ETL, ELT, and More: 6 Ways to Manage Multiple Data Sources
ETL, or extract, transform, load, is not just another silly business acronym. ETL is a process by which multiple data sources are brought together in one centralized—or a few disparate—databases. This process involves three quintessential steps: The extraction of data from its original source; the transformation of data through deduplication, combination, and quality assurance; and the loading of data into the final, central database.
The ETL process might sound overly complicated, but rest assured that it is a necessary process for businesses that want to capitalize upon their data. Furthermore, tools exist that help companies undergo the ETL process in the smoothest way possible. Alongside tools, often it is best to also seek out expertise from a partner that specializes in data management and integration. These partners get to know an organization’s unique needs and culture in a way that allows them to craft strategies that best fit a business’s goals.
Before detailing any of these tools and partners, however, let’s dive a little deeper into what ETL means in the context of business.
What is ETL in Business?
ETL is crucial for businesses that want to optimize their ability to analyze their data. Not only does ETL take multiple sources of data and consolidate them into one spot, but ETL also can enable multiple types of data to work together.
ETL tools make this consolidation possible by enabling the migration of data between a variety of sources, destinations, and tools. But how exactly does this occur?
The Steps of ETL
In this initial step, the desired data—whether structured or unstructured—is imported from a data warehouse or a data lake to a single repository. Some common data sources included in this stage are:
- Databases and legacy systems
- Cloud, hybrid, and on-premise environments
- Sales, marketing, and other business applications
- Mobile devices and apps
- CRM systems
- Data warehouses, lakes, and storage platforms
- Analytics tools
After the various desired sources of data are identified, data extraction can happen in one of the following ways:
- Update notification – notifies you when a change to a record has been made.
- Incremental extraction – identifies which records have been modified after they have been changed.
- Full extraction – reloads all the data altogether, regardless of changes made.
The half-way point of ETL is the process of transformation. It is the actual cleansing and deduplication necessary for data to undergo to prepare it for effective and accurate analysis. This is the most important and often the most arduous step of ETL, including several key stages similar to these:
- Cleansing – resolving inconsistencies and missing values in data.
- Standardization – applying formatting paradigms to datasets.
- Deduplication – discarding redundant data.
- Verification – removing of unusable or anomalous data.
- Sorting – organization of data according to type.
Transformation is a particularly crucial step of the ETL process to get right because it significantly improves data integrity by ensuring that various types and sources of data reach their end destination in viable and ready-to-use forms.
How a load occurs in the ETL process is dependent mostly on how a company plans on utilizing their data. It is crucial to take into account the end host system’s functionalities and how it operates to ensure the function of the system isn’t negatively impacted.
Depending on what host system a company uses, there are generally two different ways to load data into a data warehouse:
- Full load – the first time a data source is loaded into the warehouse, all data is dumped at the same time.
- Incremental load – in order to minimize time spent and maximize effort, incremental loading is designed based on when data was created or modified. In order to identify any change in data, incremental loading compares the data in a target system with the original data source.
Other Data Integration Methods
ETL is just one approach (with different microvariations) to data integration. Other ways to facilitate data integration include:
- ELT – a process similar to ETL but instead of transforming data before exporting it to the final host system, the data is imported raw to be transformed as needed later on.
- Change Data Capture (CDC) – identifies and captures just the source data that has been changed and moves that data to the target system.
- Data replication – copies the changes in multiple data sources in real time or periodically to a central database.
- Data virtualization – removes the need to have a transformation step at all. Instead, data is displayed in a software abstraction layer that alters how the data is viewed without altering the actual data itself.
- Stream data integration (SDI) – continuously consumes, transforms, and loads data streams, which creates a data store for powering things like machine learning that can be used to improve customer experience, fraud detection, etc.
Whichever data integration method a company uses, the same truth applies: Companies need to consider integrating their data in order to gather and utilize profit-boosting and efficiency-improving analytics.
What are ETL Tools?
- Enterprise software ETL tools – these tools are created and maintained by commercial enterprises. They usually come with a unique interface that improves navigation and allows for a more robust structure, but also come with a heftier price tag and require special employee training to run. An example of this type of ETL tool would be Azure Data Factory.
- Open-source ETL tools – open source tools allow anyone to access the source code of the tool and extend its capabilities. This can be a benefit when companies want a tool that can be easily customized, but open source ETL tools vary drastically in things like upkeep, documentation, and functionality, since they don’t have a company that supports them. An example of an open-source ETL tool would be Talend OpenStudio.
- Cloud-based ETL tools – many cloud-service providers have started offering ETL tools that work with their infrastructure. These ETL tools are particularly efficient because cloud technology has the benefit of being available anywhere and very elastic, allowing computing resources to scale to meet of-the-moment data processing demands. An example of a cloud-based ETL tool is Informatica Cloud Data Integration.
- Custom ETL tools – companies with the resources to do so can produce their own, custom ETL tools in general programming languages. The only potential roadblock with this approach is that it requires a multitude of internal resources and manpower, whereas the other tools are more readily available.
What ETL Tools Should You Be Using?
What ETL tools a company needs to quickly, easily, and successfully complete their data transition process depends on the needs of a company. Evaluation factors for a company to consider before choosing a tool or suite of tools include, but are not limited to:
- Use case – how is your company going to use this tool?
- Budget – how much can your company afford to spend on ETL tools so as to balance costs and ROI?
- Capabilities – what are the capabilities that your company needs performed?
- Multiple data sources – what tool can extract information from all of your company’s various sources?
- Technical literacy – can your developers and end users understand the tool and use it effectively?
How Kenway Can Help
Our firm has a steady history of providing companies of all sizes and industries with comprehensive data management and governance solutions. Reach out to us today about taking your first steps towards centralization of data through ETL or another data integration method.