If you want to know more about ETL, data science, and data processing, you will be interested in how the ETL process is used in data analysis. Modern businesses collect raw data from a variety of data sources. However, in most cases, they can’t ensure that the data format is consistent between multiple sources or that the data quality is sufficient for analysis.
The ETL process is vital to HiTech data analytics applications and tools. This post will thoroughly explain what ETL is and how it is used in data science to extract data from multiple sources and ensure data formats are uniform for further analysis.
What Is ETL?
ETL (extract, transform, load) is a data integration process used in data science to create a unified data repository that can be used for data analytics. A simple ETL pipeline looks like the following: raw data is extracted from an organization’s data sources, then transformed to improve data quality and ensure consistent formatting, and finally, transformed data is loaded into a target database or data warehouse.
ETL is the primary method of data cleansing and integration used in data warehousing. In addition, ETL forms the backbone of Machine Learning and data analytics processes in modern data engineering tools. ETL processes also transform data in a manner that specifically meets the unique business intelligence requirements of organizations and can improve business processes and end-user experiences.
Why Choose ETL?
If your business has legacy systems that house valuable data streams, the ETL process can be used to process data and integrate it into your organization’s current data pipeline. However, don’t lose data stored in an old data warehouse. Modern ETL tools are used to extract data from legacy data warehouses.
While ETL tools have been widely used for data integration since the 1970s, other data integration methods have been emerging to perfect data extraction and transformation. The most significant data integration improvement in data science since ETL is ELT.
What Is ELT?
ELT (extract, load, transform) is similar to ETL, but source data is loaded into a target system before transforming. ELT loads all raw data into a target database and only transforms data as needed.
Why Choose ELT?
ELT is the modern solution. Businesses have many cloud-based ELT solutions to choose from, and the capabilities of these tools continue to improve dramatically. In addition, ELT solutions tend to be more cost-effective for small and medium-sized businesses.
Both ETL and ELT leverage a variety of data repositories, including data warehouses, data lakes, and traditional databases. However, despite their immense similarities in extracting data, there are advantages and disadvantages associated with each of these data integration tools.
The Advantages of ETL
ETL is best suited for processing smaller, relational data sets that require complex transformations. However, this requires data engineers to pre-select multiple data sources deemed relevant to the analysis goals of the organization.
ETL is the better option for compliance with common privacy and security standards like HIPAA, GDPR, and CCPA. In addition, ETL is more secure than ELT because data engineers can omit a sensitive data point before it is loaded into a target data store.
The ETL pipeline and process have been around for decades. As a result, a variety of well-tested ETL tools, best practices, documentation, support professionals, and implementation experts are available to help your organization. ETL is a tried and true data integration process proven to improve data pipelines.
The Disadvantages of ETL
Despite being the standard in data science, there are some disadvantages to using the ETL pipeline. First, since data transformations take place outside the target data warehouse in a staging area before data loading occurs, it takes the ETL process longer to transform large data sets than ELT.
In addition, ETL can be expensive. High costs can keep small and medium-sized businesses from using ETL in their data pipelines. New cloud-based ETL solutions are the most cost friendly, but they can still be expensive and drive interested businesses away from using them.
The Advantages of ELT
ELT can handle data of any size and is adept at handling structured and unstructured data sets. The ELT process was developed to provide a consistent data store for data lakes and lake houses. Therefore, if your business utilizes a data lake, the ELT process is better suited to its needs.
The ELT process is faster at loading information to a target data source because data is selectively transformed as needed. Instead of transforming data before moving data into a data warehouse, all data is loaded into the warehouse from the point of data capture.
The ELT ecosystem is densely populated with cloud-based providers, giving businesses more options and reducing costs. In addition, this variety of providers and options gives businesses more choices when it comes to data storage and data virtualization.
The Disadvantages of ELT
The most significant disadvantage of the ELT process is its modernity compared to ETL. Since ELT is significantly newer than ETL, there are far fewer implementation experts, and best practices are still being developed.
Since the data transformation step of ELT happens as needed, the transformation process can take longer than ETL, especially when users actively query the data warehouse or there isn’t a lot of processing power.
ELT also carries more security risk than ETL because all data is indiscriminately loaded into the target data repository. As a result, ELT solutions are more likely to run afoul of GDPR, HIPAA, and CCPA security standards.
ETL or ELT: How Do You Choose?
How do you make the right data decision for your business? The answer truly depends on the needs of your organization and the hardware and systems currently in place. For some businesses, the best option will be ELT, and for others, it will be ETL.
There is no doubt that ELT offers new exciting advantages to ETL. However, ETL is a more established process and works far better with legacy technologies than ELT. Furthermore, ETL is more secure and offers a more secure process for handling sensitive data.
If you need to migrate data and transform it for analysis, your best option is to speak with an expert about the best path for your organization. If you thoroughly analyze your organization’s needs, the right choice should be obvious.
Extract, transform, and load has long been the gold standard in data integration. While ETL is still a critical part of modern data processing, a new process, ELT, has emerged. Choosing which option is best for your organization’s data needs can be tricky.
On the one hand, ELT is newer, faster, and more cost-effective than ETL. However, ETL is more secure, it’s a proven process, and it transforms data faster, even if it takes longer to load data. There are good reasons to choose either option.
If your business needs help making a choice, reach out to an experienced app development partner. A partner can help you take a detailed look at your business processes and data needs and help your organization make the right data decision.