Data Engineering

Airbyte vs AWS Glue: Which ETL Tool to Choose?

July 11th, 2025 By Sarad Mohanan in Data Engineering

Working with Salesforce, PostgreSQL, and S3? Understanding how Airbyte and AWS Glue approach ETL can save you hours of deployment time and simplify data pipeline management. While Airbyte focuses on…

Talend vs AWS Glue: Which ETL Tool Is Best for Your Data Stack?

July 11th, 2025 By Sarad Mohanan in Data Engineering

Choosing the right ETL tool for data integration can be tricky, especially when comparing widely used solutions like Talend and AWS Glue. Talend is known for its flexibility, rich connector…

Stitch vs. Hevo – A Guide to Find Out Which One is Better for Your Data Team

July 11th, 2025 By Himali Salot in Data Engineering

Most ETL tools provide beyond the basics, including connectors, scheduling, and automation. But the real test begins once your data pipelines are live - how often do they break, how…

Data Infrastructure Modernization: How to Do it the Right Way

July 4th, 2025 By Suraj Poddar in Data Engineering

Your data infrastructure is probably holding your business back right now. Legacy systems create bottlenecks, drain budgets, and leave teams frustrated with slow, manual processes that can't keep up with…

Why the Next Generation of Data Engineers Will Work With AI, Not Fight It?

July 4th, 2025 By Preethi Varma in Data Engineering

Not long ago, building a production-grade data pipeline was a multi-day effort. You’d define transformations, manage orchestration logic, tune for scale, and troubleshoot edge cases before anything was ready for…

How to Build a Scalable AI Data Pipeline: Benefits, Steps, and Challenges

June 20th, 2025 By Rajashree Bhat in Data Engineering, Data Pipeline

Every AI model requires an efficient data flow to fuel it. However, most teams struggle when building an efficient AI data pipeline. Traditional data pipelines were designed to move and…

Automated Data Processing: Definition, Benefits & Tools

June 6th, 2025 By Kamlesh in Data Engineering

Tired of wasting hours on repetitive data tasks? Scaling businesses experience complex data pipelines and large volumes of data. From data ingestion, transformation, and storage, ETL workflows can become extensive.…

Informatica vs Snowflake: Which Tool to Choose?

April 23rd, 2025 By Nitin Birajdar in Data Engineering

Nowadays, businesses heavily rely on data to make informed decisions. Choosing the right tool and data management platform can make or break the business. From small startups to large enterprises,…

Informatica vs MuleSoft: Which Data Integration Tool is Right for You?

April 23rd, 2025 By Roopa Madhuri G in Data Engineering

With growing data and business needs, having an efficient data integration tool to migrate and manage your data has become crucial. Almost every organization keeps its data in different locations,…

Build Data Pipelines with Apache Airflow: 5 Easy Steps

April 23rd, 2025 By Abhinav Chola in Data Engineering, Data Pipeline

Today with the rapid surge in information, managing data can be tricky. Moreover, trying to control and monitor all the data-related processes consumes excess resources and time, both of which…

Top 10 Data Modeling Best Practices You Simply Can’t Ignore

February 23rd, 2025 By Srishti in Data Engineering

You often find yourself caught in data complexity issues like data complexity, communication breakdowns, and data quality issues, making it tough for your teams to handle data modeling. Data modeling…

What is Data Orchestration?[+Steps, Components, Tools]

February 21st, 2025 By Asimiyu Musa in Data Engineering

This guide dives into data orchestration, its components, tools, importance, and best practices in data. It can be defined as the process or a tool that manages data-related activities. It…

Iceberg Catalogs: Key Features, Benefits, and Insights You Should Know!

February 21st, 2025 By Srishti in Data Engineering

Imagine you're managing a massive lake of data, but without a solid catalog, you find yourself: Facing difficulty in data discovery as data is scattered makes it hard to find…

How to Build a Modern Data Team Structure?

February 16th, 2025 By Khawaja Abdul Ahad in Data Engineering

It is the 21st century and you are leading a fast-growing fintech startup that is about to hit a breaking point. The data team has doubled in size over six…

What is Google Dataflow?

February 7th, 2025 By Raju Mandal in Data Engineering

Have you heard of the Google Cloud Platform, offering a completely managed stream and batch data processing service? Well, this modern data engineering technology plays an essential part in dealing…

What Is ETL Data Mapping And Why Should You Know About It?

February 7th, 2025 By Vishal Agrawal in Data Engineering

In today’s data-driven world, businesses rely solely on data to make informed decisions. For this, they need to efficiently extract, transform, and load (ETL) vast amounts of data. While ETL…

What is ETL Data Modeling? The Why’s and How’s

January 31st, 2025 By Muhammad Usman Ghani Khan in Data Engineering

Businesses rely on data to drive decisions, uncover trends, and stay ahead of the competition. But raw data is often messy, scattered across multiple sources, and difficult to analyze effectively.…

What Are ETL Pipelines? Steps, Benefits, and Use Cases

January 27th, 2025 By Radhika Gholap in Data Engineering, Data Pipeline

In today’s fast-paced digital landscape, businesses face the daunting challenge of extracting valuable insights from large amounts of data. The ETL (Extract, Transform, Load) pipeline is the backbone of data…

Streamlining Success: The Complete Guide to Data Pipeline Optimization

January 27th, 2025 By Muhammad Usman Ghani Khan in Data Engineering, Data Pipeline

Is your business incapacitated due to slow and unreliable data pipelines in today's hyper-competitive environment? Data pipelines are the backbone that guarantees real-time access to critical information for informed and…

What Is the Role of LLM in Data Engineering Evolution?

January 10th, 2025 By Gagandeep Kaur in Data Engineering

Let’s face it: Data engineering is like playing Tetris, always moving objects around to fit them into the right places. The data is never static; pipelines, schemas, transformations, workflows, and…