Rapid Adoption of AWS Using Streaming Data Integration with CDC
In this video, Striim Founder and CTO, Steve Wilkes, talks about moving data to Amazon Web Services in real-time and explains why streaming data integration to AWS – with change data capture (CDC) and stream processing – is a necessary part of the solution.
To learn how Striim can help you continuously move real-time data into AWS, visit our Striim for AWS page.
Adopting Amazon web services is important to your business and why? Real-time data movement through streaming integration, change, data capture and stream processing necessary parts of this process you’ve already decided that you want to adopt Amazon web services is going to be Amazon rds or ever Amazon redshift, Amazon s three Amazon, Canisius, Amazon EMR, any number of other technologies you may want to migrate existing applications to AWS scale elastically as necessary or use the cloud for analytics or machine learning or any applications in AWS as VMs or containers. So only parts of the problem. You also need to consider how to move data to the cloud and to your applications. Analytics are always up to date. Make sure the data is in the right format to be valuable. Most important starting point is ensuring you can stream data to the cloud in real time. Batch data movement can cause unpredictable load enclave targets and that’s a high latency meaning it as often now as old from an applications having up to a second.
Information is essential. For example, to provide current customer information, accurate business reporting, offer real time decision maker streaming data from on premise to Amazon web services required making use of appropriate data collection technologies for databases. This has changed their to capture or CDC. We start rectally and continuously intercepts database activity and collects all the inserts, updates and deletes as events as they happen. Love data requires file Taylor which reads at the end of one or more file across potentially multiple machines and streams the latest records as they are written. Other sources like IoT data or third party SAS applications also requires specific treatments in order to ensure data can be streamed in real time which you have streaming data. The next consideration is what processing is necessary to make that data valuable. Your specific AWS destination, and this depends on the use case for database migration or lesson scalability use cases, but the target Schema is similar to the source.
Moving raw data from on premise databases to Amazon RDS or Aurora. Maybe sufficient important consideration here is that the source applications typically cannot be stopped and it takes time to do an initial load based way. Collecting and delivering database change during and after. The initial load is essential for zero downtime migrations. The real time application sourcing from Amazon, nieces or analytics use cases built on Amazon redshift or Amazon EMR, maybe necessary to perform stream processing before the data is delivered to the cloud. There’s processing can transform the data structure and in Richard with additional context information while the data is in flight, adding value to the data and optimizing downstream analytics stream streaming integration platform. We continuously collect data from on premise or other cloud sources and delivered to all of your Amazon web service endpoints to can take care of initial loads as well as CDC for the continuous application of change. And these data flows can be created rapidly and monitored and validating continuously through our intuitive UI, the stream, your cloud migration, scaling, and analytics. We built an iterated on at the speed of your business, ensuring your data. There’s always where you wanted when you want.