Extract, transform, and load. Those three steps to data integration have become of great use to businesses across multiple fields. Organizations use this process to collect from multiple sources, create a standard for formatting, and load all of that useful information into a data warehouse. There’s a huge amount of data at a company’s disposal, but it’s important to have capabilities that transform that raw data into real-time analytics that directly impact business decisions and strategy in the long run. Let’s take a closer look at how the ETL process can be a game-changer.
The first step of ETL is, of course, extraction. This is the process of collecting statistics from multiple sources for data analysis. This can be from anything from customer transaction data to posts on social media. Data extraction is performed in three different ways. One of these methods is done based on notification of a change. An ETL system will provide analysts with a transparent view of these changes. This is one of the easiest methods that is based solely on extracting new data, but some sources do not provide notifications.
There’s also incremental data extraction, where an ETL system periodically checks sources with the help of data integration tools to find any changes in the information at hand. The incremental extraction process is more complex than notification-based extraction. Some sources may not be able to provide notifications on that change. Finally, there’s full data extraction, which is an overwhelming process that extracts from large data sets at a single given time. This requires the ETL system to keep a copy of the last extract to offer up a comparison to a new copy of this higher volume of data.
Data from different sources might have different structures. That’s why the transformation stage of the ETL process is crucial to creating a set of standards for diverse data sources. Organizations often apply business rules during this phase, starting with the creation of a common format. Within transformation is the process of data cleansing. This is where data engineers delve into everything from email communications to transactions to eliminate irrelevant materials. Cleansing helps to remove the noise in the data, including missing values and inconsistencies.
There’s also the deduplication phase, where raw data is combed for repetition and redundancy. From that, an ETL platform enters into format revision, converting data to new standards. This can include conversion of certain measurement units or character sets. Lastly, the data stream goes through a verification process. This identifies any data anomalies. Transformation also includes advanced responsiveness, such as data aggregation, filtering queries, or establishing a key-value relationship.
The ETL process ends with loading newly transformed data into a warehouse or into other data sources. There are two main ways to load for proper data analytics. The first is the full load, which takes all data in with one single batch. While the ‘full load’ is an arduous process, it’s less complex than incremental loading. The full load might lead to an exponential growth in a data warehouse, where large amounts of data can become difficult for proper master data management.
An incremental load looks for changes in incoming new data. This is more manageable than a full load, but it may lead to inconsistencies that can ultimately cause failures in data warehousing. Incremental loading can be used to create layers of analytics or business intelligence over this newly transformed information. It can also be used for a searchable database for those now unified separate sources. Now that your business can better process data, there are better business decisions and strategies that can be made for the greater good of a company.