In data analytics, ETL describes the process of extracting, transforming, and loading data to make it ready to use for a predefined task. Note that ELT (extract, load, transform) is its own distinct process, but today we’ll focus on ETL. If you’d like to learn more about ETL vs ETL, here’s a great article that explains the difference.
The primary purpose of ETL is to warehouse data. Information is extracted from various sources in its predefined form, and the data at this level may be structured or unstructured, and sometimes even inaccurate. In this state, the data is not ready for loading.
First, the data needs to be transformed to its desired state. To get the data ready for loading, information is summarized, reduplicated, filtered, cleansed, validated, and authenticated. In the final ETL stage, the clean data is loaded into a data warehouse for storing and easy access.
This process is similar to creating a daily planner. Each day you gather information about your day-to-day life, then determine what pieces of information are relevant. You may decide to encrypt the data to save it from prying eyes or convert it to relevant data. Afterward, you load the information into the planner and can reference it when you need it.
Like recording in a planner, the ETL process allows you to view information broadly and deliver meaningful and actionable insights that will improve your work processes and business decisions.
Five steps of the ETL process.
While the ETL refers mainly to Extract, Transform, and Load, there are more steps to the procedure. Here are five key steps in the ETL process;
- Extract: This is the process of salvaging data from a data pool, either structured or otherwise, and migrating it into a landing zone for processing. There are many sources to extract data from, including Web pages, CRM and ERP systems, Flatfiles, and emails.
- Sorting and amendment: This process defines the stage where you recategorize data to make it more meaningful.
- Transform: this describes the various processes leading from fine-tuning to conversion into the preferred data format. Several tasks, including Summarization, calculations, translations, Filtering, cleansing, duplicating, and conversion, are carried out to transform data,
- Load: This is the process of transferring data into a data warehouse where users can easily access it.
- Analyze: This is the process where the data in the storage is analyzed to give a live first-glance analysis of the database. The analysis helps users quickly access the data they need, especially when the information is in a quantum state.
Benefits of ETL
ETL allows you to combine data from multiple sources into a single source of truth. There are several benefits of this process, including:
- Enabling informed decisions: Consolidating disparate sources broadens the scope and quality of your insights, enabling more informed and precise decision-making.
- Providing easy access: Having a centralized hub for your business data enables quick and easy access to data from all departments in your organization.
- Enhancing data quality and consistency: Data passing through an ETL process is subject to a standard. Having cohesive data makes data engineering and analysis much easier.
- Building an integrated workplace: ETL generates cohesion between separate divisions in a company since they work on the same data. Having a shared data pool will ignite consistency and collaboration within your organization.
- Improving historical relevance: The ETL process makes it easier to sort through historical data to improve and complete projects or to identify and analyze trends.
- Auditing: In a good ETL process, the data that passes through the system is immediately reported and audited. The ETL process leaves a metadata trail, making any subprocess traceable to a source.
- Ensuring a standard of compliance: An organization that passes information through the ETL is more likely not to run amok with federal legislation like GDPR or the Sarbanes-Oxley Act.
- Improving scalability: ETL has immense potential for scalability. Maintaining similar data units across an industry provides an opportunity to scale data immediately.
- Ease of transferability: Data integration will be easier during mergers and acquisitions if all parties are ETL compliant.
- Enabling greater ROI: Companies stand to profit greatly from adopting ETL processes. Per a study by the International Data Corporation, ETL implementations achieve a five-year median ROI of 112% and a mean payoff of 1.6 years.
Conclusion
Fine-tuning data through the ETL process greatly benefits both small and large-scale businesses. The process allows companies to analyze and store incoming data in ready-to-use formats that are compliant with industry standards.
Some advantages of ETL include scalability, historical relevance, profitability efficiency, and compliance. However, companies looking to embark on an ETL initiative should know that it’s a specialized process that’s often expensive to set up and maintain. It also consumes time and requires expertise.
Sign up for Panoply today to see how a modern ELT solution, paired with a managed data warehouse, can streamline your data stack. Panoply is a Managed ELT and cloud data warehouse that manages your data effectively, granting you access to top-notch analytical tools and secure storage.
