DEV Community: Parth Maniar

⚡ Batch vs Streaming: The Future of Data Pipelines

Parth Maniar — Tue, 09 Sep 2025 15:00:08 +0000

The new oil is data, but processing that data is just as important as gathering it. Batch processing and streaming processing are two of the most popular methods in data engineering. Although each has a role, the decision between them is influencing how data pipelines will develop in the future.

🔹Batch processing: what is it?

Batch processing is the process of gathering data over time and processing it all at once.

How it operates: "Batches" are used to group and process data.
Commonly utilized tools include Spark (batch mode), AWS Glue, and Apache Hadoop.
Ideal for: Monthly financial summary, daily dashboards, and extensive reporting.

✅ For instance, creating a daily sales report at midnight that compiles all of the day's transactions.

🔹What is Processing Streaming?

Data is processed in real-time (or almost real-time) as soon as it is generated, thanks to streaming processing.

How it operates: Data moves through the pipeline continually.
Commonly used tools include Spark Streaming, Apache Flink, and Apache Kafka.
Ideal for: IoT device monitoring, fraud detection, and real-time suggestions.

✅ As an example, Netflix will suggest a movie as soon as you're done watching it.

Feature	Batch Processing 🗂️	Streaming Processing ⚡
Speed	Hours → Days	Milliseconds → Seconds
Use Cases	Reports, analytics	Real-time decisions, alerts
Complexity	Easier to implement	Harder (needs infra + scaling)
Cost	Often cheaper	Can be expensive for large scale

🔹 Which One is the Future?

The reality is: both batch and streaming will continue to coexist.

Companies will rely on batch for regular analytics and reports.
They’ll use streaming for time-sensitive insights (like fraud prevention or live dashboards).
Increasingly, modern data pipelines are becoming hybrid, using both approaches together.

🚀 Final Thought

The future of data pipelines isn’t about choosing batch or streaming — it’s about knowing when to use each.

Use batch for efficiency and scale.
Use streaming when time is critical.

As data grows faster than ever, engineers who master both approaches will shape the future of how businesses make decisions.

🔄 ETL vs ELT: The Backbone of Data Engineering

Parth Maniar — Fri, 29 Aug 2025 19:01:34 +0000

In the world of Data Engineering, two terms come up all the time: ETL and ELT. While they sound similar, they represent two different approaches to moving and transforming data. Understanding them is essential for anyone stepping into data engineering.

📌 What is ETL?

ETL = Extract → Transform → Load

Extract data from source systems (databases, APIs, logs).
Transform it (clean, filter, aggregate) into a usable format.
Load it into a data warehouse for analysis.

🛠️ Example: Traditional systems like Informatica, Talend, and SSIS rely heavily on ETL.
✅ Best for: When transformations are complex and need to be done before storage.

📌 What is ELT?

ELT = Extract → Load → Transform

Extract data from source systems.
Load it directly into the data warehouse or lake.
Transform it there, using the power of the warehouse itself.

🛠️ Example: Modern cloud warehouses like Snowflake, BigQuery, and Redshift support ELT.
✅ Best for: When storage is cheap and scalable, and transformations can be pushed downstream.

⚖️ ETL vs ELT: Key Differences

Aspect	ETL 🛠️	ELT ☁️
Process Order	Transform before storage	Transform after storage
Best For	On-premise systems	Cloud-based warehouses
Speed	Slower for big data	Faster, uses warehouse compute
Flexibility	Limited scaling	Highly scalable & flexible

🚀 Why Does This Matter?

Choosing between ETL and ELT depends on your infrastructure and use case.

Legacy systems still depend on ETL.
Modern cloud-first companies lean toward ELT for flexibility and scalability.

👉 The key takeaway: Data Engineers must understand both approaches — and know when to apply each.

✨ Closing Thought

Whether it’s ETL or ELT, the goal remains the same: make data clean, reliable, and analytics-ready. The real power lies in using the right approach at the right time.

[Boost]

Parth Maniar — Fri, 29 Aug 2025 15:49:42 +0000

🏗️ The Role of a Data Engineer: Beyond Pipelines

Parth Maniar ・ Aug 29

#datascience #dataengineering #bigdata #career

🏗️ The Role of a Data Engineer: Beyond Pipelines

Parth Maniar — Fri, 29 Aug 2025 15:38:38 +0000

When most people think of Data Engineers, they picture someone building pipelines to move data from one place to another. While that’s part of the job, the role of a Data Engineer is far more critical — and evolving rapidly in today’s data-driven world.

🔹 What Does a Data Engineer Do?

At the core, a Data Engineer is responsible for making data reliable, accessible, and usable for analysts, data scientists, and business teams. Their work often includes:

Designing and maintaining data pipelines
Building and optimizing data warehouses and data lakes
Ensuring data quality and governance
Supporting real-time and batch data processing
Collaborating with stakeholders to make data-driven decisions possible

🔹 Data Engineer vs Data Scientist

It’s easy to confuse these two roles, but they focus on different parts of the data ecosystem:

Data Engineer: Builds the infrastructure, pipelines, and tools to make data available.
Data Scientist: Uses that data to build models, run analysis, and generate insights.

