Effective data movement and transformation are critical to business growth and innovation. Loading in the datastore can take a long time. The data extraction, loading, transformation and optimization are up-to-date data warehouses and big data management tasks. This allows companies to focus on extracting helpful information from the data.
What is ETL and ELT
The ultimate goal of the data engineer is to collect data from different sources in a single storage and bring them to the desired view. This process is called ETL or ELT. ETL means to extract, transform and load, while ELT means to extract, load, and transform.
ETL tool extracts data from different source systems, converts data, and then loads them into the data storage system. The ETL receives data from the source to the target. In ETL, the process transformation engine takes care of any data changes.
ELT is the process of extracting data from one or more sources and loading it into the target datastore. Instead of converting the data before saving it, ELT uses the target system to convert the data. This approach requires fewer remote sources than other methods because it uses only raw and unprepared data.
5 Key Differences between ETL and ELT
The difference between ETL and ELT tools is related to the order of the process. These methods are suitable for different situations:
ETL downloads data first to the intermediate server and then to the target system, while ELT downloads data directly to the target system.
ETL is used for local, relational, and structured data, while ELT is used for scalable cloud structured and unstructured data sources.
ETL is mainly used for a small amount of data, while ELT is used for large ones.
ETL does not provide data lake support, while ELT offers it.
ETL is easy to implement, while ELT requires niche skills to implement and support.
Modern Approach for Big Data
ELT is an alternative to the traditional extracting, transforming, and loading process. It sends the process transformation component to the target database to improve performance. This feature helps process massive datasets needed for business intelligence (BI) and big data analysis.
ELT reduces the data transfer time, thus increasing the work efficiency. It uses the processing capabilities built into the data storage infrastructure to do this.
Benefits of ELT
The rapid growth in the number of types and volumes of data that companies have to process can overburden traditional data warehouses. Using the ETL process to manage millions of records in new formats can take time and costs. ELT offers some advantages, including:
Simplification of management. ELT separates loading and transforming tasks, minimizing the interdependencies between these processes, reducing risks, and improving project management.
Forward-looking data sets. ELT implementations can be used directly in data warehouse systems, but ELT is often used in the data lake approach when data is collected from different sources. This, combined with the isolation of the transforming process, facilitates future changes in the warehouse structure.
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