Almost all businesses use data in some form or other to gain information essential for running their operations. The data warehouse model is vital for the organization’s ability to access this data. At the same time, ELT is a crucial process that acts as an enabler of sorts, assisting with maintaining and analyzing the data. ELT means extraction, loading, and transformation. It is a data integration process where you first pull out raw information from different sources and load it into a main repository such as a cloud data warehouse or data lake where you turn it into the right layout for analysis.
This article provides an in-depth exploration of the many steps involved in an ELT process. Also, we highlight its benefits and provide examples of the available tools used to facilitate this operation.
ELT in the data warehouse model
ELT(Extract-Load-Transform) is a type of data warehouse model that allows businesses to extract and analyze large amounts of data from multiple sources. This process involves extracting the raw data from external sources, loading it into an organized database structure, and then transforming it into meaningful insights through robust analytics tools.
ELT uses various software programs such as:
- ETL (Extract Transform Load);
- SQL Server Integration Services (SSIS);
- Informatica Power Center;
- SSAS (SQL Server Analysis Services);
- and etc.
It allows automating tasks such as collecting data from different sources like databases, spreadsheets or even web APIs before moving them over to a single repository. Here they are cleansed and transformed according to specific business requirements.
Appropriate stakeholders within the organization can quickly analyze processes using the repository. Also, they will be able to spot trends in customer behavior or uncover hidden opportunities within the marketplace. Sure, it would not be possible with traditional methods. Ultimately it helps organizations better understand what truly drives success for their enterprise.
How ELT works
Using ELT in business operations, companies collect, organize and analyze large amounts of data, gaining invaluable insights about their customers and competitors. It helps businesses make better decisions for their future.
The ELT process is broken out as follows:
Extract: The first step involves extracting the raw data from sources such as SQL or NoSQL databases, cloud platforms, or XML files.
Load: Next step includes delivering the data directly into the target system. From there, specialized software programs cleanse and transform the information according to specific business needs.
Transform: Finally, powerful analytics tools convert the data structure following the target system.
By taking advantage of these technologies, companies can become more agile when making decisions about how they serve their customers best while simultaneously staying ahead of their competition, ensuring success now and well into the future.
Business benefits of ELT
ELT is quickly becoming a go-to tool for businesses looking to stay ahead of their competition. It offers numerous benefits over traditional methods, some of which we emphasize below:
- Simplifying management — ELT separates the loading and transformation tasks. It minimizes the interdependencies between these processes, lowers risk, and streamlines project management;
- Updated technologies — ELT software uses the advantages of new technologies to stimulate improvements, security, and compliance across the enterprise;
- Time to value — Using this technology, businesses can save time and money while still being able to make sense of large amounts of information within short periods, helping them improve operational efficiencies while staying ahead of their competition at the same time;
- Lowering costs — ELT is cost-efficient compared to other data warehouse processes such as ETL (Extract Transform Load);
- Flexibility — ELT process is adaptable for various areas, businesses, applications, and goals;
- Scalability — ELT solutions are used along with cloud data warehouses that, in turn, allow for an almost unlimited scale within seconds or minutes.
Uses Of ELT
The versatility and scalability of ELT make it an invaluable asset for businesses seeking a competitive advantage. Therefore ELT solutions are most effective for the following:
- Large business enterprises with extensive data volumes;
- Companies that work with a large amount of data from multiple sources and different formats;
- Businesses with a workflow that often requires quick access to integrated data;
- Data scientists who rely on business intelligence;
- IT departments, R&D teams, and data stewards interested in a low-maintenance solution.
Due to its concurrent load and data transformation functionality, the ELT process enhances data conversion and manipulation capabilities. This schema enables fast access to and querying of data.
ELT tools
Data warehouse automation and transformation systems are new ELT technologies that automate the repetitive, labor-intensive operations connected with integration and data warehousing.
Data warehouse automation removes error-prone manual coding and streamlines the full data warehousing lifecycle to impact analysis and optimize management. These automation solutions produce the commands, data warehouse structures, and required documentation.
Integrating with a real-time event capture and data integration solution provides real-time ELT by combining data integration with automatic creation; supports a diverse ecosystem of heterogeneous data sources such as relational, legacy, and NoSQL data stores.
Final thoughts
Data is essential in every business process. For it to be, it must be transferred and prepared for use. ELT is a crucial component of the data integration process since it takes a different approach to data transfer instead of traditional methods.
KITRUM provides a number of data integration services and solutions designed to support business-ready data and provide your company with the resources it requires to scale efficiently. Our team offers solutions that give businesses the assurance they need when managing big data projects, applications, and machine learning technology.