Review three top analytics architectural patterns and their associated AWS services. These are streaming, data lake, and batch processing.
- [Instructor] Now let's review…some reference architectures.…The first one is interactive and batch analytics.…So you can see here, we have services that process…and services that store our information.…And we have two flows.…We have a stream, and we have files.…The interactive stream is called the hot path,…and the file section is called the cold path.…So you can see that we're working with Kinesis here.…So we have Kinesis working with S3…to stream the data from our bucket using the Firehose…and then push that data into a number of processes…so that the data can ultimately be ready…for the most sophisticated process of all,…which is machine learning or artificial intelligence.…
So up on the top you can see…that we're working with Redshift, Athena,…EMR with some Presto and Spark processes,…and processing that data so it can be part…of the predictive model.…On the bottom in the cold path,…we're working with EMR again with Spark, Hive, and Pig,…some of the more traditional batch jobs often used…to clean massive volumes of data.…
- Explain the difference between files and databases.
- Identify examples of batching, micro-batching, and streaming.
- Prepare helpful data visualizations with QuickSight.
- Recognize the different types of analytics available in AWS.
- Demonstrate how to set up AWS CLI.
- Describe common analytics architecture patterns.
Skill Level Intermediate
Amazon Web Services for Data Sciencewith Lynn Langit3h 56m Intermediate
1. Analytics on AWS
2. Analytic Services
3. AWS Code Tools for Analytics
4. Advanced Analytics
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