to share an essential resource: SQL Fundamentals!

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

From basic queries to advanced techniques, this document provides a thorough introduction to SQL.

Key topics include:

Introduction to SQL SELECT Statements: Learn how to retrieve data from databases using basic SELECT statements.
Basic SELECT Statement Syntax: Understand the structure and components of a basic SELECT statement, including SELECT, FROM, and WHERE clauses.
Writing SQL Statements: Guidelines for constructing clear and readable SQL statements.
Selecting All Columns and Rows: Techniques for retrieving all data from tables.
Selecting Specific Columns: How to display specific columns of a table.
Column Heading Defaults and Aliases: Customizing column headings for better readability.
Arithmetic Expressions and Operator Precedence: Using arithmetic operators and understanding their precedence in expressions.
Handling Null Values: Defining and working with null values in SQL.
Concatenation and Literal Character Strings: Combining columns and adding literal strings in SQL queries.
Eliminating Duplicate Rows: Using the DISTINCT keyword to remove duplicate rows in query results.
SQL and SQL*Plus Interaction: Overview of SQL*Plus commands and their differences from SQL statements.
Displaying Table Structure: Using the DESCRIBE command to view table structures.
Enhance your SQL skills and master database management with this detailed guide!

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Writing Basic SQL Statements

I am excited to share an insightful resource: Improving Python and Spark Performance and Interoperability with Apache Arrow by Julien Le Dem and Li Jin!

This comprehensive guide delves into optimizing Python and Spark interoperability using Apache Arrow. 

Perfect for data scientists, engineers, and anyone working with big data, this document provides a deep dive into enhancing performance and efficiency in data processing.

Key topics include:

Current Limitations of PySpark UDFs: Understanding the constraints and performance issues with existing user-defined functions.
Overview of Apache Arrow: Introduction to Arrow, its goals, and benefits for in-memory columnar storage.
Performance Improvements: Real-world examples and benchmarks showcasing significant speedups in PySpark integration.
Vectorized UDFs: Leveraging pandas DataFrames for efficient data processing and reducing overhead.
Group UDFs: Techniques for improving group-based operations and data normalization.
Future Roadmap: Upcoming enhancements and support for Arrow in the Apache ecosystem.


Enhance your data processing workflows and achieve higher performance with this detailed guide on Apache Arrow and PySpark!

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Improving Python and Spark Performance and interoperabi

Pre-Interview Checklist for Big Data Engineer Roles.

1. SQL Essentials:
- SELECT statements, including WHERE, ORDER BY, GROUP BY, HAVING
- Basic Joins: INNER, LEFT, RIGHT, and FULL
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries, Common Table Expressions (WITH clause)
- CASE statements, advanced JOIN techniques, and window functions (OVER, PARTITION BY, ROW_NUMBER, RANK)

2. Python Programming:
- Basic syntax, control structures, data structures (lists, dictionaries)
- Pandas & NumPy for data manipulation: DataFrames, Series, groupby

3. Hadoop Ecosystem Proficiency:
- Understanding HDFS architecture, replication, and block management.
- Mastery of MapReduce for distributed data processing.
- Familiarity with YARN for resource management and job scheduling.

4. Hive Skills:
- Writing efficient HiveQL queries for data retrieval and manipulation.
- Optimizing table performance with partitioning and bucketing.
- Working with ORC, Parquet, and Avro file formats.

5. Apache Spark:
- Spark architecture
- RDD, DataFrame, Datasets, and Spark SQL
- Spark optimization techniques
- Spark Streaming

6. Apache HBase:
- Designing effective row keys and understanding HBase’s data model.
- Performing CRUD operations and integrating HBase with other big data tools.

7. Apache Kafka:
- Deep understanding of Kafka architecture, including producers, consumers, and brokers.
- Implementing reliable message queuing systems and managing data streams.
- Integrating Kafka with ETL pipelines.

8.Apache Airflow:
- Designing and managing DAGs for workflow scheduling.
- Handling task dependencies and monitoring workflow execution.

9. Data Warehousing and Data Modeling:
- Concepts of OLAP vs. OLTP
- Star and Snowflake schema designs
- ETL processes: Extract, Transform, and load
- Data lake vs. data warehouse
- Balancing normalization and denormalization in data models.

