As important as data is to the business, the growing number of data sources, formats, and technologies make it troublesome to integrate and analyze all that data. This puts pressure on the data analysts and engineering teams as processing such disparate datasets can be messy.
Consolidating information spread across myriad sources requires proper ETL integration capabilities to extract, and transfer large quantities of information with myriad data types.
When a business enterprise needs to garner data from data sources within its ecosystem, but that data is not properly optimized or cleansed, that is where the role of the ETL processes comes into play.
In this blog post, you’ll learn details of ETL data integration: steps, importance, challenges, and solutions.
What is ETL?
What are ETL processes?
Firstly, this question needs to be answered.
An ETL (extract transform load) process garners and refines different types of big data, and then loads it into a data warehouse or data lake. Let’s delve into each step of the ETL workflow:
Extract: In this data extraction step, raw, unstructured data is extracted using ETL tools from multiple sources (heterogenous) including, APIs, sensor data, business systems, transaction databases, etc. This data is then migrated into a temporary, staging data repository.
Transform: The large amounts of source data that have been extracted from different sources are transformed into a specific format. In this step, the raw data undergoes a data cleansing, mapping, and transformation process, often to a specific schema in order to meet specific needs. In other words, data is structured and converted to match the correct target source.
Load: Finally, the converted data is loaded from a staging area to a target system. The target system can be a target data warehouse, a database, a data lake, or a business intelligence tool. The full loading process can be implemented using various methods, such as batch processing, real-time streaming, or incremental updates. In the target warehouse, the data can be properly analyzed and used.
Depending on the size and complexity of the data, loading can be done in different ways. For instance, if dealing with a large volume of data, it may be loaded in smaller chunks or parallelized to improve processing speed and efficiency. Data validation during the loading process helps ensure that the data is accurately transferred to the target system.
Refresh and Maintenance: ETL is not a one-time process. Data sources can change, new data needs to be added, and the target system requires regular updates. As a result, ETL processes typically include mechanisms to handle data refresh and maintenance. This can involve scheduling regular data extraction, transformation, and loading jobs to keep the target system up to date with the latest data changes.
In some cases, ETL processes may also involve the creation of metadata – information about the structure, format, and meaning of the data. Metadata enables users and systems to understand and interpret the data, facilitating efficient data integration and analysis.
What’s more, ETL process in data warehousing play a pivotal role in maintaining data quality, consistency, and accessibility. It’s the engine behind data integration, enabling organizations to extract data from various sources, transform it into a standardized format, and load it into a target database or data warehouse. ETL is critical because it ensures that data is reliable, accurate, and ready for analysis, decision-making, and reporting. In today’s data-driven world, ETL is the backbone of data operations, helping businesses derive actionable insights and stay competitive in a rapidly evolving landscape.
Overall, ETL is a crucial process that ensures the quality and consistency of data for effective analysis and decision-making. By following the steps and processes discussed above, organizations can successfully integrate and transform their data into a usable format for various business purposes.
Point to note: Each step in the ETL process is performed sequentially. However, the specific nature of each step – which format will be required for the target database – will be contingent on the enterprise’s specific needs and requirements.
ETL has remained a standard for data warehousing and analytics for some time now. But with disruption happening across business marketplaces, we must ETL not only as its own microcosm of data readiness processes within an enterprise, but also in the context of enterprise-wide data integration and improved business outcomes.
ETL vs. ELT
ETL and ELT are two of the most common data integration methodologies used to move data from source systems to a data warehouse or data lake. They both consist of three main steps: extract, transform, and load. However, there are some key differences between the two approaches.
ETL stands for Extract, Transform, and Load. In ETL, data is extracted from the source system, transformed into a format that can be loaded into the data warehouse, and then loaded into the data warehouse. The transformation step is typically performed on a separate server, which can make the process slower. ETL can be a good choice for data warehouses that require complex transformations or for data that needs to be cleansed before it is loaded into the data warehouse.
ELT stands for Extract, Load, and Transform. In ELT, data is extracted from the source system and loaded directly into the data warehouse. The transformation step is then performed in the data warehouse itself. This can make the process faster, as there is no need to transfer data to a separate server for transformation. ELT can be a good choice for data warehouses that need to be able to handle large volumes of data or for data that is not well-structured.
Which approach is right for you?
The best approach for you will depend on your specific needs. If you need to cleanse your data before it is loaded into the data warehouse, or if you need to perform complex transformations, then ETL may be a good choice. However, if you need to be able to handle large volumes of data or if your data is not well-structured, then ELT may be a better option.
Ultimately, the best way to decide which approach is right for you is to consult with a data expert. They can help you assess your needs and recommend the best approach for your specific situation.
Here are some additional considerations when choosing between ETL and ELT:
- The size of your data: If you have a large volume of data, ELT may be a better choice because it can be faster.
- The type of data: If your data is not well-structured, ELT may be a better choice because it can be more flexible.
- Your budget: ETL can be more expensive than ELT, so you need to factor in the cost of your data integration project.
- Your team’s skills: If your team is not familiar with ELT, you may need to invest in training or hiring an expert.
How Does ETL Help Businesses?
The quality of data is directly related to the ability of an organization to generate insights and make better decisions. And this is where ETL helps. It helps users ensure good data hygiene and added business value to the output. Some of the critical functions performed by ETL solutions are:
- It reconciles different data formats to move data from a legacy system into modern technology.
- It synchronizes data from partners including, suppliers and customers.
- It consolidates data from multiple overlapping systems acquired via merger and/or acquisition.
- It combines transactional data from a data store so it can be read and analyzed by business users.
- It provides the opportunity to cleanse and scrub data, removing inconsistencies, errors, and redundancies, which is crucial for data accuracy.
- It helps businesses adhere to data privacy regulations by ensuring data is appropriately handled and secured.
Basically, it enables users such as sales teams to gain information about potential customers and marketing teams to analyze digital conversion rates – to ultimately improve data readiness and data integration so companies can easily use the actionable insights for making decisions.
What Are Its Challenges?
Despite all these benefits, many ETL integration tools are not able to deliver the required value at the speed of business. Oftentimes, ETL processes are difficult to scale. And rigorous support of full-time data engineers to develop and maintain the scripts that keep the data flowing is needed. With any change in schemas or APIs, the data engineers need to update their scripts to accommodate them, which results in downtime and high operational overheads. With large quantities of data being ingested from so many disparate (often fast-moving) data sources, teams find it tough to maintain and refurbish critical ETL flows.
Another major challenge is that step of creating source-to-target data mappings for enabling data transformation is time-consuming and tedious especially when the underlying source and target systems change. This fuels problem such as missing information and data inserted into the wrong fields. Incorrect mappings ultimately risk organizations’ ability to make decisions, leading to missed opportunities and lost revenue.
The need of the hour is to reimagine the data integration tools that help users supercharge their ETL capabilities.
What is the Solution?
To resolve these issues, companies need to transform the way they extract, transform, and load customer data using self-service.
By reimagining the data integration solutions through self-service, companies can empower their business users to create new customer data connections in minutes—securely and easily. Users can easily point and click through easy screens and utilize machine learning and security protocols to onboard and manage multi-dimensional, complex data and stream it in real-time to execute modern-day business transactions. This frees IT teams from tedious and thankless custom coding and EDI mapping and instead focus on more strategic tasks.
Conclusion
As we are heading into the digitally transformative era, it’s even important for us to revamp our ETL data integration methods. Not only will that enable us to make good decisions but also delight customers and create revenue faster.