Data processing: Batch vs Streaming

“Data is the new oil.” But just like oil, raw data isn’t valuable until it’s refined.

How we refine or process that data, whether in large scheduled chunks (batch) or as a real-time stream, directly affects the speed, cost, and impact of decisions we make with it.

Let me break it down for you:

• What is batch processing?

• What is streaming processing?

• Key differences with examples

• How to choose the right one for your pipeline

• Modern practices

What is Batch Processing?

Batch processing is a data processing method where a large collection of data known as a batch, is collected, stored, and then processed together, usually on a scheduled basis (e.g., hourly, nightly, or monthly).

Think of it like doing laundry: you collect dirty clothes over a few days, then wash them in one go, rather than cleaning each shirt as soon as it gets dirty.

Example Use Cases

You have probably used products powered by batch processing without even realizing it. Here are a few examples where batch really shines:

• Monthly Reports: Think of your company’s sales dashboard showing last month’s numbers, those aren’t live stats. They’re usually crunched overnight in a batch job.

• Historical Analytics: Want to know how customer behavior changed over the last year? Batch processing digs through massive archives to surface those insights.

• Data Warehousing & BI: Tools like Power BI or Tableau often connect to cleaned, preprocessed data and guess what? That cleanup likely happened in a nightly batch pipeline.

• Data Backup and Archival: Need to archive logs every 24 hours or process files dropped into a folder once a day? Yep, batch processing handles that like a pro.

How It Works

1. Data Collection
   Raw data accumulates over time from logs, databases, or files.

2. Processing in Bulk
   Tools like Apache Spark or AWS Glue transform the data in one go.

3. Storing Output
   The processed results are saved in data warehouses (e.g., Redshift, Snowflake) or storage systems (e.g., S3, HDFS) for reporting or analysis.

   

Common Tools for Batch Processing

• Processing Engines: Apache Spark, Hadoop MapReduce, AWS Glue

• Orchestration & Scheduling: Apache Airflow, Luigi, Prefect

• Storage & Querying: HDFS, Amazon S3, Apache Hudi, Hive, SQL

Advantages of Batch Processing

1. Simple to build, test, and maintain

2. Efficient for processing large datasets

3. Ideal for historical analysis and periodic reporting

Limitations

1. Not suitable for real-time use cases

2. Higher latency (minutes to hours)

3. Less responsive to new data or anomalies

What is Streaming Processing?

Streaming processing handles data continuously as it arrives. It breaks the wait and there is no need to collect everything first.

Think of it like watching a live cricket match: You get ball by ball updates in real time. Batch, in contrast, would be watching the highlights after the match ends.

Example Use Cases

• Fraud Detection
  Identify and block suspicious UPI transactions within milliseconds.

• Live Dashboards
  Show real-time user activity, payments, or system performance.

• Ride-Hailing Apps
  Continuously track driver locations and assign rides instantly.

• IoT Sensor Monitoring
  Monitor data like temperature or pressure from devices in real time.

• Real-Time Recommendations
  Suggest videos, products, or articles based on live user clicks or views.

How Streaming Works

1. Event Ingestion
   Data flows in continuously from apps, sensors, or logs using tools like Kafka or Kinesis.

2. Real-Time Processing
   Processing engines like Apache Flink or Spark Structured Streaming apply business logic as events arrive.

3. Immediate Output
   Results are sent instantly to dashboards, alerting systems, databases, or storage platforms..

Common Tools for Streaming Processing

• Messaging Systems: Apache Kafka, Amazon Kinesis, Apache Pulsar

• Stream Processing Engines: Apache Flink, Spark Structured Streaming, Apache Storm

• Storage & Serving: Apache Hudi, Delta Lake, Redis, Elasticsearch

Advantages of Streaming Processing

1. Ultra-low latency and fast decision making

2. Fresh, always updated insights

3. Enables proactive systems (e.g., alerts, fraud prevention)

Challenges

1. Higher development complexity

2. Difficult to debug and test

3. Demands stronger infrastructure for fault tolerance and scalability

Batch vs Streaming: Quick Comparison

Go with Batch Processing when:

• You’re working with historical data, like last week’s sales or yearly customer trends.

• Speed isn’t a deal breaker and it’s okay if results come in a few hours later.

• You want a simpler, more cost effective setup that’s great for processing large datasets at once.

Example: Running a daily ETL job that loads clean data into a warehouse each night.

Choose Streaming Processing when:

• You need to make decisions on the fly, like catching fraud or triggering alerts.

• You’re working with continuous data streams such as sensor feeds, payment events, or app logs.

• Delays actually hurt maybe they cost revenue, miss opportunities, or frustrate users.

Example: Detecting and blocking a suspicious UPI transaction before the money leaves the account.

Modern Best Practice: Combine Both

Many data platforms today use hybrid architectures like Lambda or Kappa, combining the reliability of batch with the speed of streaming.

You can use streaming for real-time alerts and batch for historical reprocessing, giving you the best of both worlds.

Wrapping Up

Batch and streaming aren’t rivals, they’re tools built for different jobs. Batch excels when you're dealing with large volumes of data and can afford to wait. Streaming shines when speed and immediacy are non-negotiable.

In today’s fast-paced world, most modern systems use both, blending the stability of batch with the responsiveness of streaming to create scalable, intelligent data platforms.

The best pipelines aren’t just fast or accurate they’re built with purpose. And knowing when to use batch or streaming is the first step toward that purpose.