ETL vs ELT Use Cases
ETL and ELT are data manipulation processes. These tools allow data scientists and other experts to process data into information. However, each scheme is used differently and for diverse uses. This article is going to explore the use cases between the two terms. Before then, you will learn the definitions and differences between the two processes.
ETL means to extract, transform and load. This term defines the data copying process from a source(s) to a storage destination. However, the source and destination are mostly different. In most cases, this process is used within the data warehousing system. ETL allows you to collect data, convert it into a uniform format, and store it in a storage system.
The extraction part of ETL works on similar or diverse sources of data. On the other hand, transformation and data cleaning are used at the transform stage. Finally, the load stage refers to the process of storing data within a database.
ELT means to extract, load, and transform. It gives you an alternative version of ETL. However, ELT requires more power than ELT. Therefore, data is extracted then loaded before it is transformed. This process is used when fast loading is needed.
Differences Between ETL and ELT
Below are some differences between ETL and ELT.
- ETL moves transform data before storing it. However, ELT uses databases or data warehouses to transform after storage.
- ETL is cheaper than ELT. It also requires a lot more power.
- ETL is ideal for compliance and data security. It does this by cleansing sensitive information before storage. Also, ELT can perform better transformations than ETL.
Use Cases of ETL
ETL has several uses. Below are some use cases to consider.
- ETL is often used by companies that go into a joint venture. Each business has different clients, associates, and service providers. All this data is mostly sorted using different protocols on varying storage systems. For them to work together, they will need to combine their data. ETL allows them to synchronize their business information into a single database with the same format.
- ETL is often used when an organization transfers its data from an old system to a newer one. ETL lets you transform and load data onto the new system considering all variables.
Use Cases of ELT
The ELT use cases are shown below.
- ELT works well with institutions with massive databases. It works best when the destination is a cloud-based system. Similarly, it works for both unstructured and structured data.
- ELT is ideal for large companies with plenty of resources. Since this process requires a lot of power, a business can transform all extracted data within its data storage system. In addition, so much money is needed to prevent security problems.
- Unlike ETL, ELT is faster. Using ELT lets it move data faster when required. This speed is because ELT does not transform until the data gets to the warehouse. Therefore, a business uses ELT to extract and move data as soon as possible. Similarly, a company can still use ELT occasionally even though they primarily use ETL.
Other useful articles:
- How to Extract Data from PDF
- Data Visualization
- Data Analysis
- Web Data Extraction
- Data Labeling
- Data Portability
- Brief Introduction of PDF Extractor SDK
- History of PDF
- Data Extraction Techniques
- Using Google Analytics for Data Extraction
- Data Extraction from PDF
- Data Extraction Software
- Using Python for Data Extraction from PDFs
- Web Scraping Tools to Save Time on Data Extraction
- Data Extraction Use Cases in Healthcare
- Data Extraction vs Data Mining
- Data Extraction and ETL
- TOP Questions about Data Extraction
- How Data Extraction Can Solve Real-World Problems
- Which Industries Use Data Extraction
- Types of Data Extraction
- Detailed Data Extraction Process
- Things to Consider Before Data Extraction
- What is an ETL Database
- How ETL is Done
- Is ETL Part of Data Science
- Who Works with ETL
- ETL vs ELT Use Cases