10. Cloud Computing for Data Engineering:
- Benefits of cloud services (AWS, Azure, and Google Cloud)
- Data storage solutions: S3, Azure Blob Storage, and Google Cloud Storage
- Cloud-based data analytics tools: BigQuery, Redshift, and Snowflake
- Cost management and optimization strategies

If I had to start learning #dataengineering all over again

(I will follow this plan, mostly in order)

1. Learn SQL
— Aggregations with GROUP BY
— Joins (INNER, LEFT, FULL OUTER)
— Window functions and Common table expressions

2. Learn about data modeling
— read about data normalization and 3rd normal form
— read about fact, dimension, and aggregate tables
— read about efficient table designs such as cumulative

3. Learn Python
— Learn loops, if statements
— Try out some really solid libraries like Pandas, numpy, scikit-learn
— Will not waste time on Machine Learning

4. Learn about data quality
— how do you write a good data check?
— how can you implement the write-audit-publish pattern in your pipelines?

5. Learn about distributed compute, and job orchestration
— Read about MapReduce 
— learn about partitioning, skew, and spilling to disk
— try a scheduler like Airflow

If you follow this, you’ll feel way more confident in interviews
#DataEngineering

I am excited to share an insightful resource: Data Engineering Teams: Creating Successful Big Data Teams and Products by Jesse Anderson!

This comprehensive guide explores the intricacies of building and managing data engineering teams, essential for tackling the challenges of big data. 

Whether you're a manager, VP, or individual contributor, this book provides valuable insights and strategies to ensure your data projects succeed.

Key topics include:
- The need for data engineering in the era of big data
- Essential skills required in a data engineering team
- The roles and responsibilities of data engineers
- Effective collaboration with data science and data warehousing teams
- Building and managing data pipelines
- Strategies for running successful data engineering projects
- Overcoming common pitfalls and challenges in big data projects
- Steps to creating robust big data solutions


Enhance your understanding of data engineering and lead your team to success with this detailed guide!

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Creating a Successful Data Engineering Teams

25 Most Common Interview Questions for Data Analyst Positions 🚀

I will hire you if you can answer these questions.

Here are 25 key questions to help you prepare:

Fundamentals:
1.	What is data analytics, and why is it important for businesses?
2.	Explain the difference between structured and unstructured data.
3.	What are the key steps in the data analysis process?
4.	How do you handle missing or corrupted data in a dataset?
5.	What are the key differences between quantitative and qualitative data?

Data Tools & Techniques:
6.	What is the role of SQL in data analysis?
7.	What are some key Excel functions used in data analysis?
8.	Explain the use of JOINs in SQL.
9.	What is data normalization in databases, and why is it important?
10.	How would you explain the concept of a relational database to someone new?

Data Visualization:
11.	Why is data visualization important in data analysis?
↳ Helps present complex data clearly to stakeholders.
12.	What are some popular data visualization tools?
↳ Mention Tableau, Power BI, and Excel as key tools.
13.	When would you use a bar chart vs. a line chart?
↳ Bar charts for comparing categories, line charts for trends over time.
14.	What are some best practices for creating a dashboard?
↳ Focus on clarity, simplicity, and relevance to the audience.
15.	Explain the use of heatmaps in data visualization.
↳ Heatmaps represent data using colour gradients to show patterns or density.

Statistical Concepts:
16.	What is the difference between correlation and causation?
↳ Correlation shows a relationship, while causation implies one variable causes the other.
17.	Explain the concept of standard deviation.
↳ Measures the amount of variation or dispersion in a dataset.
18.	What is the p-value in hypothesis testing?
↳ The p-value helps determine the significance of your results.
19.	How do you determine if a dataset is normally distributed?
↳ Use histograms, Q-Q plots, and skewness/kurtosis measures.
20.	What is A/B testing, and how is it used in decision-making?

Practical Implementation:
21.	How do you clean and prepare data for analysis?
↳ Explain steps like handling missing data, removing duplicates, and outlier treatment.
22.	What are the challenges of working with large datasets?
↳ Mention issues like performance, storage, and data quality.
23.	How do you handle outliers in your data?
↳ Depending on the context, you can remove, cap, or transform outliers.
24.	Explain a situation where you identified a problem and solved it using data.
↳ Be prepared to walk through a real-life example with clear steps.
25.	How do you ensure data accuracy and integrity throughout your analysis?
↳ Double-check data entry, validate sources and perform consistency checks.

