The Key Steps in the ETL Integration Process

Embrace ETL integration technology and improve how you integrate data to drive business value

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.

Whether it’s data from third-party vendors or customers or in-house systems and applications, it is critical to consolidate and integrate data to maintain accuracy and ensure reliable analytics processes. 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, the process of ETL will be valuable.

ETL has been the standard for data warehousing and analytics within enterprises for some time. But as we progress further into 2019, it’s important that we view ETL not just as its own microcosm of data readiness processes within an enterprise, but also in the context of enterprise-wide integration and enhanced business outcomes.

ETL Integration Overview

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 puts emphasis on 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 a number of critical business functions. They:

  • 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.

Steps in the ETL Process

Essentially, ETL is the process of moving data from a source system into a data warehouse. Just before it's loaded into a data warehouse, the data is transformed from a raw state into the format required by the enterprise data warehouse.

While there are various actions performed in an ETL process, they are built around three steps, as implied in its name. They are:

  • Extract: Retrieving raw data from an unstructured data pool and migrating it into a temporary data repository
  • Transform: Structuring, cleansing, and converting the data in order to accurately match it with the target source
  • Load: Pushing the structured data into the data warehouse so it can be analyzed properly by business intelligence teams

In addition to the three steps within an ETL process, there are several different functions that ETL processes support within an enterprise. Today’s ETL integration tools are able to connect to many different data sources, even non-relational databases, in order to load to equally varied destinations.

ETL Process Example

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 ETL workflow within a company includes:

  • 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.
  • Beginning transformations on the staged data, which can range from being performed in-memory or in temporary tables on the disk.
  • Writing the process outputs to log files for debugging.
  • Deploying various functions, including data cleansing and filtering, formatting, and applying lookups and calculations.
  • 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 in a different way. 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 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.

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