ETL (Extract, Transform, Load) is a process that involves extracting data from a source, transforming it to meet the requirements of the target destination, and then loading it into a said destination. It can even be used to migrate data from one database to another or even from one type of database to another. Various types of ETL tools and approaches can be used depending on the specific situations as well. Data professionals need to be familiar with the ETL process in order to move data efficiently between different systems.
As mentioned, ETL stands for extract, transform, load.
Extract: The first step in ETL is to extract the data from its current location. This can be done manually or through an automated process.
Transform: The next step is to transform the data into the desired format. This may involve cleaning up the data, converting it to a different format, or performing some other type of transformation.
Load: The last step is to load the transformed data into its new destination. This can be done by importing it into a new database, file, or another type of data store.
What is an ETL pipeline?
An ETL data pipeline is a process for extracting data from one or more sources, transforming it into a format that can be used by downstream applications, and loading it into those applications. ETL pipelines are common in data warehousing and business intelligence applications, where they are used to extract data from transactional systems, transform it into a format that is suitable for analysis, and load it into data warehouses or business intelligence tools.
ETL pipelines can be complex, with multiple stages that must be executed in a specific order. For example, data extractors may need to run before transformers, and transformers may need to run before loaders. ETL pipelines can also be triggered by events, such as the arrival of new data in a source system.
ETL pipelines can be built using a variety of tools and technologies. Some ETL tools are designed to work with specific types of data sources or target applications. Others are more general-purpose, providing a framework that can be used to build ETL pipelines for a variety of purposes.
4 common ETL challenges
There are four common ETL challenges data professionals face. These include:
1. Data cleansing
This is the process of identifying and cleaning up inaccuracies and inconsistencies in data. ETL tools can help automate this process to some extent, but it can still be time-consuming and resource-intensive.
2. Data transformation
This is the process of converting data from one format to another. ETL tools can help with this, but it can still be a challenge to ensure that all data is transformed correctly.
3. Data loading
This is the process of loading data into a target system. ETL tools can help with this, but it can still be a challenge to ensure that all data is loaded correctly and efficiently.
4. Data management
This is the process of managing ETL data sources, transformation rules, and target systems. ETL tools can help with this, but it can still be a challenge to keep track of everything and ensure that processes are running smoothly.
Types of ETL tools
There are four main types of ETL tools:
Batch processing ETL tools
These tools are used to extract data from a variety of sources, transform it into a consistent format, and load it into a target data store. However, the process is done in batches, meaning that the data is not always up-to-date.
Cloud-native ETL tools
These tools are designed to be used in a cloud environment. They are often more scalable and flexible than traditional ETL tools.
Open source ETL tools
These tools you can find for free. They are often community-driven and can be customized to fit your specific needs.
Real-time ETL tools
These tools are used to extract data from a variety of sources, transform it into a consistent format, and load it into a target data store. However, the process is done in real-time, meaning that the data is always up-to-date.
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