________________________________________

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So more data analysts can prepare for interviews!

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Good luck with the interviews! 💼

An aspiring Data Engineer aspirant asked me a question.

Why is PySpark so important to learn?

I showed them this list:

- PySpark + DataFrame API = Data Manipulation
- PySpark + RDD = Distributed Datasets
- PySpark + filter() = Data Filtering
- PySpark + join() = Data Integration
- PySpark + groupBy() = Data Aggregation
- PySpark + orderBy() = Data Sorting
- PySpark + union() = Combining Datasets
- PySpark + withColumn() = Data Transformation
- PySpark + select() = Column Selection
- PySpark + SQL Queries = SQL Integration
- PySpark + createOrReplaceTempView() = Virtual Tables
- PySpark + map() = Data Mapping
- PySpark + reduceByKey() = Data Reduction
- PySpark + partitionBy() = Data Partitioning
- PySpark + broadcast() = Data Broadcasting
- PySpark + accumulators = Shared Variables
- PySpark + Spark SQL = Structured Data
- PySpark + DataFrame Caching = Performance Optimization
- PySpark + Window Functions = Advanced Analytics
- PySpark + UDFs = Custom Functions
- PySpark + Machine Learning = Scalable Models
- PySpark + GraphX = Graph Processing
- PySpark + Streaming = Real-Time Processing
- PySpark + DataFrame Joins = Efficient Merging
- PySpark + MLlib = Machine Learning
- PySpark + Structured Streaming = Continuous Processing
- PySpark + Pipeline API = Workflow Automation
- PySpark + Delta Lake = Reliable Lakes
- PySpark + Databricks = Cloud Platform
- PySpark + ETL Pipelines = Data Extraction
- PySpark + Performance Tuning = Query Efficiency
- PySpark + Cluster Management = Distributed Computing

With consistency and effort, you will get there.

LIKE and SHARE, if you can relate to this post.

LinkedIn Growth Hacker✌️ ||Believe in Smart work💥 || Content Creator🖋️|| Believe in Opportunities💫|| Adding a little spark✨

LinkedIn Growth Hacker | Brand Builder | Marketer | ML Engineer | Public Speaker | Featured at Times Square

Most LinkedIn profiles get views. Few get growth.

I help creators, professionals, and brands turn their LinkedIn presence into a growth engine, attracting visibility, opportunities, and authority.

Many people have incredible skills or products, but their profiles don’t reflect their value. I change that.

I’ve scaled my own LinkedIn to 130,000+ followers, not with luck, trends, or gimmicks, but with a proven system of growth-focused content, strategic branding, and audience-first engagement.

Now, I help others do the same. My focus is on amplifying your authority, building a strong personal brand, and growing your audience, so you become the go-to name in your niche.

What I do:

✅ LinkedIn Growth Strategy 
✅ Content Creation & Branding 
✅ Brand Collaborations & Campaigns 
✅ Profile Optimization

If you want to scale your LinkedIn, grow your influence, and turn your brand into a visible authority, let’s connect.

➡️ DM me or email: v.toshniwal2828@gmail.com

Always open to meaningful conversations, collaborations, and opportunities in AI, ML, and tech-driven products.

Currently seeking Tech / Machine Learning Engineer opportunities.

Vidhi Toshniwal

My developer quoted me ₹5,00,000
for a simple internal app.

All I wanted was:

➡️ Track my clients’ LinkedIn post performance
➡️ See engagement data clearly
➡️ Monitor growth trends
➡️ Generate clean reports

Nothing complex.
Just clarity.

₹5 lakh.

For a minute, I convinced myself…
“Maybe that’s the cost of building software.”

Then, while scrolling LinkedIn, I discovered Replit.

I decided to try it.

That decision saved me ₹5,00,000.

No servers.

I described what I wanted.

And I had a working, full-stack app.

Not a mockup.
Not slides.

A real app with logic, data storage, authentication, ready to use.

That’s when I understood something powerful:

Replit isn’t just for experimenting.

