In the 1970s, ETL became the first standardized method for facilitating data integration. Enterprise businesses adopted multi-pronged computer systems and heterogeneous data sources, and ETL grew in popularity. These companies needed a way to aggregate and centralize data from transactions, payroll systems, inventory logs, and other ERP data.
Cloud data lakes and data warehouses ushered in a new era of ELT with the rise of cloud computing in the 2000s. Businesses could use ELT to load an unlimited amount of raw data directly into a cloud DWH. Engineers and analysts could run an endless number of SQL queries on top of this raw data, all from within the cloud data warehouse. Businesses could finally gain access to the analytical power and efficiency that big data had always promised. ELTs ushered in a new era of analytics and data-driven decision-making when combined with visualization tools and cloud DWHs.
This post talks about ETL Vs ELT and helps you determine which one to choose and which is better.
Table Of Contents
- What is ETL?
- What is ELT?
- ETL Vs ELT: Comparison
- What to choose?
What is ETL?
Extract, transform, and load (ETL) is a data integration method that extracts raw data from sources, transforms it on a secondary processing server, and then loads it into a target database.
Data Warehouses that use Online Analytical Processing (OLAP) must work with relational SQL-based data structures, whether they are cloud-based or on-premise. As a result, any data you load into an OLAP data warehouse must first be transformed into a relational format before it can be ingested. Data mapping may be required as part of this data transformation process to combine multiple data sources based on correlating information (so your business intelligence platform can analyze the information as a single, integrated unit).
That’s why data warehouses necessitate ETL—transformations must occur prior to loading.
When data needs to be transformed to conform to the data regime of a target database, ETL is used. The method first appeared in the 1970s and is still widely used in on-premise databases with limited memory and processing power.
Some examples of ETL Tools are Informatica PowerCenter, SAP Data Services., Talend Open Studio & Integration Suite, SQL Server Integration Services (SSIS), IBM Information Server (DataStage).
There is a plethora of ETL tools available (one of which is SSIS ETL), and deciding which one to use can be difficult.
There are a plethora of ETL tools available (one of which is SSIS ETL), and deciding which one to use can be difficult.
SQL Server Integration Service ETL (SSIS ETL) is an acronym that stands for SQL Server Integration Service. SSIS ETL is a component of the Microsoft SQL Server Database that performs Data Integration, Transformation, and Migration tasks. The object model, runtime, data flow, and service are the four components of the architecture.
Prior to SSIS ETL, Microsoft produced DTS (Data Transformation Services), which was a legacy solution. The Microsoft team renamed SQL Server 2005 and effectively replaced it by updating it to the latest technology. The 2008 SQL Server was the next to be updated, with new sources and a slew of other changes.
SSIS ETL 2012 introduced new features such as easier package configuration and improved storage. SSIS ETL 2014 and 2016 only added minor differences, such as the ability to deploy individual packages and entire projects, new sources, and improved support.
What is ELT?
ELT stands for “Extract, Load, and Transform.” Data is leveraged via a data warehouse in this process to perform basic transformations. This eliminates the need for data staging. For all types of data, ELT uses cloud-based data warehousing solutions, including structured, unstructured, semi-structured, and even raw data.
Extract, load, and transform (ELT), unlike ETL, does not require data transformations prior to loading. Instead of moving raw data to a processing server for transformation, ELT loads it directly into the target data warehouse.
Cleansing, Enrichment, and Transformation of Data all take place within the data warehouse when using ELT. The data warehouse stores raw data indefinitely, enabling multiple transformations.
The invention of scalable cloud-based data warehouses paved the way for ELT, which is still relatively new.
Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure are all cloud data warehouses with the digital infrastructure to support raw data repositories and in-app transformations.
Despite the fact that ELT isn’t widely used, it’s becoming more popular as businesses adopt cloud infrastructure.
Data lakes and the ELT process are complementary. Unlike OLAP data warehouses, “Data Lakes” are specialized data stores that accept both structured and unstructured data. Before loading your data into a data lake, you don’t have to transform it. Any type of raw data, regardless of format or lack thereof, can be immediately loaded into a data lake.
ETL Vs ELT: Comparison
ETL Vs ELT: Technology and Availability of Tools
ETL is a well-established process that has been in use for more than two decades, and ETL experts are readily available. ETL has been around for over two decades as a data integration/transformation process, which means there are a plethora of well-developed ETL tools and platforms to help with data extraction, transformation, and loading requirements. Also, skilled and experienced data engineers who can set up ETL pipelines are readily available
Because ELT is a relatively new technology, finding experts and developing an ELT pipeline can be more difficult than developing an ETL pipeline.
