Incremental data pipelines are a critical component of any modern data architecture. They allow for efficient and reliable loading of transactional data changes into a data warehouse or other data store. In this article, we will discuss how to use Amazon Web Services (AWS) Data Migration Service (DMS), Delta 2.0, and Amazon Elastic MapReduce (EMR) Serverless to construct an incremental data pipeline for loading transactional data changes.
AWS DMS is a managed service that allows users to easily migrate data from one database to another. It supports both full and incremental data loads, making it an ideal choice for constructing an incremental data pipeline. With AWS DMS, users can set up a replication task that will continuously replicate changes from the source database to the target database. This allows for near real-time loading of transactional data changes into the target database.
Delta 2.0 is an open-source framework for building data pipelines. It is designed to be used with AWS DMS and provides a powerful set of features for constructing incremental data pipelines. Delta 2.0 allows users to define a set of rules that will be used to detect changes in the source database and replicate them to the target database. It also provides a number of features for managing the replication process, such as scheduling, retry logic, and error handling.
Finally, Amazon EMR Serverless is a managed service that allows users to quickly spin up and down compute clusters for processing data. It is designed to be used with Delta 2.0 and provides a cost-effective way to run the replication tasks defined in the Delta 2.0 pipeline. With EMR Serverless, users can easily scale up or down the compute resources needed to run their replication tasks, allowing them to optimize their costs while still ensuring that their data is replicated in a timely manner.
In conclusion, AWS DMS, Delta 2.0, and Amazon EMR Serverless provide an effective way to construct an incremental data pipeline for loading transactional data changes. By leveraging these services, users can quickly and easily set up a reliable and cost-effective solution for replicating their transactional data changes into a target database.
Source: Plato Data Intelligence: PlatoAiStream