Today, companies have access to a broad spectrum of big data gathered from various sources. These sources include web crawlers, sensors, server logs, marketing tools, spreadsheets, and APIs. To gain a competitive advantage in the business, it is crucial to gain proficiency in using data to improve business operations. However, the information from different sources cannot be used immediately to address business needs.
To effectively harness the power of the data, it is necessary to process information through either ETL. One of the earlier techniques that came into existence was ETL batch processing and is continued to be used in multiple use cases. Popular data warehouses such as Amazon Redshift and Google BigQuery still recommend loading events through files in batches as it provides much better performance at a much lower cost compared to direct and individual writes to the tables.
But how does it work, and where is it used? How is it different from streaming ETL processing? No worries! This article provides all the answers on ETL batch processing in a nifty 6-minute read.
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What is ETL? ETL is a short form of Extract, Transform, and Load. ETL streamlines the process of transferring data from one or more databases to a centralized database. Data-driven companies often use it to simplify the movement of data for better accessibility of information across departments.
Batch ETL Processing: How it Works
Batch ETL processing or ETL batch processing involves collecting and storing data in batches over a predetermined period of time called a “batch window.” This approach aids businesses in effectively managing and processing massive volumes of data.
Raw data in ETL batch processing typically takes from one hour to several days to be processed and ready for analysis. The ETL job is run on a defined schedule or once the amount of data reaches a certain threshold.
The working of ETL batch processing is similar to the general ETL process. Initially, businesses define the type and source of data desired to optimize business operations. Next, the data is collected over a batch window from various sources, like APIs, JSON, and CSV files.
Once the data has been extracted, it is validated to ensure that it falls within the expected ranges, and any data that doesn’t meet the criteria are discarded. Continuously analyzing rejected records helps to identify and fix any issues in the source data. After data validation, the data is cleaned by eliminating duplicates, null values, and more. The information is then aggregated to obtain comprehensive data according to the requirement.
The transformed data is then loaded into a staging database, where it can be used for quicker rollback or to produce audit reports and detect errors. Finally, the ETL batch processing data is loaded into the desired tables in the data warehouse. While loading, either it overwrites previous information or adds a timestamp to indicate when it was loaded. The frequency of data loading can be daily, weekly, or monthly.
Typically, ETL batch processing is initiated manually or scheduled for low-traffic times, such as overnight. This allows efficient processing of large amounts of data, minimizing the impact on real-time operations and ensuring that the data is consistent and accurate.
By using ETL batch processing, the need for always using real-time data integration is reduced. This results in lower demands on resources and ensures that the data movement process does not interfere with other critical operations. Therefore, it is especially beneficial for businesses that produce a lot of data.
In ETL batch processing, the specifics could fluctuate, but the fundamental components remain constant. While executing a batch job, the user specifies a number of essential details:
- Name of the individual submitting the job.
- Applications or batch operations to be executed.
- Location of the input data in the system.
- Location for the output of the processed data.
- Batch window or job execution timeframe.
Batch Processing vs. Streaming Processing
ETL batch processing and streaming ETL processing are two independent data processing technologies with unique use cases and benefits. The batch ETL is best suited for processing huge volumes of data on a periodic basis. And the streaming ETL caters to real-time data as it arrives.
Latency
Data Latency refers to the time elapsed between an event and the appearance of the data in the database or data warehouse. Since ETL batch processing handles a lot of data simultaneously, it has a higher latency. This can take anywhere from minutes to days. In contrast, ETL stream processing has a lower latency as it processes data in real-time, with a latency measured in seconds or milliseconds. In simpler terms, stream processing guarantees faster data processing and availability for analysis as compared to ETL batch processing.
Data Size
Batch processing is designed to handle large quantities of data all at once, while stream processing focuses on processing smaller, continuous data streams. In ETL batch processing, the size of the data is determined and finite, making it easier to process in one bulk operation. In contrast, the magnitude of the data in ETL stream processing is unknown and can be infinite. This necessitates a more flexible approach to data processing.
