Extract, Load, Transform
What is ELT?
ELT stands for Extract, Load, Transform. It is a process used to move data from a source system to a destination system, both of which may be on-premise or cloud-based. During the process, data is extracted from the source system, either in its raw form or as an aggregated or summarized version of the data. Once the data is extracted, it is loaded into the destination system. Finally, any necessary transformations are applied to the data in order to make it ready for use in the destination system. This process can be done manually or automated using specific software tools designed for this purpose.
Why do we need ELT Tools?
ETL tools provide a centralized platform for data processing, allowing organizations to automate and streamline the process of extracting, transforming, and loading data into a target system. This makes it easier to manage large volumes of data and ensures that the data is accurate and up-to-date. Additionally, ETL tools can help reduce costs associated with manual data input and help improve overall efficiency. By using ETL tools, organizations can also create automated reports quickly, enabling them to make better business decisions faster.
How ELT is different from ETL?
ETL (Extract, Transform, Load) is a process in data warehousing that involves extracting data from outside sources, transforming it to fit operational needs, and then loading it into the end target database or data warehouse for reporting and analysis.
ELT (Extract, Load, Transform) is similar to ETL but is different in that the data is first loaded into the destination database or data warehouse before being transformed. This allows for more flexibility in terms of the transformations that can be applied and makes it easier to make changes to the transformation process without having to re-extract the source data.
Benefits of ELT when compared to ETL
- More efficient use of computing resources, as data is processed closer to its source
- Reduced data latency due to pre-transformed datasets being readily available
- Flexible and rapid development of advanced data analytics pipelines
- Improved scalability and extensibility for high-volume data processing
- Greater automation capabilities, reducing manual labor and improving accuracy
- Enhanced scalability for complex transformations, involving multiple sources and databases
- Increased availability of real-time insights into large datasets
List of commercial ELT Tools available today
- Amazon Web Services (AWS) Glue
- Informatica Intelligent Cloud Services
- Alooma
- Microsoft SQL Server Integration Services (SSIS)
- Talend Data Integration and Big Data
- Stitch Data
- CloverETL
- Hevo Data
- Fivetran
- Matillion ETL for Amazon Redshift
List of Opensource ELT Tools Available today
- Apache Airflow: Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It offers a rich set of features to facilitate the ETL process.
- Talend Open Studio: Talend Open Studio is a free, open-source data integration solution for quickly and easily convert raw data into meaningful information. It helps extract and transform data from any source into any target format.
- Pentaho Data Integration: Pentaho Data Integration (PDI) is an open-source ETL tool that simplifies the process of extracting data from various sources and transforming it into meaningful formats for analysis and reporting.
- Kettle: Kettle is a popular open-source ETL tool for quickly and easily converting raw data into meaningful information. It is written in Java and offers a wide range of features to help you quickly build ETL processes.
- Apache NiFi: Apache NiFi is an open-source ETL tool for securely transferring data from one system to another. It supports a wide range of data formats and offers an easy-to-use graphical interface for creating data flows between different systems.
- Informatica PowerCenter: Informatica PowerCenter is a comprehensive, enterprise-level ETL tool used to extract, transform, and load data from disparate sources. It also provides powerful graphical tools for designing, building, and managing ETL processes.
- Apache Sqoop: Apache Sqoop is a command-line interface for quickly extracting data from relational databases and loading it into Hadoop. It supports a wide range of data formats, including CSV, JSON, and Avro, and offers the ability to customize the ETL process.
- CloverETL: CloverETL is an open-source ETL platform that provides an easy-to-use graphical interface for creating ETL processes. It supports a wide range of data sources, including databases, flat files, and web services, and offers powerful features such as parallel processing and scheduling capabilities.