June 2018
Intermediate to advanced
58 pages
1h 5m
English
Content preview from Streaming Change Data Capture
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Chapter 4. Case Studies
Now let’s explore some case studies. Each of these illustrates the role of change data capture (CDC) in enabling scalable and efficient analytics architectures that do not affect production application performance. By moving and processing incremental data and metadata updates in real time, these organizations have reduced or eliminated the need for resource-draining and disruptive batch (aka full) loads. They are siphoning data to multiple platforms for specialized analysis on each, consuming CPU and other resources in a balanced and sustainable way.
Case Study 1: Streaming to a Cloud-Based Lambda Architecture
Qlik is working with a Fortune 500 healthcare solution provider to hospitals, pharmacies, clinical laboratories, and doctors that is investing in cloud analytics to identify opportunities for improving quality of care. The analytics team for this company, which we’ll call “GetWell,” is using CDC software to accelerate and streamline clinical data consolidation from on-premises sources such as SQL Server and Oracle to a Kafka message queue that in turn feeds a Lambda architecture on Amazon Web Services (AWS) Simple Storage Service (S3). This architecture is illustrated in Figure 4-1. Log-based CDC has enabled them to integrate this clinical data at scale from many sources with minimal administrative burden and no impact on production operations.
GetWell data scientists conduct therapy research on this Lambda architecture, using both historical batch ...
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