Learn about some key design issues and recommendations for the individual components within the data warehouse use case.
- [Instructor] Now that we have the architecture chosen,…let us dive deeper into some important areas…that need attention.…First, we look at denormalization.…RDBM's databases are normalized in the specific…audit trail use case.…The audit trail will have IDs for elements like users,…products, et cetera.…They will need to be joined with the corresponding…dimension tables to get the names…and other relevant information.…
But doing joins in HDFS is difficult and time-consuming.…So it is best practice to denormalize data…as much as possible in the source…before pushing it into HDFS.…So how do you denormalize with Apache Sqoop?…We can create views for the audit trail…which will join the required dimension tables…with the main audit trail tables.…Then, we configure Apache Sqoop…to pull data from these views.…
This way, the data is pre-joined when it reaches HDFS.…Next, we look at parallelism.…Sqoop allows you to configure multiple MapReduce jobs…to perform data movement in parallel.…This is something you want to experiment with.…
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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