Understanding Batch Processing in Data Engineering
Batch processing is a data processing term that describes the execution of a series of non-interactive jobs on a computer system. Batch processing is about processing data in finite groups and allows data engineers to work with large volumes of data in an efficient way.
In this post, we will discuss the fundamental knowledge of batch processing, its application, and tools for batch processing.
Basic Concepts of Batch Processing
Batch Processing Workflow
Batch processing follows a structured workflow described as follows:
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Data Generation: The data to be processed is generated in different formats like xml, json, csv, or plain text.
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Data Collection and Aggregation: Data is collected from different sources and is aggregated together into a data warehouse.
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Data Cleaning and Validation: Before processing the data, it must be validated and cleaned to remove inconsistencies, redundancy, and errors.
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Task Ingestion: In this stage, the batch processing engine starts ingesting the data to be processed.
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Batch Processing: The processing engine then breaks down the data into smaller chunks or batches and runs the processing jobs in parallel.
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Data Output: The output is then saved in a data store or sent to a downstream application.
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Data Visualization and Analysis: The processed data is then visualized and analyzed for further insights.
Advantages of Batch Processing
Batch processing has several advantages, which have made it an integral part of data engineering.
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Volume Management: Batch processing helps handle large volumes of data that are too big to be handled in real-time.
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Data Consolidation: Batch processing can consolidate data from different sources into a single dataset.
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Reduced Cost: Batch processing is cost-effective and allows the processing of large volumes of data in a shorter runtime.
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Scalability: Batch processing is easily scalable to handle changes in data volume, processing requirements, or computational workload.
Limitations of Batch Processing
Despite its advantages, batch processing has some limitations that should be considered:
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Timing and Latency: Batch processing depends on timing and cannot provide real-time data.
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Unforeseen Issues: Unforeseen issues can arise during batch processing, and the processing time may increase significantly.
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Data Required to be Stored Temporarily: Batch processes often require storing a large amount of data temporarily, which requires additional storage capacity.
Batch Processing Tools
Several batch processing tools are used in data engineering. These tools have different strengths and weaknesses and can be categorized into two parts:
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On-Premise Batch Processing Tools: These tools are installed locally on a computer system and require handling data storage, computation, and processing. Examples of on-premise batch processing tools are Apache Hadoop, Apache Spark, and Apache Flink.
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Cloud-based Batch Processing Tools: These are cloud-based batch processing tools that are designed to complement or replace on-premise batch processing tools. Examples of cloud-based batch processing tools include AWS Batch, Google Dataflow, and Azure Data Factory.
Apache Hadoop
Apache Hadoop is an open-source, Java-based batch processing tool that provides a distributed file system for storing and processing large data sets. It can efficiently store and process large datasets across many machines.
Hadoop's ecosystem includes several tools such as Hadoop Distributed File System (HDFS), MapReduce, and YARN.
Apache Spark
Apache Spark is an open-source, distributed computing system used for batch processing. It is designed to be efficient in processing large data sets and can run on Apache Hadoop's HDFS. Spark includes several libraries, such as Spark SQL, MLlib, and GraphX.
Apache Flink
Apache Flink is an open-source, batch processing, and real-time processing framework that processes large amounts of data. It provides a flexible architecture for batch processes that can be run efficiently on different computer systems and withstand system failures. Flink's architecture is based on streaming data processing first, before batch processing.
Amazon Web Services (AWS) Batch
AWS Batch is a fully-managed batch processing service that enables the efficient processing of millions of compute jobs in AWS. It enables developers to define their batch computing workflows, and AWS Batch takes care of the rest.
Google Cloud Dataflow
Google Cloud Dataflow is a fully-managed batch processing and streaming data processing service. It can handle large data sets in real-time, and data processing jobs can be scheduled, monitored, and optimized to meet various requirements.
Microsoft Azure Data Factory
Microsoft Azure Data Factory is a cloud-based data integration service designed for batch processing, real-time processing, and hybrid scenarios. It allows data engineers to create, schedule and orchestrate data integration workflows in a cloud-based environment.
Conclusion
Batch processing is an essential part of data engineering. It allows data engineers to work with large datasets efficiently and tackle problems like handling large volumes of data and data consolidation. Batch processing has limitations, and understanding the tools available for batch processing can be crucial in choosing the right tool for the job.
Category: Data Engineering