ETL (Extract-Transform-Load)

ETL comes from Data Warehousing and stands for Extract-Transform-Load. ETL covers a process of how the data are loaded from the source system to the data warehouse. An essential component of every successful analytics project requires the movement of data from one or more sources to one or more target systems. Regardless of the target destination, it is critical to have a team with a broad scope of data integration experiences.

Each step of the ETL process will be tailored to your individual needs and desired outcome to ensure all extracted data is processed and transformed for successful loading into the data warehouse. The sequence is Extract-Clean-Transform-Load. We design the ETL process keeping fail-recovery in mind.

  •   Data Cleaning and Validation

    Data is often found in a messy and disorganized fashion. Data points may be duplicated or improperly entered. Without proper attention to data cleanup, validating data entries, testing, databases can become unstable and ineffective and we take care that does not happen.

  • Extract

    Data extraction from the source system and makes it accessible for further processing. Retrieving all the required data from the source system with as little resources as possible. We design extraction in a way that it does not negatively affect the source system in terms or performance, response time or any kind of locking.

  • Clean

    Ensuring the quality of the data in the data warehouse. We perform cleaning with data unification rules, such as: Making identifiers unique, Convert null values into standardized Not Available/Not Provided value, Convert phone numbers/ ZIP codes to a standardized form, Validate address fields, convert them into proper naming.

  •   Transform

    The transform step applies a set of rules to transform the data from the source to the target. This includes converting any measured data to the same dimension using the same units so that they can later be joined. The transformation is done by joining data from several sources, generating aggregates, generating surrogate keys, sorting, deriving new calculated values, and applying advanced validation rules.

  •  Load

    The target of the Load process is often a database. In order to make the load process efficient, we disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity is maintained by ETL tool to ensure consistency.

    Our data migration, integration, and Extract-Transform-Load (ETL) experts have extensive experience planning, developing, implementing, and tuning comprehensive and effective data warehousing and data migration solutions for Microsoft, Oracle, and MySQL technologies. Our flexible and scalable approach toward ETL, data migration, and data integration can make your data accessible, efficient, and capable of being analyzed across multiple departments within your organization.