The Key Steps in the ETL Data Integration Process
As important as data is to the modern enterprise, the growing number of formats, data sources, and technologies make it increasingly difficult to aggregate all that data and understand it.
Integrating data spread across varying sources requires proper ETL integration capabilities to extract, transform, and load the volumes of valuable enterprise information flowing through an ecosystem.
When a company needs to extract data from a data source within its digital ecosystem, but that data is not yet cleansed or optimized for analysis, that is where the ETL process becomes useful.
Here is all you need to know about ETL data integration:
What is ETL (Extract-Transform-Load) Data Integration?
Extract, transform, load (ETL) refers to the process of moving data from a source system into a data warehouse. Before loading into the warehouse, the data is transformed from a raw state into the format required by the enterprise data warehouse.
What is the ETL Process?
The 5 steps of the ETL process are: extract, clean, transform, load, and analyze. Of the 5, extract, transform, and load are the most important process steps.
Extract: Retrieves raw data from an unstructured data pool and migrates it into a temporary, staging data repository
Clean: Cleans data extracted from an unstructured data pool, ensuring the quality of the data prior to transformation.
Transform: Structures and converts the data to match the correct target source
Load: Loads the structured data into a data warehouse so it can be properly analyzed and used
Analyze: Big data analysis is processed within the warehouse, enabling the business to gain insight from the correctly configured data.
Each step is performed sequentially. However, the exact nature of each step – which format is required for the target database – depends on the enterprise’s specific needs and requirements.
Extraction can involve copying data to tables quickly to minimize the time spent querying the source system. In the transformation step, the data is most usually stored in one set of staging tables as part of the process. Finally, a secondary transformation step might place data in tables that are copies of the warehouse tables, which eases loading.
Each ETL stage requires interaction by data engineers and developers to deal with the capacity limitations of traditional data warehouses.
ETL has been the standard for data warehousing and analytics within enterprises for some time. But as we progress further into 2021, we must view ETL not just as its own microcosm of data readiness processes within an enterprise, but also in the context of an enterprise-wide integration and enhanced business outcomes.
Why is ETL Data Integration Important?
Have you ever heard the phrase, “garbage in, garbage out?” That phrase is more appropriate than ever in today’s digital data landscape because it emphasizes how the quality of data is directly related to accurate insights and better decision-making. And thus, we have ETL – extract, transform, load – to help ensure good data hygiene and added business value on the output.
ETL database tools perform several critical business functions:
- Reconcile varying data formats to move data from a legacy system into modern technology, which often cannot support the legacy format
- Sync data from external ecosystem partners, like suppliers, vendors, and customers
- Consolidate data from various overlapping systems acquired via merger and/or acquisition
- Combine transactional data from a data store so it can be read and understood by business users
ETL has been around for some time and is an important part of a data integration strategy, but it’s those companies that have started to take a modern approach to ETL data that are beginning to see even more results.
Major data warehouses, such as Amazon Redshift and Google BigQuery, are very powerful and offer enterprises dynamic and interactive data analytics tools. Because these newer databases are cloud-based, they can perform data transformations directly in place in an analytics database like SQL instead of needing a special staging area for it. Such tools have paved the way for more lightweight ETL software and processes that expedite data query results and business benefits.
From sales teams gaining quality information about potential customers to marketing teams analyzing digital conversion rates, your ETL data warehouse integration tools enhance data readiness so you can better spend your time leveraging valuable business insights from that data.
While each enterprise will utilize ETL differently to best meet their needs, there are similar actions in how the data goes from source to data warehouse. A typical workflow within a company includes five steps of the ETL process:
- Connecting to a single or multiple operational data sources, including an ERP or CRM database.
- Extracting batches of XML, JSON, and flat files (or other formats) into rows according to one or more source system’s tables, based on certain criteria.
- Copying the data that was extracted to a staging area where data values can be standardized and writing the process outputs to log files for debugging...
- Beginning transformations on the staged data, which can range from being performed in-memory or in temporary tables on the disk.
- Connecting to the data warehouse that is targeted and copying the processed data to one or more of the tables for organized, accessible storage.
A retailer, for instance, might have information on a customer across several internal departments, each of which could identify the customer differently. The brand loyalty department might list the customer explicitly by name, while the credit services department if the consumer has the retailer’s credit card, might identify the customer by number. The digital marketing team might only have an email address. ETL tools rationalize all these data points and consolidate the elements so that titles and addresses can be verified, duplicates can be removed, and a single source of truth can be maintained for reliable analytics.
Experiencing ETL Data Integration Challenges?
There are many ways enterprises can gain critical insights from its expansive data sets as long as it's ready for big data processing, but the sheer amount of data flowing through a typical digital ecosystem can be overwhelming. It’s so important to have a partner you can rely on to ensure you are getting the very best results from that data.
Cleo data integration solutions connect the data sources across your cloud, on-premise, or hybrid environment and support ETL data transformation processes required to cleanse store, and integrate that data into analytics platforms. Cleo handles the heavy integration lifting and improves how your business handles its data, so your data integration processes are primed to deliver more valuable insights.
Learn how an integration platform will support your data migration and ETL tools and will help your enterprise gain end-to-end business transparency from an important resource you already have – the data that’s flowing through your business ecosystem.