Course paper on the theme «Practice and assessment of in teaching pronunciation at all stages elt» done by: 3rd course student of Faculty of Foreign Philology English Teaching Metodology Sayfiddinova Sabohat

Extract. A data extraction tool pulls data from a source or sources such as SQL or NoSQL databases, cloud platforms or XML files. This extracted data is often stored temporarily in a staging area in a database to confirm data integrity and to apply any necessary business rules.
Load. The second step involves placing the data into the target system, typically a cloud data warehouse, where it is ready to be analyzed by BI tools or data analytics tools.
Transform. Data transformation refers to converting the structure or format of a data set to match that of the target system. Examples of transformations include data mapping, replacing codes with values and applying concatenations or calculations.
The main difference between the two processes is how, when and where data transformation occurs. The ELT process is most appropriate for larger, nonrelational, and unstructured data sets and when timeliness is important. The ETL process is more appropriate for small data sets which require complex transformations. Today, your business has to process many types of data and a massive volume of data. This can be a significant challenge for the traditional ETL pipeline and on premises data warehouses.²
Extract Load Transform (ELT)
In the ELT process, data transformation is performed on an as-needed basis within the target system. This means that this process takes less time. But if there is not sufficient processing power in the cloud solution, transformation can slow down the querying and analysis processes. This is why this process is more appropriate for larger, structured and unstructured data sets and when timeliness is important.
Extract Transform Load (ETL)
In the ETL process, transformation is performed in a staging area outside of the data warehouse and before loading it into the data warehouse. The entire data set must be transformed before loading, so transforming large data sets can take a lot of time up front. The benefit is that analysis can take place immediately once the data is loaded. This is why this process is appropriate for small data sets which require complex transformations. Building and maintaining a data warehouse can require hundreds or thousands of ETL tool programs. As a result, building data warehouses with ETL tools can be time-consuming, cumbersome, and error-prone — introducing delays and unnecessary risk into BI projects that require the most up-to-date data, and the agility to react quickly to changing business demands.
Data warehouse automation eliminates error-prone manual coding and automates the entire data warehousing lifecycle from design and development to impact analysis and change management. These automation tools automatically generate the commands, data warehouse structures, and documentation necessary for designing, building, and maintaining your data warehouse program, helping you save time, reduce cost, and reduce project risk. Seamless integration with a real-time event capture and data integration solution enables real-time ELT by combining real-time source data integration with automated generation—and supports a wide ecosystem of heterogeneous data sources including relational, legacy, and NoSQL data stores.