ETL (Extract, Transform and Load) data processing is an automated procedure that extracts relevant information from raw data, converts it into a format that fulfills business requirements, and loads it into a target system. The extraction, transformation, and loading processes work together to create an optimized ETL pipeline that allows for efficient migration, cleansing, and enrichment of critical business data.
In this article, we’ll explain some key benefits of ETL data processing and how it differs from data integration. We’ll also cover the main factors that influence ETL processes.
Benefits of ETL Data Processing
ETL tools offer a more straightforward and faster alternative to traditional ETL processing, which involves complex, and often painstaking hand coding and testing.
Here are some of the benefits of ETL tools:
User-Friendly Automated Processes
ETL data processing tools come packaged with a range of ready-to-deploy connectors that can automatically communicate with data source and target systems without users having to write a single line code. These connectors contain in-built data transformation logics and rules governing extraction from each related system, shaving weeks off data pipeline development times.
Leading ETL tools have graphical user interfaces that allow for intuitive mapping of entities between source and destination. The GUI will show a visual representation of the ETL pipeline including any transformations applied to entities on their way to the destination. These operations are present in ETL software as drag-and-drop boxes that provide a handy visualization for end-users.
When in operation, ETL pipelines can often be fragile, especially when high volume or complex transformations are involved. With an in-built error control functionality, ETL tools can help develop robust and error-free data processes for users.
Optimum Performance in Complex Data Processing Conditions
You can extract, transform, and load huge data volumes in batches, increments, or near-real-time using modern ETL tools. These tools streamline various resource-intensive tasks, including data analysis, string manipulation, and modification and integration of numerous sets of data, even where complex data manipulation or rule-setting is required.
Sophisticated Profiling and Cleaning of Data
ETL tools offer advanced data profiling and cleaning, which are often needed when loading data in high-volume architectures, such as a data warehouse or data lake.
Improved BI and Reporting
Poor data accessibility is a critical issue that can affect even the most well-designed reporting and analytics process. ETL tools make data readily available to the users who need it the most by simplifying the procedure of extraction, transformation, and loading. As a result of this enhanced accessibility, decision-makers can get their hands on more complete, accurate, and timely business intelligence (BI).
ETL tools can also play a vital role in both predictive and prescriptive analytics processes, in which targeted records and datasets are used to drive future investments or planning.
Your business can save costs and generate higher revenue by using ETL tools. According to a report by International Data Corporation (IDC), implementing ETL data processing yielded a median five-year return on investment (ROI) of 112 percent with an average payback of 1.6 years. Around 54 percent of the businesses surveyed in this report had an ROI of 101 percent or more.
You can streamline the development process of any high-volume data architecture by using ETL tools. Today, numerous ETL tools are equipped with performance optimizing technologies.
Many of the leading solutions providers in this space augment their ETL technologies with data virtualization features, high-performance caching and indexing functionalities, and SQL hint optimizers. They are also built to support multi-processor and multi-core hardware and thus increase throughput during ETL jobs.
ETL and Data Integration
People often confuse ETL with data integration, while these processes are complementary, they differ significantly in execution. Data integration is the process of fusing data from several sources to offer a cohesive view to the operators whereas, ETL involves the actual retrieval of data from those disparate locations, its subsequent cleansing, and transformation, and finally the loading of these enhanced datasets into a storage, reporting, or analytics structure.
Essentially, data integration is a downstream process that takes enriched data and turns it into relevant and useful information. Today, data integration combines numerous processes, such as ETL, ELT, and data federation. ELT is a variant of ETL that extracts the data and loads it immediately before it is transformed. Whereas, data federation combines data from multiple sources in a virtual database that’s used for BI.
By contrast, ETL encompasses a relatively narrow set of operations that are performed before storing data in the target system.
Factors Affecting ETL Processes
Difference Between Source and Destination Data Arrangements
The disparity between the source and target data arrangements has a direct impact on the complexity of the ETL system. Because of this difference in data structures, the loading process normally has to deconstruct the records, alter and validate values, and replace code values.
If the data has poor quality, such as missing values, incorrect code values, or reliability problems, it can affect the ELT process as it’s useless to load poor quality data into a reporting and analytics structure or a target system.
For instance, if you intend to use your data warehouse or an operational system to gather marketing intelligence for your sales team and your current marketing databases contain error-ridden data, then your organization may need to dedicate a significant amount of time to validate things like emails, phone numbers, and company details.
Incomplete loads can become a potential concern if source systems fail while your ETL process is being executed. As a result, you may choose to cold-start or warm-start the ETL job, depending on the specifics of your destination system.
Cold-start is when you restart an ETL process from scratch, while a warm-start is employed in cases where you can resume the operation from the last identified records that were loaded successfully.
Organization’s Approach Towards Technology
If your managers are not familiar with data warehouse design or have zero technical knowledge, they may prefer to stick with manual coding for implementing all ETL processes. Thus, your management should be willing to explore the latest technology so that it doesn’t limit your choices.
Another factor that governs the way your ETL process is implemented is your in-house proficiency. While your IT team may be familiar with coding for specific databases, they may be less capable of developing extraction processes for cloud-based storage systems.
It should also be noted that ETL is a continuing process that requires consistent maintenance and optimization as more sources, records, and destinations are added into an organization’s data environment.
Data Volume, Loading Frequency, and Disk Space
A large data volume tends to shrink the batch window as jobs will take longer to run, and there will be less time between each one. The volume and frequency of data extraction and loading can also impact the performance of source and target systems.
In terms of the former, the strain of processing day-to-day transactional queries as well as ETL processes may cause systems to lock up. While target structures may lack the necessary storage space to handle rapidly expanding data loads. The creation of staging areas and temporary files can also consume a lot of disk space in your intermediary server.
The Bottom Line
With the help of ETL tools, you can collect, process, and load data without any expertise in several coding languages. Due to robust operation and built-in error handling functionality, these tools leave less room for human fault, making data processing more effective. As a user, you’re also less likely to have issues with data availability.
All of these advantages result in improved speed, proficiency, and data quality for your data pipelines. ETL tools also allow you to reduce the number of employees needed for data processing while still ensuring fewer errors and quicker querying for frontline users. Ultimately, these factors translate to a significant and sustained return on your initial investment.