The Intricacies of AWS CDC to Amazon Simple Storage Service

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  Let’s see the many intricacies of the Amazon Web Service Change Data Capture (AWS CDC) feature while building data lakes on the Amazon Simple Storage Service (S3). When AWS CDC to S3   is carried out from a relational database that is located upstream to a data lake on S3, it is necessary to handle the data at a record level. The processing engine has to read all files, make the required changes, and complete datasets. Change data capture rewrites the files as new activities such as all inserts, updates, and deletes, in specific records from a dataset. On the other hand, poor query performance is often the result of AWS CDC to S3 . It is because when data is made available by AWS CDC to S3   in real-time, it becomes split over many small files. This problem is resolved with Apache Hudi, an advanced open-source management framework. It helps in managing data at the record level in Amazon S3, leading to the simplified creation of CDC pipelines with AWS CDC to S3 . Data ingestion is
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Database Migration with AWS ETL

One of the most critical services from Amazon Web Service (AWS) is database migration, either between one cloud provider to another or from an on-premises environment to the cloud. Database migration is between data warehouses, NoSQL databases, or relational databases with AWS ETL being the most optimized method to do so.
























ETL stands for Extract, Transform, Load and is a tool that helps to combine multiple databases into a centralized database or a single data warehouse. The complete flowchart of the AWS ETL goes like this – extracting data from a source, transforming it into a specific structure, and finally loading the processed data into the target data repository.

The main advantage of AWS ETL is that it automates the migration process and can be done without any human intervention. Hence, the possibility of any errors or data loss during migration is eliminated, leading to high-performing and cost-effective databases.

Further, when using the AWS ETL tool, it is not necessary to install any additional drivers or applications or make any changes to the source database. The migration process is carried out directly from the AWS Management Console and all changes to data in the source database are replicated seamlessly to the target database via the Change Data Capture option. 

During the use of AWS ETL, all changes are updated at regular pre-determined intervals provided the source and the target databases are in sync. Additionally, the source database remains fully functional during migration without the need for downtime. This factor is very beneficial for large organizations as shutting down systems for any period will upset operational schedules.

Given these aspects, AWS ETL is the preferred option for database migration today. 


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