Review the technology options available for the data warehouse use case, compare them, and choose the right one for the modules.
- [Instructor] Let us consider existing technologies…that we can use for building the AuditTrail…data archive pipeline.…We can start off with the destination database…we want to use.…Let's consider three options here.…We will evaluate these options against the goals we have…to see which one suits our requirements the best.…First, there is MySQL.…MySQL is a popular free relational database.…
It is very cheap when compared to Oracle…and provides a stable, mature, and usable products,…but when it comes to scalability, it is limited.…When we need to scale a MySQL deployment across a cluster…to support very high data volumes,…it starts to get expensive to acquire and operate.…However, MySQL does support excellent querying capabilities…through its SQL Support.…
The second product we're going to consider is HDFS,…the Hadoop File System.…HDFS has become a rage in the big data vault…for its ability to store massive amounts of data…and scale across commodity hardware.…HDFS is open-source and hence, very cheap.…It also can scale horizontally across commodity hardware.…
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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