👉 Simply put: Data Scientists are only as good as the data pipelines provided by Data Engineers.

🔹 Why Are Data Engineers So Important?

In today’s digital landscape, companies generate massive amounts of data from various sources, including apps, websites, IoT devices, and more. Without a solid data infrastructure:

Data becomes inconsistent and unreliable
Analysis takes too long
Business decisions are delayed or based on incomplete insights

Data Engineers ensure that data is trustworthy and ready at the right time — enabling everyone else to work more efficiently.

🔹 The Expanding Role

The modern Data Engineer’s role goes beyond traditional ETL. They now work with:

Streaming platforms like Apache Kafka
Workflow orchestration tools like Apache Airflow
Cloud data warehouses like Snowflake, Redshift, and BigQuery
Automation & DataOps practices

This shift shows how Data Engineering is no longer just “moving data” but about shaping the foundation for AI, analytics, and innovation.

🚀 Final Thought

The role of a Data Engineer is evolving — from pipeline builders to strategic enablers of data-driven organizations. They’re the unsung heroes behind every data science project, machine learning model, or dashboard you see.

Next time you read about an exciting AI breakthrough, remember: somewhere behind the scenes, a Data Engineer made it possible.

🏗️ A Brief History of Data Engineering: From ETL to the Modern Data Stack

Parth Maniar — Sat, 23 Aug 2025 17:03:04 +0000

📀 The Early Days: Data Warehousing (1980s–1990s)

In the 1980s, businesses realized that operational databases weren’t enough for decision-making. The idea of a data warehouse emerged — a central place to store structured data for reporting and analytics.

Tools like ETL (Extract, Transform, Load) pipelines became essential. Engineers developed batch processes to transfer data from transactional systems into warehouses, such as Oracle, Teradata, and SQL Server.

💻 The Big Data Era (2000s–2010s)

The explosion of the internet created a flood of data — too big for traditional warehouses. Enter Big Data.

Technologies like:

Hadoop (distributed storage & processing)
MapReduce (parallel computation)
NoSQL databases (MongoDB, Cassandra)

allowed companies to handle massive amounts of unstructured data at scale. This was when Data Engineering became a distinct discipline, separate from software engineering.

☁️ The Cloud & Modern Data Stack (2015–Present)

With the rise of cloud computing, the data landscape changed again.

Tools like:

Apache Spark for fast processing
Cloud warehouses (Snowflake, BigQuery, Redshift)
Data pipelines & orchestration (Airflow, dbt, Kafka)

made it easier to scale, automate, and democratize data.

Today, Data Engineers don’t just move data — they design systems that make data reliable, accessible, and analytics-ready for Data Scientists and business teams.

🚀 The Future

Data Engineering continues to evolve with:

Real-time streaming (Kafka, Flink)
AI-powered pipelines
DataOps & Automation

The role of Data Engineers is becoming more strategic, ensuring organizations can trust and leverage their data for decision-making.

✨ Closing Thought

Data Engineering has grown from simple ETL scripts to powering the modern AI-driven world. Understanding this history helps us see not just where the field came from — but also where it’s heading.

👋 Hello World! A Little About Me

Parth Maniar — Fri, 22 Aug 2025 19:27:55 +0000

Hi everyone,

I’m Parth Maniar, and I’m excited to start this blogging journey! I recently completed my Master’s in Data Science from Rowan University USA, and I’m passionate about using data to solve real-world problems.

🎓 My Background

During my academic journey, I worked on projects that involved:

Big Data Processing using Hadoop, Kafka, and Apache Spark
Data Visualization & Analysis (exploring crime datasets, customer feedback, Reddit sentiment analysis
Applying machine learning models to uncover insights and improve decision-making

These experiences helped me realize how much I enjoy turning raw data into stories that people can actually use and understand.

💡 What You’ll Find Here

Through this blog, I’ll be sharing:

My journey as I grow in the Data Science / Data Engineering field
Practical tutorials, projects, and insights from my work
Career growth experiences as an international graduate building a career in the US
Occasional personal stories, reflections, and lessons I learn along the way

🌱 Beyond Data

“Beyond the world of coding and analytics, you’ll often find me exploring new cuisines (always on the hunt for hidden food gems 🍜), diving into the latest tech trends, or tinkering with side projects that push me out of my comfort zone.”

🚀 Looking Ahead

This blog is my way of documenting my journey while connecting with others who share similar interests. Whether you’re a fellow data enthusiast, a student, or just curious about tech, I hope you’ll find something valuable here.

Thanks for stopping by — and welcome to the start of my blogging journey! 🙌