Batch vs Streaming

These are two different data processing semantics used for data engineering workloads, including ingestion, transformation, and real-time processing.

Batch Processing

It’s a method in which large volumes of collected data are processed in chunks or batches.

This approach is especially effective for resource-intensive jobs, repetitive tasks, and managing extensive datasets where real-time processing isn’t required. It is ideal for applications like data warehousing, ETL (Extract, Transform, Load), and large-scale reporting.

Due to its versatility in meeting various business needs, batch processing remains a widely adopted choice for data processing.

Data batch processing is mainly automated, requiring minimal human interaction once the process is set up. Tasks are predefined, and the system executes them according to a scheduled timeline, typically during off-peak hours when computing resources are readily available.

Human involvement is usually limited to configuring the initial parameters, troubleshooting errors if they arise, and reviewing the output, making batch processing a highly efficient and hands-off approach to managing large-scale data tasks.

There are a variety of ETL tools for batch processing. A common tool is Apache Airflow, which allows users to quickly build up data orchestration pipelines that can run on a set schedule and have simple monitoring. Explore different tools to find the one that best fits your business needs!

Stream Processing

Also called real-time data processing, it’s a data processing approach designed to handle and analyze data in real time as it flows through a system.

Unlike batch processing, which involves collecting and processing data in large, discrete chunks at scheduled intervals, stream processing deals with data continuously and incrementally.

Data is collected from various sources such as sensors, logs, transactions, social media feeds, or other live data sources.

Data streams are then processed as they are received, involving a series of operations such as filtering, transforming, and aggregating the data. This allows for real-time implementation such as live analytics, triggering alerts, real-time dashboards, or feeding into other systems for further action. These insights are often used to influence immediate decisions.

Streaming processing applications include real-time analytics for financial markets, fraud detection, network traffic monitoring, recommendation engines, and more.

Streaming systems often include capabilities for constant monitoring and managing data flows and processing pipelines to support high-velocity data. This includes tracking the system’s performance, the health of the data streams, and the outcomes of the processing tasks.

One popular framework is AWS Kinesis, which is combined with Lambda. Amazon Kinesis is a cloud-based service that allows you to collect, process, and analyze real-time, streaming data, whereas Lambda supports complex functions and automation.