Data can spring up from multiple sources, and businesses need to be equipped to handle the amount of information that is at their disposal. It’s important to have systems in place that not only address information in real-time but also offer up ways of tracking down trends within what’s already in your data warehouse. However, this process in the past has been long in a complex source system. However, that has been transformed by what we call the ETL process.
Extract, Transform, and Load
You’re probably wondering, what is ETL? Extract, transform, load, or ETL, is a data integration process that collects information from multiple sources, standardizes it, and loads it into a warehouse for analysis or databases for storage. Organizations are using ETL to transform their data that is spread across multiple systems into unified formats and styles. This effort to transform data is a way that some companies are trying to rein in the large volumes of data at their disposal. ETL helps organizations utilize these facts more efficiently, enhancing business intelligence.
ETL has found use across multiple fields, particularly deriving value from customer data. This process collates all customer data from various sources, transforming the information to adhere to a standard format. It is then loaded into a data warehouse or other data source for analysis. This gives an organization a greater scope of customer interactions with a brand, as this can happen in different ways depending on the company. It also promotes a highly personalized experience within the customer service realm.
The Steps of ETL
ETL breaks down into three major steps: extract, transform, and load.
The process starts with extracting data from sources ranging from legacy databases to customer transaction information. Data extraction can be done based on notification of a change in an ETL system for new data. Incremental data extraction offers a more complex operation, periodically checking data through the extraction process to identify changes. Full data extraction involves a higher volume of data transfer than other methods, as it takes time to copy over all datasets to load into an ETL system.
Transforming data types can take time depending on the amount of data and its different characteristics. This starts with standardizing extracted data to be brought into a common format. From there, data undergo cleansing to fix missing values and inconsistencies. That’s followed by deduplication, removing repeats in raw data, and avoiding redundancies. Data assets then undergo format revision and then verification to check data integrity and maintain reliable data systems.
Loading data can be done in a full load that takes a significant amount of time depending on the data volume, or in an incremental fashion that puts relevant data as a priority to gain business insights with some immediacy.
Types of ETL Tools
Depending on the different types of data that your business may be dealing with, you’ll want to consider which ETL tools to utilize for operational efficiency. Batch processing tools, for example, process the data during off-hours so as not to interfere with daily operations. This is best for businesses that don’t have to rely on real-time ETL capability. Those that need real-time analytics should rely on open-source tools for assistance and instant availability online.
Cloud-based tools offer an ETL platform as a service, ensuring quick access and easy integration, with scalability to back up the ongoing process. Real-time tools use continuous data processes to extract data from multiple sources and set them aside in a data warehouse. These tools are useful in processing stream data, or data from internet of things use cases. It’s all about finding the best technology for better decisions for your business.