It supports the full journey:

👉 Start — Turn an idea into something real, fast.
👉 Build — Everything in one place.
👉 Scale — Production-ready, secure, built to grow.

Biggest realization?

We don’t always need a full tech team to validate an idea.
Sometimes we just need the right platform.

₹5 lakh saved.
One idea shipped.

If you’re sitting on an idea because “it’s too expensive”,

Maybe it’s not.

Maybe you just haven’t started yet.

➡️ https://lnkd.in/gg9cPEwR

P.S: I have already saved 5 Lakh Rs.  
Now it's your turn 😎

I used to type everywhere.

On the bus.
At the cafe.
Even in random co-working spots.

My brain would move faster than my fingers. Mistakes. Backspaces. Endless edits.

Then I tried something different.

I started speaking instead of typing.

With Wispr Flow, everything I say turns into perfectly formatted text, emails, Slack messages, notes, even code. It works across every app I use.

➡️ 500ms latency
➡️ 85% zero-edit rate
➡️ Emoji commands & whisper mode
➡️ Works in 70+ apps

72% of my work today? Voice-only. My keyboard barely gets touched anymore.

And now, Android users can finally join in. Same speed, same accuracy, same magic.

Work anywhere. Speak naturally. Get more done.

My keyboard? Officially retired.

Try it yourself and see how much faster you can get things done.

https://wisprflow.ai

Wispr Flow

Wispr Flow 

A mentor once told me,

“Invest in yourself early. The returns compound for life.”

At that time, I didn’t fully understand it.
Now I do.

We spend years working hard…
But hesitate when it comes to upgrading our own skills.

Here’s the truth:

𝙊𝙥𝙥𝙤𝙧𝙩𝙪𝙣𝙞𝙩𝙞𝙚𝙨 𝙙𝙤𝙣’𝙩 𝙬𝙖𝙞𝙩.
𝙎𝙠𝙞𝙡𝙡𝙨 𝙘𝙧𝙚𝙖𝙩𝙚 𝙩𝙝𝙚𝙢.

This Holi season, instead of just adding color to your clothes…
Add value to your career. 🎨

Right now, Coursera Plus Annual is available at ₹7,499 (instead of ₹13,999) until 9th March.

Access to 10,000+ programs.
Top universities.
Industry experts.
One subscription.

We always say “I’ll start learning soon.”

Soon becomes next month.
Next month becomes next year.

And suddenly we’re behind.

Building your future is important.
But building the right skills is what makes that future powerful.

This could be the smartest ₹7,499 you invest this year.

If growth is on your mind, don’t ignore this.

Here’s the link:
https://lnkd.in/gZ9P499A

Let’s upgrade ourselves 🚀

Building a 24/7 financial assistant used to sound like something only Wall Street firms could afford.

Now anyone can do it in minutes.

I recently built a simple Autonomous Financial Intelligence System using Kimi Claw.

Just a smart AI agent that:

➡️ Tracks financial data
➡️ Generates automated reports
➡️ Sends insights on schedule
➡️ Remembers preferences over time

Instead of constantly checking dashboards and market updates… the system brings insights to me.

A few things I love about it:

✓ Runs 24/7 in the cloud
✓ Generates weekly and daily financial reports
✓ Uses thousands of built-in skills for analysis
✓ Stores data and past insights with persistent memory

The carousel breaks down the exact steps I used to build it with Kimi Claw.

Now it's your turn.

Do check out
➡️ https://www.kimi.com/bot

Kimi AI 

Google just reset the AI career ladder.

One certificate.
Built for real work.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗔𝗜 𝗰𝗼𝘂𝗿𝘀𝗲.
𝗜𝘁’𝘀 𝗮 𝗰𝗮𝗿𝗲𝗲𝗿 𝘀𝗮𝗳𝗲𝘁𝘆 𝗻𝗲𝘁.

That gap?
It’s widening every single month.

𝗧𝗛𝗜𝗦 𝗜𝗦 𝗧𝗛𝗘 𝗗𝗜𝗙𝗙𝗘𝗥𝗘𝗡𝗖𝗘.

