What’s batch processing? How it works and examples

While all data can be important, not every data set needs to be processed on a streaming basis. For any information that’s not urgent or time sensitive, batch processing is a great option. This method allows you to collect large volumes of data over time before processing it in a more efficient, bulk manner.

Although other ingestion strategies, like data streaming, often get the spotlight, a batched approach is cost-effective and flexible. Let’s dig into where batch processing shines and how to get the most out of this approach.

What’s batch processing?

As the name implies, batch processing manages collected data in batches of records instead of individually. Typically, each new group is a collection of all stored specifics received since the previous processing. Triggering a new batch job will depend on your settings — either at a regular interval or when a specific volume of data has been processed by your data source. 

For businesses that don’t need instant results, batch processing is incredibly useful since you’re able to schedule processing for off-peak hours. This reduces resource consumption and keeps your systems running smoothly. 

Batch processing vs. stream processing 

Batch systems process volumes in sequential order, typically detecting and processing only changed values to reduce resource consumption. A company collects data for a set amount of time and processes it in bulk, typically at a scheduled time. Batch processing is suitable whenever business decisions do not depend on by-the-second responsiveness.

Stream processing integrates large volumes of information continuously. Every single event, as soon as it’s generated and logged, is processed by your system. This option takes more resources, but is essential for use cases like detecting anomalies, cybersecurity, real-time collaboration, sensor monitoring, and others that depend on instant decision-making.

Batch and stream processing are suitable to different kinds of operational and analytical use cases, and businesses typically use both.

3 advantages of batch processing

Instead of having to continuously earmark resources for data integration, a batch system lets you plan a more stable and low-impact way to ingest information.

Here are some of the main benefits: 

1. Simplicity: Batch systems are easier to create and maintain since they don’t need strict timing constraints or low-latency systems. If you spot any errors, you can pinpoint which group originated the error and rerun it directly. 
2. High throughput: When you don’t have to consider immediacy, you can plan to move data when your systems are under the least strain. Planning batch movements for off-peak hours means you can maximize data throughput, allowing a large volume for ingestion.
3. Cost-effectiveness: When data is collected over time and processed only when you need it, you’re able to minimize infrastructure and ingestion costs. There’s also less maintenance involved in batch processing, meaning you can save costs on data pipeline monitoring.

When you partner with an automatic ELT workflow like Fivetran, batch processing becomes that much easier. Find the exact format and content of data your company needs with 700+ ready-made connectors.

How does batch processing work?

Batch processing begins with collecting data over a defined period of time. Everything that updates during this window is organized together, with the size of a group depending on your settings — like maximum threshold or what’s accumulated since the previous job.

Once arranged, the workload is placed into a queue along with instructions for how the system should ingest and transform it. The job then executes automatically once the right conditions are met.

With the correct settings in place, these jobs are fairly low maintenance. They run independently, meaning you only need to look out for errors and rerun any failed batches.

Batch processing examples and use cases

Batch processing works best for large volumes of data where downstream systems don’t require immediate results. Below are a few examples of batch processing in action.

Billing and invoicing

Most organizations don’t process invoices or report billing every day. As a monthly, or potentially quarterly, process, batch processing is perfect for collecting data over a period of time and transforming it all at once. 

ETL (Extract, Transform, Load)

Transformations can be computationally expensive to run. Strategies like ETL, which involve applying bulk processes to larger volumes of data, can cut back on costs. ETL data pipelines are well suited to large-scale data migrations where cost control matters more than immediacy.

Medical research

It’s not uncommon for those in the medical field to process large volumes of data, whether it’s clinical modeling or genome sequencing. Teams collect the exact details needed and then ingest it all at once before they perform additional research.

Best practices for batch processing

Although these processing systems are easy to manage once built, there are a few best practices you can follow to make sure they work as well as possible.

Optimize scheduling and prioritization

Make sure to select a schedule that aligns best with your business, using a timeslot where you have the least amount of resources being consumed by your system. Doing so reduces congestion and lets you bulk move data without creating performance bottlenecks elsewhere.

Maintain data accuracy and consistency

Introducing validation pathways that check data quality both at the source and across your pipeline will help prevent errors from entering your environment. Check the content and structure of the specifics that moves through your pipeline for inconsistencies to pinpoint any settings needing refinement. 

Monitor performance regularly

Watching your pipeline performance is the easiest way to get ahead of errors and reduce the impact of issues like schema drift. By double-checking data, validating its quality, and looking at performance data within your pipeline, you’ll spot any issues long before they cause trouble. 

Alternatively, you can use Fivetran’s automated ELT pipelines to outsource those extra hours of monitoring. By fully managing the end-to-end data integration process, Fivetran gives your data engineers more hours to focus on analysis and high-value downstream activities.

Challenges of batch processing

Although batch processing serves several use cases and helps businesses create stable, cost-effective data ingestion pipelines, it’s not perfect.

You may experience one or all of these issues:

• High latency: Any highly time-sensitive use case cannot use batch processing due to the significant gap between data generation and ingestion.
• Requires ample infrastructure: Because of the necessity of moving and storing pre-processed batches, you’ll need ample space for both pre-processed and post-processed datasets. 
• Debugging difficulties: Especially if you only process batches infrequently, you might be working with a huge volume of data. If a job fails, identifying what made things go wrong from a large pool of information can be challenging.

How Fivetran supports data processing workflows

Fivetran gives you more from batch processing by removing the need to create custom, brittle batch scripts or orchestrate complex ingestion workflows. With automated data movements and pre-built connectors, you can get out-of-the-box ELT batch processing pipelines that feed your downstream analytics.

Easily use flexible scheduling to ingest information from any source, collecting data rapidly, consistently, and efficiently. Get started with Fivetran by signing up for a free account or learn more by requesting a live demo today.

FAQs

What are batch processing tools?

Batch processing tools are any system that helps move data from source systems into a destination data architecture. These may include workflow schedulers, big frameworks, or even fully managed ELT pipelines like those offered by Fivetran.

What types of tasks can batch processing handle effectively?

The best tasks for batch processing are repetitive and large-scale. Running jobs at regular intervals or moving huge volumes of data works well with batch processing.

What are some examples of batch systems?

Batch processing is a core part of how many companies integrate data into their storage systems. Everything from payroll systems to data backups may use batch processing to save on ingestion and avoid bottlenecks.

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