ETL Process Overview

ETL Process Overview

ETL stands for Extract, Transform, and Load, which is a process of merging data from various sources into a single and consistent data storage which is then stored in the data warehouse or in any target system.

Understanding the ETL process can help you enforce data quality as well as save time on development work. Here’s your guide to understanding what an ETL really does. 

Extracting data from various sources that are not optimized for analytics is one of the most difficult tasks in data warehousing. ETL typically involves three stages: the extract stage, the transform stage, and the load stage. 

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Data is extracted from its source; (which may be structured, semi-structured, or unstructured); it is transformed into another form and then it is loaded into an enterprise database.

The traditional ETL process

The traditional ETL process

Data is extracted from online transaction processing (OLTP) databases, which are now more commonly referred to as “transactional data,” as well as other data sources.

Data in these systems is in the form of log records in a sequential manner with a timestamp for each record. This data can be extracted by various means like PL/SQL packages or programming languages like python or other available tools in the market like Informatica, Oracle Data Integrator or SQL Server Integration Services etc,.

Business intelligence (BI) teams then run queries on the data, and present it to an end-user, or to an individual responsible for making decisions, or use it as input for machine learning algorithms or other data science projects.

ETL process for Data Warehouses 

Data warehouses are valuable tools in data analysis and decision-making, but the process of turning raw data into a usable form for analysis can be complicated. 

In order to harmonize data from many sources, it must go through a rigorous ETL process. This step in the data warehouse lifecycle is entirely manual and often requires advanced technical skills, making it time-consuming and expensive.

Most businesses that work with large amounts of data depend on data Extraction, Transformation, and Loading (ETL) processes to keep the data in their data warehouse up-to-date. These businesses must use the ETL process to take information from multiple systems, process it, and make it available for analytics. If this process does not happen regularly, the data becomes out-of-date.

The biggest advantage of the ETL process  is that you can cleanse the data and standardize it  before it is loaded into the warehouse, which improves the quality of the data.

You can also automate this process to save time. It also means that the processed data can be consumed more quickly and efficiently, which speeds up decision-making.

This lets the BI team, data scientists, and analytics specialists automate complex process, improve performance and work with clean data 

Critical ETL components

When designing ETL processes, there are some critical components you should consider. These include: 

  • Purging Data: For many data warehouses and analytics systems, the purging of stale  or redundant data is a necessary task. ETL components are responsible for the purging of the data. The ETL component needs to identify and remove any unnecessary data from databases and other storage repositories.  This process is automated by adding rules to identify stale data and purge it. 
  • Data Transformation: This is where most of the data cleansing and validation happens. Example: converting NULL to 0 in numeric  field. You can filter data, apply rules to lookup for values in other tables, split a column or merge multiple columns. You will also perform various aggregations on the source data. Ex: Aggregating the sales data. All these transformations can be automated using various ETL tools.
  • Data Loading: Data loading is the last step of the process where data is loaded to the target database/ data warehouse after transformation. We can configure the ETL process to do a full load or incremental load based on the business needs. Full load will truncate all the data that was loaded in the previous ETL run and reloads all the data from source. Incremental data load will identify any new or updated records in the source from the time the last ETL refresh was done and load only the new or updated records. 

Conclusion

In conclusion, ETL processes are complicated, but the benefits of having a standardized process can make it worth the effort. ETL is often necessary even for small companies, and being aware of best practices will help ensure that your business doesn’t miss anything and that you’re giving yourself a strong foundation to grow on.

Many businesses have spent years developing their systems to work just right for their business needs. And you want to be the same? spending many years in this research? don’t worry DiLytics can help you with it.

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