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 first stage in the data ETL process is data extraction, which retrieves data from multiple sources and combines it into a single source. The next step is data transformation, which comprises several processes: data cleansing, standardization, sorting, verification, and applying data quality rules. This step transforms data into a compatible-ready-to-use format. The final step is loading the transformed data into a new destination.
The extraction, transformation, and loading processes work together to create an optimized ETL pipeline that allows efficient migration, cleansing, and enrichment of critical business data. In addition, a user-friendly ETL interface is important for non-technical users to make critical business decisions with the data at hand.
Now that we’ve answered the critical question, ‘what is ETL data processing?’, let’s understand some key benefits of ETL data processing and how it differs from data integration. We’ll also cover the main factors that influence dataflows and how important it is to have an efficient ETL interface.
Benefits of ETL Data Process
Automated ETL tools offer a more straightforward, code-free ETL interface, which is a faster alternative to traditional ETL data processing that involves complex and often painstaking hand coding and testing. Here are some of the benefits of ETL tools:
User-Friendly Automated Processes
ETL 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. In addition, these connectors contain in-built data transformation logic 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 data 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.
ETL pipelines can often be fragile when in operation, especially when high volume or complex transformations are involved. ETL tools can help develop robust and error-free data processes for users with an in-built error control functionality.
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 data sets, even where complex data manipulation or rule-setting is required.
Sophisticated Profiling and Cleaning of Data
ETL tools offer advanced data profiling and cleaning, 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 processes. ETL tools aim for an ETL interface that makes 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 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 Process and Data Integration
People often confuse ETL and data integration; while they are complementary processes, 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 storage, reporting, or analytics structure to convert it to ETL big data. Extracting, transforming and loading in the database may seem like a difficult process, but the right automated tool can maintain the database despite the continuous inflow of data into the organization.
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, the ETL interface encompasses a relatively narrow set of operations performed before storing data in the target system.
Factors Affecting ETL Data Processes
There are various factors affecting the data ETL process, including:
Difference Between Source and Destination Data Arrangements
The disparity between the source and target data arrangements directly impacts 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 for a smooth process of ETL in a database.
Incomplete loads can become a potential concern if source systems fail while your ETL operation 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 operation from scratch, while a warm-start is employed in cases where you can resume the process from the last identified records that the operation loaded successfully.
Organization’s Approach Towards ETL tools
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 operations. Thus, your management should be willing to explore the latest data warehousing technology so that it doesn’t limit your choices.
Another factor that governs the way your ETL mechanism is implemented is your in-house proficiency. While your IT team may be familiar with coding for specific ETL databases, they may be less capable of developing extraction processes for cloud-based storage systems.
It should also be noted that maintaining an ETL database is a continuing process that requires consistent maintenance and optimization as more sources, records, and destinations are added to 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 during ETL batch processing 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 operations, 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.
Get Started with ETL Data Integration
With the help of ETL tools, you can collect, process, and load data without any expertise in coding languages. Due to robust operation, built-in error handling functionality, and a simple interface, these tools leave less room for human fault than manual coding.
Manual coding requires high involvement of IT personnel. The data processed, in turn, requires a lot of time and batch-processing. Hence, modern ETL tools are preferred by businesses as they make large ETL data sets and complex processes more effective.
All of these advantages result in improved speed, proficiency, and data quality for your ETL pipelines. Optimized 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.
Astera Centerprise, an enterprise-level data management tool, allows you to build a cohesive data foundation by leveraging ETL and its rich data mapping and transformation capabilities. The solution makes it easier for businesses to execute ETL processes.
Try Astera Centerprise and find out how it can help your organization’s data-driven processes.