Apache Kafka - Real-time Stream Processing (Master Class) - Understanding States and State Stores

Why is it important to remember past events in stream processing?

• To handle each event independently

• To transform events into Hadoop records

• To compute values like loyalty points

• To provide context for processing current events

What is the main goal of the invoice processing example discussed?

• To filter invoices for prime customers

• To transform invoices into notification objects

• To notify customers of their total reward points

• To compute rewards for each invoice separately

In the proposed solution, what is the role of the lookup table?

• To store the current state of customer rewards

• To filter invoices for prime customers

• To compute rewards for each invoice

• To transform invoices into notification objects

What distinguishes a stateful processor from a stateless processor?

• Stateless processors are used for aggregation operations

• Stateful processors do not require a state store

• Stateless processors depend on the state for processing

• Stateful processors require a state store for processing

Which of the following is a feature required by a state store in real-time projects?

• Limited scalability

• Fault tolerance

• Remote access

• High latency

What is a potential drawback of using remote databases for state management?

• They are always fault-tolerant

• They offer faster performance

• They are not scalable

• They can suffer from network delays

What are the two types of state stores provided by Kafka Streams?

• Fault tolerant in-memory and recoverable local persistent state stores

• Distributed and centralized state stores

• Remote and local state stores

• Temporary and permanent state stores