A typical data integration process uses batch processing to pull data out of the source, makes changes according to requirements, and then loads the transformed data into a database or BI platform to provide better business insights.With data available to query, business leaders can make data-driven business decisions. Below is list of the data integration tools, open source and paid, generally used in the data management industry:
1. Informatica PowerCenter
2. Microsoft SQL Server SSIS
4. Azure Data Factory
5. Apache Camel
6. Apache Kafka
The data integration process and toolkit mentioned above are a part of data management paradigm known as ETL (Extract Transform Load), which has been a bedrock process of data analytics and data warehousing since the beginning, but the increased pace of data usage and the nosediving price of storage mean that it’s often necessary these days to get data in front of analysts as quickly as possible. Because the Transform step in an ETL pipeline can often be a chokepoint in the data pipeline, that means that some more modern data warehousing companies are switching to an ELT-based approach, where the transformation step is pushed to the end of the process, or even delayed until the point of query by analysts. Modern architectures demand a shift from ETL to No ETL paradigm.
‘No ETL,’ means that the ETL process is supplanted by Extract, Load, Transform (ELT), where data transformation happens in SQL as needed for downstream use, rather than upfront during the loading stage.
Athena is a service provided by Amazon Web services cloud computing platform. This service is the pioneer in the ELT paradigm. “With Athena, you extract the data from the sources, and then load it with no or minimal preprocessing. This style of ELT is a superior model for most use cases, because it results in a simpler architecture and gives analysts more visibility into how the raw data becomes transformed.”