𝗚𝗢𝗢𝗚𝗟𝗘 𝗔𝗜 𝗣𝗥𝗢𝗙𝗘𝗦𝗦𝗜𝗢𝗡𝗔𝗟 𝗖𝗘𝗥𝗧𝗜𝗙𝗜𝗖𝗔𝗧𝗘

➡️ 6 short, practical courses
➡️ 20+ hands-on labs
➡️ A final project where you build a real AI solution

You’ll work with tools used in real jobs:

– Google AI tools
– Gemini
– NotebookLM
– AI Studio

Why this matters (a lot):
AI isn’t “nice to have” anymore.
It’s becoming baseline skill expectation.

This certificate:

✓ Is built from real job-market data
✓ Teaches how to apply AI, not just talk about it
✓ Ends with a Google-backed certificate employers actually trust

If you’re a professional in 2026 and
AI is still “optional” for you, the market has already decided otherwise.

Learn how to build with AI, not compete against it.

👉 Start here: https://lnkd.in/gRUcR9QP

AI won’t take your job.
But someone who knows how to use it might.

#ai

I think we’re underestimating what AI agents can actually do.

I watched Abacus AI Deep Agent take a simple prompt and build a full working app in minutes.

Not slides. Not concepts.
An actual product with frontend, backend, database, and logic already connected.

That’s when it clicked for me.
👉 http://deepagent.abacus.ai

Here’s what stood out:

• FULL-STACK APPS FROM ONE PROMPT
UI, backend, databases, and forms built together in one flow.
And now mobile apps too. Describe your idea, and Deep Agent builds a full app ready to publish.

• LAUNCH AN ONLINE STARTUP IN MINUTES
Landing pages, login systems, payments, and data storage created in the same build.

• COMPLEX SOFTWARE & SAAS WITH SIMPLE TEXT
You explain how it should work.
Deep Agent handles the structure and builds it.
CRMs, internal tools, Notion-style systems, and HR software usable tools.

• MICRO-SAAS IDEAS THAT FEEL REALISTIC
Resume tools, niche utilities, and lead magnets are easier to test and launch.

• APPS & GAMES CREATION
Sudoku, Minecraft-style builds, Conway’s Game of Life, and interactive tools.

• CREATE YOUR OWN CHATGPT-STYLE APPS
Founder assistants, workout planners, research helpers, and domain-specific AI apps.

• REPORTS, DECKS & PRESENTATIONS
Charts, visuals, structured insights - ready quickly.

• SPREADSHEETS → INTERACTIVE DASHBOARDS
Upload Excel or CSV files and get metrics and visual breakdowns.

• IMAGE & VIDEO GENERATION BUILT IN
Work with text, PDFs, images, videos, and code in one place.

• TURN RESEARCH PAPERS INTO AUDIO
Deep Agent converts research into listenable conversations.

• DEEP AGENT BROWSER EXTENSION
Describe a workflow, and it executes it step by step.

For founders, this speeds up idea testing.
For teams, it changes how work gets executed.

👉 Abacus AI Deep Agent
http://deepagent.abacus.ai

I thought people were exaggerating when they said “AI agents change everything.”

Then I watched one quietly build an app from scratch.

In a few minutes, it had the frontend, backend, database, and logic in place.
Nothing fancy on the surface - it just… worked.

This is Abacus AI Deep Agent 
An AI agent that handles execution the way a small engineering team would.

👉 http://deepagent.abacus.ai

Here’s what really stood out:

➡️ FULL-STACK APPS FROM ONE PROMPT
UI, backend, databases, and forms created together in one flow.
And now mobile apps too. Describe what you want, and Deep Agent builds a full app you can publish.

➡️ LAUNCH AN ONLINE STARTUP IN MINUTES
Landing pages, login systems, payments, and data storage are handled together instead of pieced apart.

➡️ COMPLEX SOFTWARE & SAAS USING SIMPLE TEXT
You explain how it should look and work.
Deep Agent figures out the structure and builds it.

CRMs, internal tools, Notion-style systems, HR software built for real usage.

➡️ MICRO-SAAS IDEAS THAT FEEL POSSIBLE
Resume tools, niche utilities, and lead magnets ideas that used to feel heavy now feel worth trying.

➡️ APPS & GAMES GENERATED QUICKLY
Sudoku, Minecraft-style games, Conway’s Game of Life, and interactive tools - ready to use as soon as they’re created.

