ETL is the abbreviation for “extract, transform, load” and it enables organizations to collect and copy data from various sources into a single, centralized system. Different types of data are collected and refined by an ETL process. This data is then delivered to a data warehouse. Moreover, it enables the migration of data among multiple sources, destinations and analytical tools. An ETL process not only aids in executing strategies for data management but also provides business intelligence.

 An adequately designed ETL system is important for strategies that involve data integration and is usually used in data warehousing. An ETL process ensures the extraction of data from source systems along with enforcing standards for consistency as well as data quality. This process makes it possible for separate data sources to be used together and for various types of data to function together seamlessly and consecutively delivers data in a presentable form enabling application developers to build applications and end users to make decisions.

The process of data extraction extracts data from sources that are either homogenous or heterogeneous. Data is processed in the data transformation step with the aid of data cleaning. Additionally, the data is moulded into a structure or storage format that enables querying as well as analysis. Last but not the least is the step of data loading which involves the insertion of data into the corresponding database (a data warehouse, an operational data store, data mart etc.). ELT systems involve data integration from various applications which are developed and supported by different vendors.

To digress, the websites that we access also have data. This data is stored on the servers of web hosting companies and delivered from there via the Internet, upon user request, to ensure that websites are accessible. Web hosting services can be of various types and the most efficient web hosting services are usually referred to as the “Top Cloud Hosting”, the “Best Linux Shared Hosting”, the “Best Windows Shared Hosting” etc.

The ETL Cycle

A real-life ETL cycle will have certain steps with regard to the process’ execution, such as cycle initiation, building reference data, extracting from sources, validating, transforming, loading into staging tables, auditing reports, publishing and finally archiving.

The Functioning of ETL

There are three steps that are essential to the ETL process. These steps enable the integration of data from source to destination. These three steps of an ETL process are data extraction, data transformation and finally data loading. Let us touch upon these steps individually.

1:Data Extraction

Usually businesses rely on multiple data types as well as systems. Such complex data needs to be extracted from its source prior to being moved into a new system. The first step of the ETL process has to do with data extraction and entails not only importing but also consolidating structured and unstructured data into a single repository. Sources for data extraction include Cloud, hybrid and on-premises environments, data storage platforms, data warehouses, existing databases, legacy systems, CRM (Customer Relationship Management) systems, mobile devices and applications, marketing and sales applications and analytical tools. By automating the process involved in data extraction, ETL tools ensure an efficient and reliable workflow and save time as well as eliminate manual errors.

2. Data Transformation

The second step in an ETL process has to do with data transformation. This phase entails the application of rules and regulations for ensuring accessibility to data and its quality. Additionally, such rules can be applied that aid an enterprise in meeting reporting requirements. The phase of data transformation has many steps. These are mentioned below-

• Cleansing- In this step data-related inconsistencies and missing values are resolved.

• Standardization- This step applies formatting rules to a data set.

• Deduplication- In this step redundant data is discarded.

• Verification- This step ensures the removal of unusable data.

• Sorting- Data organization based on its type takes place in this step.

• Other tasks- The quality of data is enhanced by applying optional or additional rules.

The data transformation phase is usually considered as the most crucial phase of the entire ETL process as it ensures enhancement in data integrity and delivers data to its next destination in a form that is compatible and can be used readily.

3. Data Loading

As the last step in the ETL process, data loading loads the transformed data. This loading of data can take place at one go, which is known as full loading or it can happen at scheduled intervals, which is called incremental loading.

Conclusion:

Enterprises these days rely on efficient ETL tools to facilitate their data integration processes. Such tools provide not only speed, cost-effectiveness, reliability but are also aligned with a business’ data management strategies and deliver augmented productivity and efficiency.