Extract, Transform, Load (ETL) has many practical uses, chief among them are its capabilities for data integration. Although data integration is one of the most common ways businesses choose to use ETL, ETL also has uses in loading data warehouses, augmenting data for profiling, and data migration. So, why do ETL procedures have a hand in so many types of data processes? Why is ETL beneficial for integration? What should a business be looking for in an ETL data integration tool?
What is ETL?
ETL is a process with three specific steps. Step one is to extract data from one or more sources. Step two is to transform that data into a form that is accessible to the target solution, and step three is loading the transformed data. In the midst of the ETL process, developers are carefully monitoring errors to keep things running smoothly. Let’s get into a bit more detail on each aspect of ETL.
Extract: Many organizations collect and store data across numerous platforms and databases. Data integration is used to gather all the information and make it accessible across platforms, thereby creating greater transparency and alignment between teams. The initial step of ETL data integration is to extract data. Before that happens though, the organization doing the data integration must define which pieces of information are significant to their processes. Once the integrating data is established, the ETL tool can be used to create connections that properly sync the information between systems.
If you read through StarfishETL’s website, you’ll see many references to “connectors”. Some solutions offer Native integrations, meaning, they are using the integration Web services / API provided by the manufacturer. Since most manufacturers have their own connector format, it becomes challenging to scale and manage change to software, and to support new softer. StarfishETL takes a different approach. We have a proprietary standard connector format which allows us the build against one format and support and scale to multiple applications with limited changes the program and any maps that were previously built.
In addition to connectors, the extract phase also requires some limited mapping to determine a data retrieval strategy. The data map tells the integration which data matches with which fields. For example, if you’re source system uses the term “Accounts” but your destination system labels accounts as “Companies”, the map must specify the connection between those two fields.
Transform: The extraction of data and creation of connectors establishes the baseline of the ETL process. The next step is to convert that data into a consistent format that meets the structural requirements of the target destination. During the transformation stage, the data must first be cleaned to ensure its integrity. The rest of the process involves setting specific business rules that constrain the data to the correct format.
Load: This is the final step in the ETL process. The load stage is when the transformed data is loaded into its destination system. There are two methods of loading data: doing a full load and doing an incremental load. A full data load is pretty much what you would imagine. The first time data is loaded into the integrating solution, it’s pushed all at once. Because a full load is processing so much data, techniques like concurrent workflow execution and bulk data loading may be used to try and streamline the process.
Incremental loading then synchronizes new or updated data as it is added, allowing the integration to process only the most recent transactions instead of synchronizing all the data every time a new piece of information is logged. This saves time and computing power.
The Benefits of ETL Data Integration
Integrations can be complex beasts. An ETL tool makes managing that complexity easier by creating an organized structure, automatically identifying the correct formats of the data, and setting business rules for how the data will interact. Other key advantages of ETL data integration are:
- The graphical user interfaces of ETL tools visualize the data process and allow drag-and-drop functionality to make changes.
- ETL tools are pro at transferring large volumes of data in batches to reduce lag time.
- If your ETL tool provides data cleaning capabilities (Like StarfishETL does!) it makes integration even quicker.
- With the ability to create connectors for many types of systems, ETL tools make it easy to build effective data warehouses and boost their performance.
- ETL provides metadata that can be used to manage data governance and support data quality.
- Automated monitoring of data flows means transformation errors can be caught and resolved sooner.
- Big data is easier to handle with a modern ETL tool because of its ability to combine extremely large data sets from disparate sources.
What to Look for in an ETL Data Integration Solution
There are several ETL data integration solutions to choose from out there, so how do you which ones are equipped with the ideal features for your integration? Here are some features to look for in a high-performing ETL:
- Extensive Connector Library: An extensive connector library helps unite the internal and external data sources of the business with less initial up-time and cost. You don’t have to wait for the integrator to build the connector or pay for them to build it if their library already includes the connections you need. The more connectors offered, the greater the possibilities. StarfishETL offers connections to 100+ solutions, whether they’re on-premise, in the Cloud, or in a hybrid environment.
- Low Code Functionality: The whole point of ETL is to make data integration easier. The solution you choose should have a visual design interface and drag-and-drop functions to edit your integration quickly.
- Encrypted Security: This one may be a given because, as we all know, any time we’re dealing with important business data, it’s critical that the data is kept safe. An encrypted SQL database, threat detection, and HTTPS encryption are all features that will allow you to safely move data between systems. StarfishETL provides all these encryption capabilities, plus tools for enabling GDPR compliance.
- Ability to Integrate Through a Firewall: The opportunity may arise when you need to integrate a locally installed application or database with one in the Cloud. A high-performing ETL will offer the ability to integrate through your firewall, so you will not need to expose your data to connect systems. StarfishETL’s Ray feature enables this.
- AI Capabilities: Artificial intelligence can detect software and build integrations based on heuristics. This allows for the rapid development of new integrations and streamlined architectures. StarfishETL uses a stitching engine to enable these AI functions in its iPaaS solution.
- Data Quality & Cleaning Tools: Integration is meant to connect ONLY the best data between your systems. Otherwise, reporting and analytics will be an ill-fitting representation of your business. With the data cleaning power of InsideView built into StarfishETL, businesses can augment, clean, and enrich data on an ongoing basis.
- Job Scheduling: ETL should automate as much of the data integration as possible. Job scheduling and other automation capabilities let the business be more hands off and runs the integration at time intervals that make the most sense for the organization.
- On-premises Options: When you’re doing a high-volume data transfer, the Cloud can become a limiting solution. If the integration solution can be installed in a private Cloud or on-premises, the system is going to run significantly faster. StarfishETL offers on-premises options for this very reason.
For help getting started with your next ETL data integration, contact our team. We’ll set up a free consultation to discuss your needs.
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