➡️ BUILD YOUR OWN CHATGPT-STYLE APPS
Founder assistants, workout planners, diet tools, research helpers, domain-specific AI tools.

➡️ REPORTS, DECKS & PRESENTATIONS
Charts, visuals, and structured insights put together in minutes.

➡️ SPREADSHEETS → INTERACTIVE DASHBOARDS
Upload Excel or CSV files and get metrics, explanations, and visuals automatically.

➡️ IMAGE & VIDEO GENERATION BUILT IN
Work with text, PDFs, images, videos, and code in the same place.

➡️ TURN RESEARCH PAPERS INTO AUDIO
Deep Agent turns long papers into audio conversations you can actually listen to.

➡️ DEEP AGENT BROWSER EXTENSION
Describe a workflow, and it executes each step automatically with no manual setup.

For founders, this changes how fast ideas can be tested.
For teams, it changes how work gets distributed.

Worth keeping an eye on.
👉 Abacus AI Deep Agent
http://deepagent.abacus.ai

Cancel your AI subscriptions.

I know that sounds dramatic. But I actually did it after 2 years of paying for almost every AI tool out there.

I was opening ChatGPT for writing, research, and summaries.
Gemini for images.
Kling for videos.
Another tool for vibe-coding apps.
Another one for presentations.

Nothing was wrong with the tools. The problem was fragmentation.

Then I moved everything into ChatLLM by Abacus hi AI. - 

One place for all major AI models
GPT-5.2, Claude 4.6, Gemini 3 Pro, Grok 4.1 Fast, DeepSeek, LLaMA, same workspace, same context.

Images and videos without switching products
Nano Banana Pro, FLUX, Seedream, Ideogram for images.
Sora, VEO, Kling, Runway, Luma, Grok Imagine for videos.

Full-stack apps, websites, and SaaS from prompts
Type an idea and see it live. Layout, colors, copy, and logic all editable.

Presentations that don’t feel empty
Simple inputs turn into structured decks with research, charts, and flow.

An assistant who keeps working in the background
Tasks, searches, and workflows progress even when I’m not active.

PDFs become interactive
Upload documents, ask questions, and extract notes instead of reading line by line.

AI text that sounds human
Generate, humanize, choose a tone, and use it as-is.

Real-time web research
Answers come with sources, visuals, and context.

Deep Agent for real outputs
Apps, reports, dashboards, workflows, micro-SaaS.

A code editor that actually supports building
Write, run, debug, and improve without chaos.
At some point, it stopped feeling like using tools.
It felt like working inside one system.

That’s why I cancelled everything else.  Try it now, it’s only $10 a month - http://chatllm.abacus.ai

I was flipping through a magazine when a full-page ad made me pause.

“The best camera we’ve ever made.”

Naturally, I assumed it was a camera brand.
The product styling, the setup, the confidence in the line, everything pointed to a serious professional camera.

Then I opened the page.

The camera quietly slipped into a pocket.

And the line read:
“It’s also a smartphone.”

That small reveal changed the entire story.

What looked like a professional camera ad was actually showcasing the Xiaomi 17 Ultra, a smartphone built around a camera-first philosophy, co-engineered with Leica.

It’s a clever reminder of how far smartphone photography has come.

Features like advanced imaging tech, innovations such as LOFIC and 200MP continuous optical zoom, and the optional Photography Kit Pro are pushing the boundaries of what we expect from a phone.

What I appreciated most wasn’t just the product story, it was the storytelling.

In a time where most ads compete for attention on screens, this print execution creates a genuine “aha moment.”

One simple twist turns curiosity into admiration.

Smart idea.
Unexpected reveal.
Memorable storytelling.

#Xiaomi17Ultra

As a creator, I see hundreds of brand campaigns every week.

Most of them? Copy-paste energy.
Bold claim. Pretty visual. Quiet fine print.

But this one actually made me stop scrolling.

POCO just launched something called the Fastest BS Service.

The idea, since their X8 Pro Series needs zero disclaimers, they'll help other brands hide theirs. They brought in India's fastest rapper, Gaush, to speed-rap competitor fine print so fast nobody can understand it.

The brands winning with Gen Z aren't the loudest ones.
They're the most honest ones.

