Apache Flink: Stream Processing and Batch Processing

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Questions and Answers

What is a key characteristic of stream processing?

It involves processing data in batches

It involves sorting data to produce a final report

It involves processing data in real-time (correct)

It involves storing data for later retrieval

What is Apache Flink used for?

Only for batch processing

For connecting, enriching, and processing data in real-time (correct)

Only for stream processing

For storing data for later retrieval

What is an example of an unbounded stream?

A final report that summarizes all input data

Events from a web server, such as clicks and downloads (correct)

A batch of historic data that is reprocessed

A dataset that is sorted and processed

What is event streaming?

The practice of capturing events in real-time Signup and view all the answers

What is a key difference between bounded and unbounded streams?

Bounded streams have a fixed start and end time, while unbounded streams extend indefinitely into the future Signup and view all the answers

What is a characteristic of batch processing?

It involves processing data in batches Signup and view all the answers

Why is partitioning into independently processed pipelines crucial in Flink?

For scalability and parallel processing Signup and view all the answers

What is a key characteristic of stream processing with Apache Flink?

It processes unbounded streams of data Signup and view all the answers

What happens to parallel input streams before being ingested by Flink?

They are partitioned upstream Signup and view all the answers

What is an example of a real-time business event that can be streamed?

Credit card transactions Signup and view all the answers

What does the first operator in the job graph do?

Forwards input from the source downstream Signup and view all the answers

What is a challenge of stream processing?

Data is unbounded with unpredictable intervals Signup and view all the answers

What is the purpose of a Flink program?

To manipulate, process, and react to streaming events Signup and view all the answers

Why is shuffling event streams more expensive than forwarding them?

Because it requires serializing each event Signup and view all the answers

What is the purpose of rebalancing in Flink?

To redistribute event streams in a round-robin fashion Signup and view all the answers

What is an example of a use case for Apache Flink?

Detecting fraudulent credit card transactions Signup and view all the answers

What is a characteristic of batch programs in Flink?

They are a special kind of streaming program Signup and view all the answers

What is the drawback of rebalancing in Flink?

It requires serializing each event Signup and view all the answers

What is the alternative to implementing the example using Flink's APIs?

Using Flink SQL Signup and view all the answers

What is a challenge of processing unbounded streams of data?

Latency factor impacts accuracy of results Signup and view all the answers

What type of processing does Flink support?

Both batch and stream processing Signup and view all the answers

What is the first step to write a Flink program?

Bootstrap sources Signup and view all the answers

Study Notes

Advanced Analytics - Technology and Tools (Flink)

Apache Flink is a powerful framework for connecting, enriching, and processing data in real-time.
Stream processing involves unbounded data streams, where the input may never end and data is continuously processed as it arrives.
Bounded streams, on the other hand, have a fixed end and can be processed in batches.

Streaming

Unbounded streams extend indefinitely into the future and can be manipulated, processed, and reacted to in real-time.
Examples of unbounded streams include events from web servers, trades from a stock exchange, or sensor readings from a machine.
Bounded streams can be stored for later retrieval and reprocessing, making them a special case of streaming.

Stream Processing with Apache Flink

Flink can be used to manipulate, process, and react to streaming events as they occur in real-time.
Examples of use cases include:
- Fraud detection: alerting users to fraudulent credit card activity
- Estimated delivery time: providing accurate estimates of delivery times and alerting users to disruptions

Stream Processing Challenges

Data is unbounded, meaning no start and end
Unpredictable and inconsistent intervals of new data
Data can be out of order with different timestamps
Latency factor impacts accuracy of results

Flink Flow

To write a Flink program, follow these steps:
- Bootstrap sources
- Apply operations
- Partitioning into independently processed pipelines is crucial for scalability
Flink's APIs are used to specify what to do in each operator and where to send results

Stream Processing with Flink

At each stage of the job graph, application code specifies what to do in each operator and where to send results
Flink handles forwarding event streams efficiently
Shuffling event streams is more expensive than forwarding and may be necessary in some cases
Rebalancing event streams can be expensive and requires serializing each event and using the network

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Description

Learn about Apache Flink, a powerful framework for real-time data processing and batch processing. Understand the differences between stream processing and batch processing paradigm.