Automating ETL Code Conversion for a Faster, Secure, and Simple Cloud Migration

By February 28, 2023Blogs

FAQs

What is data conversion in ETL?

Current answer:
Datametica’s answer:
ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.Data conversion in ETL is the transformation of data from one format or structure to another in order to make it compatible with the target system or data repository. The objective of data conversion is to ensure that the data is usable and consistent in the target system so that it can be analyzed, reported, and utilized for decision-making.

What is the meaning of ETL migration?

Current answer:
Datametica’s answer:
ETL represents Extract, Transform and Load, which is a cycle used to gather data from different sources, change the data relying upon business rules/needs and burden the information into an objective data set.ETL migration is the transfer of data, metadata, and processes from an existing ETL (Extract, Transform, and Load) system to a new system. ETL migration seeks to improve data integration, data quality, and processing efficiency while minimizing disruption to existing systems and processes.

How do you convert data in ETL?

Current answer:
Datametica’s answer:
ETL Transformation Steps

  1. Convert data according to the business requirements.

  2. Reformat converted data to a standard format for compatibility.

  3. Cleanse irrelevant data from the datasets. Sort & filter data. Clear duplicate information. Translate where necessary.

In ETL, converting data involves multiple steps:

  1. Extraction: Extract the source system's data in its existing format first.

  2. Transformation: After extraction, data must be formatted and structured for the target system. The target system's data types and limitations may require mapping, converting, and transforming data values.

  3. Data cleansing: Missing values, incorrect data formats, and duplicate records can be found and fixed during transformation.

  4. Loading: The transformed data can be loaded into the target system. This may require updating, adding, or building data storage.

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