POCO didn't just launch a phone.
They launched a mirror at the entire industry.

9000mAh battery. Dimensity 9500. 3Mn+ Antutu score.
No asterisks. No conditions.

All Power. No BS.

Honestly the kind of campaign I wish I could say I ideated. 😭

📍 https://lnkd.in/gwqUC5eQ

Yaar koi mujhe batao how is Manyavar so good at this??

They just dropped a Wedding Music Video with Vijay and Rashmika and I am not okay. 

The video is SO cute, the chemistry is insane, and Amit Trivedi ka music? Chef's kiss.

But also, let's talk about the big brain energy here. Manyavar didn't make an ad. They made a wedding film that people actually want to watch and share. No voiceover or "buy now." Just a beautiful story about a wedding that almost  just happened.

They literally created a cultural moment.

The timing is also wild. When Vijay and Rashmika are the hottest topic on the internet, Manyavar already had this in the bag. That's not coincidence, that's someone on their team who really understands the pulse.

Bohot din baad kuch aisa dekha jisme brand feel hi nahi hua. 

And that's the biggest compliment you can give a marketing team.

Vedant Fashions Limited - Manyavar-Mohey 

#Manyavar #WeddingMusicVideo #VijayRashmika

When I was younger, I used to get irritated when my nani asked too many questions.

“How does this work?”
“Who decides this?”
“What happens if it fails?”

At the time, it felt unnecessary.
Today, it feels like wisdom.

Every AI system I’ve seen break in production broke for the same reason:
no one truly understood it.

It worked in demos.
It impressed in meetings.
But when something went wrong, there were no clear answers, only guesses.

The strongest teams I’ve worked with build differently.

They slow down first.
They analyze intent before writing logic.
They design how agents interact, not just what they output.
They build systems they can explain without slides.

That approach is exactly what Architect enables.

It follows the same discipline:
analyze → orchestrate → build.

Nothing hidden.
Nothing assumed.
Just glass-box transparency from the start.

Funny how nani’s questions ended up becoming a blueprint for how AI should be built.

I’ve been exploring Architect and seeing how it applies these principles to real workflows:
https://lnkd.in/g6grt-M6

I never thought that a task as simple as booking a househelp could teach me a business lesson.

Last week, I tried Urban Company for the first time.

Service done. Payment done. I thought that was it.

Then the app asked:
“Do you want to book the same professional next time?”

A tiny question… but a genius idea.

Because in service businesses, the hardest part isn’t getting a customer once.
It’s making them come back.

Urban Company quietly solves that problem. The next time you open the app, the professional you already trust appears as a preferred option.

Just comfort and familiarity.

Most companies chase the first sale.
The smart ones? They make the second sale effortless.

Small product decisions = big retention wins.

P.S: What’s one small feature in a product or service that made you come back again and again?

I was reading an article this morning, and one thing just stopped me, the new Tata Punch.ev has been revealed at a starting price of Rs 9.69 lakh (ex-showroom). And once I went down that rabbit hole, I couldn't stop.

Now I follow car launches quite closely, and the EV space especially has been fascinating to watch. But what Tata has done with the new Punch.ev genuinely surprised me.

The safety package alone is something else. Over 60 safety features, including Hill Ascent and Descent Control, Electronic Stability Program, Hill Hold Control, ISOFIX child seat anchors, and even an SOS calling function built right in. That's not just car safety, that's peace of mind at a price most people wouldn't expect.

Then there's the connected car experience through IRA.ev, which comes standard across all variants. We're talking about real-time charge status, remote diagnostics, geo-fencing, valet mode, and alerts for everything from an open door to an abrupt charge cut-off. And if that wasn't enough, you can control the car from your smartwatch, lock, unlock, switch the AC on, and precool the cabin before you even walk to the parking lot.

On the comfort side, a voice-assisted electric sunroof, cooled glovebox, 90-degree door opening, and 366L of boot space are the kind of everyday touches that make ownership genuinely enjoyable rather than just practical.

A lifetime battery warranty on the 40 kWh pack ties it all together. Tata isn't just selling a car here; they're selling safety and confidence.

The more I read, the more I felt this isn't just a good EV. It's a genuinely good car.

I stopped paying for most of my AI tools.