ETL Vs ELT: Availability of Data
When you create the data warehouse and ETL process, ETL only transforms and loads the data that you decide is required. As a result, only this data will be accessible.
ELT can load all data at once, allowing users to choose which data to transform and analyze later.
ETL Vs ELT: Calculations
Calculations can be appended to the dataset to push the calculation result to the target data system, or they can be used to replace existing columns.
ELT directly adds calculated columns to an existing dataset.
ETL Vs ELT: Compatibility with Data Lakes
In most cases, ETL is not a viable option for data lakes. It prepares data for use in a structured relational data warehouse system by transforming it.
For data lakes, ELT provides a pipeline for ingesting unstructured data. After that, it transforms the data as needed for analysis.
ETL Vs ELT: Compliance
Compliance is another important benefit of ETL over ELT. To protect their clients’ privacy, businesses governed by GDPR, HIPAA, or the CCPA must frequently remove, mask, or encrypt specific data fields. This might entail converting emails to just the domain name or removing the last part of an IP address. Because it transforms data before putting it into the data warehouse, ETL provides a more secure way to perform these transformations. ETL reduces the risk of compliance violations by ensuring that non-compliant data never finds its way into a data warehouse or is reported by accident.
ELT, on the other hand, requires you to upload sensitive information first. As a result, it appears in logs that SysAdmins can access. Furthermore, if non-compliant data leaves the EU when uploaded to a data lake, using ELT to transform data may inadvertently violate the EU’s GDPR compliance standards.
ETL Vs ELT: Load Time
Because ETL is a multi-stage process, it takes longer to load data than ELT: (1) data loads into the staging area, (2) transformations, and (3) data loads into the data warehouse. Analysis of the information is faster than ELT once the data has been loaded.
Data loading is faster because no transformations are required and the data is only loaded once into the target data system. The analysis of the data, on the other hand, is slower than ETL.
ETL Vs ELT: Time to Perform Transformations
Data transformations take longer at first because each piece of data must be transformed before being loaded. Transformations also take longer as the size of the data system grows. Analysis, on the other hand, happens quickly and efficiently once the data has been transformed and entered the system.
Transformations are much faster because they happen after loading and on an as-needed basis—you only transform the data you need to analyze at the time. The need to transform data on a regular basis, on the other hand, slows down total query/analysis time.
ETL Vs ELT: Costs
Integrate.io, a cloud-based SaaS ETL platform that bills on a pay-per-session basis, offers flexible plans that start at around $100 and go up from there, depending on usage requirements. Meanwhile, a high-end onsite ETL solution like Informatica could cost more than $1 million per year.
Flexible plans start at around $100 and go up from there for cloud-based SaaS ELT platforms that bill on a pay-per-session basis. One of the cost advantages of ELT is that you can load and save your data without paying a lot of money, and then apply transformations as needed. If all you want to do is load and save data, this can save you money on upfront costs. Financially strapped businesses, on the other hand, may never be able to afford the processing power required to fully benefit from their data lake.
ETL Vs ELT: Maintenance Requirement
Integrate.io, for example, is a cloud-based ETL solution that requires very little maintenance. An onsite ETL solution that uses a physical server, on the other hand, will necessitate regular maintenance.
Because ELT is cloud-based and usually includes automated solutions, it requires very little upkeep.
ETL Vs ELT: Hardware Requirements
Integrate.io and other cloud-based ETL platforms don’t require any special hardware. The hardware requirements for legacy, onsite ETL processes are extensive and costly, so they aren’t as popular as they once were.
ELT processes are cloud-based and do not necessitate the use of special hardware.
ETL Vs ELT: Data
Smaller data sets requiring complex transformations are best served by ETL.
As the size of the dataset grows, aggregation becomes more difficult. Unstructured data can be structured using ETL, but it cannot be passed into the target system.
ELT is the most efficient way to deal with large amounts of structured and unstructured data. You can process massive amounts of data quickly if you have a powerful, cloud-based target data system. ELT is a method of putting unstructured data into a data lake and making it accessible to business intelligence systems.
Which to Choose?
Cloud data warehouses have ushered in a new era of data integration, but the decision between ETL and ELT is based on the needs of the team.
Despite the fact that ELT offers exciting new benefits, some teams will stick with ETL because it makes sense for their deployment, legacy infrastructure or not.
Whatever option they choose, data teams across the board are using a data integration platform to successfully implement their integration strategies.
Conclusion
This post explains ETL and ELT in details and helps you choose the right solution for your company. It also discusses ETL Vs ELT and helps understand the advantages of ETL and ELT.