Time of Execution
In ETL batch processing, data is extracted, transformed, and loaded in bulk from various sources into a data warehouse. Usually, ETL batch processing is carried out on a schedule or triggered manually. Instead of processing the data in discrete, real-time increments, it is handled all at once.
In contrast, a streaming ETL processing job is instantly started whenever a new record is introduced to the data source. This implies that a new record is promptly retrieved, modified, and fed into the data warehouse as soon as it is available.
Processing Time
Batch ETL can take anywhere from minutes to hours, whereas streaming ETL is completed in milliseconds or seconds. As a result, batch processing is excellent when processing massive amounts of data are more crucial than real-time analytics. Examples include inventory processing, subscription cycles, supply chain fulfillment, etc. However, stream processing is very helpful when real-time analytics are needed, such as in fraud detection. Insights from real-time analysis can help companies take immediate measures to stop fraudulent transactions from being completed.
Data Processing Order
ETL batch processing often processes vast amounts of data at once. But, it does not ensure the sequence in which the data is handled. This implies that the output’s order of the data may differ from the order in which it was received. This might be a drawback when processing time-sensitive data or in other circumstances where the sequence of the data is significant.
As opposed to ETL batch processing, stream ETL processing processes data in the order that it is received. In other words, data is processed right away in real-time as soon as it becomes available. Therefore, stream processing is well-suited for instances where maintaining the order of data is absolutely necessary. Stream ETL processing, for instance, guarantees that the data is accurate and current in the financial services industry, where transactions must be processed in the order in which they occur.
ETL Batch Processing Use Cases
E-commerce Stores
E-commerce businesses use different digital solutions like digital marketing tools, email marketing tools, CRMs, and more to boost sales. While these platforms help companies optimize their business operations, data is often segregated into individual platforms. However, data collection is not limited to internal tools embraced by e-commerce companies. To enhance the scope of collecting relevant data, businesses can collect data from social media and product review platforms. Collecting data from these sources would require e-commerce companies to deploy batch ETL processes to collect data on a daily or weekly. The data can be stored in a data warehouse so that information can be accessed across the departments for better decision-making.
Medical Research
In medical research, it is typically necessary to examine big data sets. Medical research applications such as computational chemistry, clinical modeling, molecular dynamics, and genomic sequencing testing and analysis can all benefit from batch ETL processing. For example, scientists can use batch ETL processing to collect extensive data during the early stages of drug design, enabling them to understand the function of a particular biochemical process.
Stock Brokerage Firms
Stock brokerage firms operate during market hours, which are only open for a few hours a day. Due to massive trading volume during operational hours, stock brokerage firms use batch processing post-market hours to move data. Brokerage firms collect all the trading data using batch processing ETL for analytics and regulatory requirements. Since brokerage firms are highly regulated, it becomes essential for stock brokerage firms to deploy batch processing to handle their data effectively with little to no human errors.
Banks
Banks usually cater to millions of customers, which leads to numerous transactions every second. These transaction records are then sent to its customers on a monthly, quarterly, or yearly basis. To streamline the process of compiling, auditing, and sending statements for every customer based on their needs, banks use batch ETL processes. This allows them to process large amounts of transactional data in one go, eliminating the need for real-time processing for occasional jobs.
Key Takeaways
The ETL batch processing approach gathers and transforms data in bulk from the source system at regular intervals. After being transformed, the extracted data is generally loaded into a data warehouse. This is the standard method for processing data, which works well for dealing with massive volumes of data that are collected over time. Consequently, organizations can opt for ETL batch processing when time sensitivity is not necessary for data processing.
To implement ETL for multiple data sources and massive volumes of data, you would need to ask your engineering team to develop and maintain data pipelines. Apart from their core engineering objectives, they always have to be on a constant lookout for any data leaks and fix them on priority. Or, you could try out a more economical, effortless, automated, and no-code ETL/ELT solution like Hevo Data which offers 150+ plug-and-play integrations for near real-time data replication. Hevo also offers ETL batch processing for destinations like Redshift, BigQuery, and Snowflake, where your events may be loaded in batches of files to improve performance.
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Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
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Preetipadma is passionate about freelance writing within the data industry, expertly delivering informative and engaging content on data science by incorporating her problem-solving skills.