=================================================================================

Extract, load, and transform (ELT) emerged because of big data processing. All the data resides in a data lake. A data lake is a pool of raw data for which the data purpose is not predefined. In a data lake, each project forms individual transformation tasks as required. It does not anticipate all the transformation requirements usage scenarios as in the case of ETL and a data warehouse. Organizations opt to use a mixture of ETL and ELT.

Extract, transform, load (ETL) plays a crucial role as the initial phase in any data processing pipeline, supplying data to warehouses for subsequent use in applications, machine learning models, and various services. In the final stage of the ETL pipeline, data can be saved to a disk or transferred to a different database. Additionally, there is the option to output the data as a JSON file on the disk or to store it in another database, such as PostgreSQL. Alternatively, an API can be utilized to move the data to a database, for example, a PostgreSQL database.

ETL process is a broad term used in data handling and data warehousing that involves:

• Extracting data from homogeneous or heterogeneous sources,
• Transforming the data, which may involve cleaning, filtering, aggregating, and otherwise preparing it for analysis, and
• Loading the data into a final target database, data warehouse, or data lake.

The primary goal of data transformation is to change the data into a format that is useful for business users. This process involves converting data from one format or structure into another, often to make it more suitable for analysis, reporting, or specific business applications. It can involve activities such as cleansing, aggregating, and reorganizing data to meet specific needs.

The tools and libraries from the Apache ecosystem — Spark, Arrow, and Flink (see page3328) — can be involved in various stages of an ETL pipeline, especially the extraction and loading parts. They also have capabilities for performing complex transformations on the data as part of processing workflows, making them suitable for comprehensive ETL tasks. ETL is often a crucial first step in a machine learning (ML) pipeline.

The relationship between ELT (Extract, Load, Transform), data lakes, and data warehouses is interconnected and integral to modern data architectures, especially in big data and analytics-driven environments:

• ELT (Extract, Load, Transform):
  • ELT is a variant of the traditional ETL process where the order of operations is changed. In ELT, data is extracted from various sources and loaded directly into a data storage system, like a data lake or data warehouse, before any transformation occurs. This approach leverages the processing power of modern data storage systems to perform transformations on the data after it has been loaded. This can be more efficient for handling large volumes of data, as it allows for more scalable and flexible transformations.

• Data Lake:
  • A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. Data lakes are typically used to store unstructured and semi-structured data, and they provide a high level of flexibility because they allow you to store all types of data without having to define the data structure at the time of storage. This makes data lakes highly compatible with the ELT approach, where data can be dumped into the lake and then transformed as needed using powerful processing tools like Apache Spark or Hadoop.

• Data Warehouse:
  • A data warehouse is a system used for reporting and data analysis, and it is a core component of business intelligence. Data warehouses are designed to store structured data in a defined schema, making them ideal for supporting queries and analysis. They traditionally utilized an ETL process to ensure that data is cleaned and transformed before being loaded into the warehouse's structured format. However, with the advent of more powerful data warehousing technologies, such as Google BigQuery and Amazon Redshift, the shift towards ELT processes is becoming more common, where raw data is loaded into the warehouse and transformations are performed within the warehouse itself.

• Interrelationship:
  • The choice between using a data lake and a data warehouse often depends on the specific needs of the organization regarding data analysis and the types of data involved. Data lakes and data warehouses can also be used in conjunction, where a data lake serves as a staging and storage area for raw data, which can then be processed (transformed) and moved into a data warehouse for more complex analysis and reporting.
  • ELT is particularly beneficial when used with data lakes and modern data warehouses because it allows organizations to manage and process very large volumes of data more efficiently. The transformation process in the storage layer takes advantage of the powerful compute resources of these systems, enabling more dynamic and complex data manipulation capabilities.

===========================================