Apache Flink: Stream Processing and Batch Processing

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What is a key characteristic of stream processing?

It involves processing data in real-time

What is Apache Flink used for?

For connecting, enriching, and processing data in real-time

What is an example of an unbounded stream?

Events from a web server, such as clicks and downloads

What is event streaming?

The practice of capturing events in real-time

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

What is a characteristic of batch processing?

It involves processing data in batches

Why is partitioning into independently processed pipelines crucial in Flink?

For scalability and parallel processing

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

It processes unbounded streams of data

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

They are partitioned upstream

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

Credit card transactions

What does the first operator in the job graph do?

Forwards input from the source downstream

What is a challenge of stream processing?

Data is unbounded with unpredictable intervals

What is the purpose of a Flink program?

To manipulate, process, and react to streaming events

Why is shuffling event streams more expensive than forwarding them?

Because it requires serializing each event

What is the purpose of rebalancing in Flink?

To redistribute event streams in a round-robin fashion

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

Detecting fraudulent credit card transactions

What is a characteristic of batch programs in Flink?

They are a special kind of streaming program

What is the drawback of rebalancing in Flink?

It requires serializing each event

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

Using Flink SQL

What is a challenge of processing unbounded streams of data?

Latency factor impacts accuracy of results

What type of processing does Flink support?

Both batch and stream processing

What is the first step to write a Flink program?

Bootstrap sources

Study Notes

  • 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.
  • 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
  • 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
  • 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

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.

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