The perspective in the data domain has experienced multiple transformations. Due to the recent advances made in machine learning, the data management processes of organizations are started to reform like never before. The exponential growth of available and accessible data also demands modern solutions regarding the management and handling of immense data assets.The end-to-end routes of data architecture are known as pipelines. Every pipeline possesses one or more sources and target systems to access and manipulate the available data.
Data goes through various stages in these pipelines, including transformation, validation, normalization, etc. People often confuse the ETL Pipeline with ELT Pipeline.This blog post is intended to answer one of the most trending concerns of the masses. What is the difference between the ETL Pipeline and ELT Pipeline?
ETL Pipeline in a Nutshell
Data ETL Pipelines are architectures that involve specific processes, including extracting data from a source, its transformation, and then loading it into the target destination for different purposes like machine learning, statistical modelling, extracting insights, etc. The said target destination could be a data warehouse, data mart, or database. ETL stands for Extraction, Transformation, and Loading. As the title suggests, the ETL process involves:
- Data integration
- Data warehousing
- Data Transformation
The extraction involves fetching data from heterogeneous sources such as business systems, applications, sensors, and databanks. The next stage is data transformation, which involves converting into a defined and improved format to use by a multitude of applications. Last but not least, the accessible and improvised form of data finally loads into a target destination. The primary objective of building an ETL Pipeline is to employ the correct data, make it available for reporting, and store it for instant and convenient access. An ETL tool assists businesses and developers to spare time and effort to focus on core business processes. Various strategies exist to build ETL pipelines depending on a business’s unique requirements.
ETL Pipeline - Use Case
There are various scenarios where ETL pipelines can be used to deliver faster, superior-quality decisions. Data ETL pipelines are implemented to centralise all data sources and allow businesses to have a consolidated data version. Consider the Customer Resource Management (CRM) department that uses an ETL pipeline to extract customers’ data from multiple touchpoints during the purchase process. It can also allow the department to develop comprehensive dashboards that can serve as a single source containing customer information from different sources. Similarly, it often becomes essential for companies to internally transit and transforms data between multiple shelves. For instance, if data is stored in different intelligence systems, it becomes difficult for a business user to drive clear insights and make rational decisions.
ELT Pipeline in a Nutshell
ELT stands for "Extract, Load, and Transform." Data gets leveraged via a data warehouse to do fundamental transformations in this process. That means there's no need for data staging. ELT uses cloud-based data warehousing solutions for all data types, including structured, unstructured, semi-structured, and even raw data types. The ELT process also works hand-in-hand with data lakes. "Data Lakes" are particular kinds of data stores that accept structured or unstructured data, unlike OLAP data warehouses. Data lakes don't require you to transform your data before loading it. You can immediately load any raw information into a data lake, no matter the format or lack thereof. Data transformation is still necessary before analyzing the data with a business intelligence platform. However, data cleansing, enrichment, and transformation occur after loading the data into the data lake. ELT is a relatively new technology made possible because of modern, cloud-based server technologies. Cloud-based data warehouses offer near-endless storage capabilities and scalable processing power. For example, platforms like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities. ELT paired with a data lake lets you ingest an ever-expanding pool of raw data immediately as it becomes available. There's no requirement to transform the data into a special format before saving it in the data lake. ELT transforms only the data required for a particular analysis. Although it can slow down the process of analyzing the data, it offers more flexibility—because you can transform the data in different ways to produce different metrics, forecasts, and reports. Conversely, with ETL, the entire ETL pipeline—and the data structure in the OLAP warehouse—may require modification if the previously-decided structure doesn't allow for a new type of analysis.
ELT Pipeline - Use Case
ELT pipelines are helpful for accurately extracting and driving practical data insights. The methodology works well for businesses or companies that store and depend on multiple, vast chunks of data sources, perform real-time data analysis, and have their data stored in the cloud. For instance, ELT Pipeline tools and methodologies perform predictive analysis to filter the most probable future trends from the least probable ones. A production department can perform predictive analytics to determine if the raw material is likely to run out. It could also make forecasts about the possible delays in a supply line. In this way, these insights can help the production department handle its operations free from any resistance or errors.
Difference between ETL Pipelines and ELT Pipelines
Although ETL and ELT are closely related concepts, they have considerable differences. However, people often use the two terms interchangeably. ELT and ETL pipelines are both designated to shift data from one source to another; the main difference is the application for which the pipeline is designed. Some significant differences include:
The difference in terminology between ETL pipeline & ELT Pipeline
ETL pipelines possess mechanisms that fetch data from a source, transform it, and load it into the target destination. Whereas an ELT pipeline is a kind of broader terminology. It lacks the transformation phase (in-between) and functions by transferring data from a source to loading it into the target destination and then transforming the necessary pieces of data.
Purpose of ETL pipeline VS ELT pipeline
In a more straightforward means, ELT is intended to transfer data from sources, such as business processes, applications, sensors, etc., into a data warehouse to run intelligent and analytical methods by transforming the data later (if necessary). On the other hand, as the name suggests, the ETL pipeline is a specific kind of data pipeline in which data is extracted, transformed, and then loaded into a target destination. After extracting data from the source, the critical step is to adjust this data into a designated data model designed according to the specific business intelligence requirements. This adjustment includes accumulation, cleaning, and transformation of the data. In the end, the resulting data is then loaded into the target system.
Differences in how ETL and ELT Pipeline run
An ETL pipeline operates to fetch data in batches, which is done by moving a certain amount of data to the target system. These batches can be organized in such a way to run at a specific time daily when in case of low system traffic. On the other hand, an ELT doesn't stockpile from the source and can be deployed as a real-time process by ensuring every event must be handled as soon as it happens instead of in batches. For instance, transfer data coming from an air traffic control (ATC) system. Moreover, the ELT pipeline doesn’t require adjusting data before loading it into a database or a data warehouse. This data can be loaded into any destination system, such as the Amazon Web Services bucket.
ETL is more reliable than ELT
It’s important to note that the tools and systems of ELT are still evolving, so they're not as reliable as ETL paired with an OLAP database. Although it takes more effort to set up, ETL provides more accurate insights when dealing with massive data pools. Also, ELT developers who know how to use ELT technology are more difficult to find than ETL developers.
There is no opinion about which methodology is best, ETL or ELT. These methodologies are used in different scenarios and address different needs. Get in touch with us if you are looking for ETL or ELT implementations.