Finesse the earlier architecture blueprint for the data warehouse use case with technology options and finer details, to arrive at the final architecture.
- [Instructor] Now that we have…figured out the technologies,…let us enhance the architecture outline.…We will use HDFS plus Hive…as the database for the audit trail.…We need to also create suitable partitions…and indexes in Hive for better querying.…For the data pump, we will use Apache Sqoop.…Apache Sqoop will be configured to run queries…on the source audit trail table at periodic intervals,…extract records from the table, and push them to HDFS.…
There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for data acquisition, transport, processing, storage, and service. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand.
- Components of a big data application
- Big data app development strategies
- Use cases: archiving audit logs and performing customer analytics
- Technology options
- Designing solutions
- Best practices
Skill Level Advanced
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
1. Intro to Big Data Applications
2. Use Case 1: Data Warehouse (DW)
3. Use Case 2: Log Accumulation (LA)
4. Use Case 3: IT Operations Analytics (OA)
5. Use Case 4: Customer 360 (C360)
6. Use Case 5: Customer Analytics (CA)
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