Not because they were bad.
Because using them together became exhausting.

Every task meant opening a different product.

One tool for writing and research.
Another for images.
Another for video generation.
Another for building apps.
Another for presentations.

The tools were powerful. The workflow was messy.

That’s where Abacus AI changed the workflow.
https://abacus.ai/ 

Start for as low as $7!!
Instead of jumping across tools, everything runs inside one system.

Here’s what that looks like:

1. Multiple frontier models in one workspace

GPT-5.4, Gemini 3.1 Pro, Claude 4.6, Grok 4.1, DeepSeek V3.2 and others available inside the same workspace.

2. AI video generation

Use models like Kling 3.0, Sora 2, Luma, Runway, Wan 2.5, and Veo 3.1 to generate videos from prompts.

3. AI image generation

Create visuals using Nano Banana Pro, Seedream 4.5, FLUX, Ideogram, Recraft and ChatGPT Image Gen.

4. Humanize AI content

Turn AI-generated text into natural, human-sounding writing and adjust tone easily.

5. Build full apps and websites

Type a prompt and generate full-stack apps, websites, or small SaaS projects.

6. AI-generated presentations

Provide a topic and generate structured slides with research, charts, and flow.

7. Secure OpenClaw agents

Create a personal Telegram agent that can handle emails, schedule meetings, book tickets, and connect with tools securely.

8. AI coding environment

A desktop workspace with a CLI, AI code editor, and assistant to build and run projects without switching tools.

9. DeepAgent for real outputs

Generate research reports, dashboards, apps, travel plans, workflows, and more from a single prompt.

At some point, it stopped feeling like using separate AI tools.

It felt like working inside one system.
Try now 👉🏻 https://abacus.ai/  Start as low as $7 - Do everything with AI

A B2B fintech brand just made me stop scrolling. That doesn't happen often.

Cashfree Payments dropped "One of Your Kind" and it is nothing like what you expect from a payments company.

No product pitches. No feature talk. Just the real moments behind building BigBasket, Third Wave Coffee, RedBus, Heads Up For Tails and more.

As someone who lives and breathes brand storytelling, this is how you build emotional equity in a space everyone treats as a commodity.

Every business builds its own way. The infrastructure behind it should understand that.

This is what intentional brand building looks like.

Shoutout to Akash S. and Reeju Datta for bringing this story to life. 👏

#CashfreePayments #OneOfYourKind

For over a decade, Cashfree Payments has powered payments for 1 million+ businesses across India.

Working closely with startups, D2C, SaaS, travel and growing businesses reveals something quickly.

No two successful businesses operate the same way - each is one of a kind. 
Some brands scale through flash sales.
Some run marketplaces with multiple sellers.
Some deal with COD orders and returns.
Some start selling globally from day one.

Every business solves problems in its own way. And their payments need to work the same way.

At Cashfree Payments, we build payments around how businesses actually operate and what they need. Whether you are processing your first hundred orders or scaling to millions of transactions.

Cashfree Payments. One of Your Kind.

bigbasket | | redBus | Heads Up For Tails | Third Wave Coffee | Prashanti Sarees

#OneOfYourKind

Today’s social media scroll came with a pleasant surprise.

I came across RADHA Diamond and Gold Jewellery, and for a moment it felt like the jewellery brand KISNA diamond and Gold Jewellery had introduced a new identity.

But the idea behind it was far more meaningful and unique.

For Women’s Day, the brand chose to rename itself, placing a feminine power at the forefront as a tribute to the strength, grace and wisdom.

It’s a subtle gesture, but one that speaks volumes.

Because sometimes, the most powerful tributes simply place women where they truly belong, at the heart of it all.

#InternationalWomensDay #HonouringHer #TodayIAmRadha #KISNADiamondAndGoldJewellery #CelebratingWomanhood

taplio

The Battle

Dr Emmanuel Ogungbemi

Vidhi Toshniwal

Followers

Posts

Engagements

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Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance

Performance ScoreVidhi Toshniwal's LinkedIn Performance

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taplio

The Battle

Dr Emmanuel Ogungbemi

Vidhi Toshniwal

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Dr Emmanuel Ogungbemi's LinkedIn Performance

Performance ScoreVidhi Toshniwal's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance