Data integration is crucial to businesses to be able to collect data from multiple sources and create a standardized format for analysis and storage. One of the processes that this is completed is through what’s called extract, transform, and load, or ETL.
Organizations have huge amounts of data. Many have trouble moving information from their source systems, and the ETL process creates efficient functions From cloud-based tools to hand-coding and batch processing, let’s take a closer look at what ETL can offer companies of any size.
Master data management can be a key cog in forwarding an organization, laying out goals, and achieving them in real-time. ETL software offers the tools to turn raw data into incredible analytics. This starts with extracting data, collecting information from multiple sources. This can be everything from customer relationship management systems and legacy systems to customer transaction data and social media. Data extraction is often performed in three different ways.
Some data sources provide a notification to the ETL system when there is any data change. The ETL system only needs to extract from new data sources that provide notification based on extraction. Incremental data extraction offers a more complex method to a data integration process. This requires periodic checks into data sources to see if there’s any change. ETL systems will incrementally extract that new data to update your architecture. Lastly, there’s full data extraction. This involves a higher volume of data transfer than any of the other methods because each time, the entire dataset needs to be copied over for review.
The next step in ETL is transforming data, taking all of the facts from different sources, and standardizing them to allow for greater efficiency and ease of visibility. The format of the extracted data from various sources may vary greatly. Standardization brings the data to a common format with the business rules that apply to it. Data integration software can come through vast amounts of information, spotting things that may not be relevant through what’s known as the cleansing process. This removes the noise in the data and fixes inconsistencies.
Raw data then enters the deduplication process, removing any repetition and redundant information. Format revision also allows for ease of use by creating a standard for all data types to abide by going forward. This may include the unit of measurement conversion, date-time conversion, or conversion of a character set. This wraps up with verification and validation of these statistics. Data integrity is imperative in order for an ETL system to work properly. The transformation includes data aggregation and filtering data to maintain proper data flow, leading to greater analytics and better decisions for the business.
In the final step of ETL, the transformed data is loaded into a data warehouse or another data source. There are two main ways in which a business can load data. The first method is referred to as a full load, delivering all of that transformed data into a warehouse in one single batch. While this takes a long time, it’s less complex than the incremental load method. However, the full load might lead to an exponential growth in data volume. This could lead to some hurdles in master data management depending on the streams of data.
Incremental load looks for changes in incoming data sets. This involves creating a new data record only if unique information is discovered. Incremental load is much more manageable than full load, but it may lead to inconsistencies in the event of an ETL system failure. This method can be used to create a layer of analytics or business intelligence over the data or as a training set to a machine learning algorithm.