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, transfer, load large quantities of information.
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 process comes into play.
In this blog post, you’ll know details of ETL data integration: steps, importance, challenges, and solutions.
An ETL process garners and refines different types of data, and then loads it into a data warehouse or data lake. Let’s delve into each step:
Extract: In this step, raw data is extracted from the target sources (heterogenous) including, APIs, sensor data, business systems, transaction databases, etc. This data is then migrated into a temporary, staging data repository.
Transform: The raw data that has been extracted from different sources is transformed into a specific format. In this step, data undergoes a 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 database. In the target warehouse, the data can be properly analyzed and used.
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.
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.
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.
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.
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.
Related White Paper: Reimagine your Customer Data Integration
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.