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Questions and Answers
What is a key characteristic of stream processing?
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?
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?
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?
What is event streaming?
What is a key difference between bounded and unbounded streams?
What is a key difference between bounded and unbounded streams?
What is a characteristic of batch processing?
What is a characteristic of batch processing?
Why is partitioning into independently processed pipelines crucial in Flink?
Why is partitioning into independently processed pipelines crucial in Flink?
What is a key characteristic of stream processing with Apache Flink?
What is a key characteristic of stream processing with Apache Flink?
What happens to parallel input streams before being ingested by Flink?
What happens to parallel input streams before being ingested by Flink?
What is an example of a real-time business event that can be streamed?
What is an example of a real-time business event that can be streamed?
What does the first operator in the job graph do?
What does the first operator in the job graph do?
What is a challenge of stream processing?
What is a challenge of stream processing?
What is the purpose of a Flink program?
What is the purpose of a Flink program?
Why is shuffling event streams more expensive than forwarding them?
Why is shuffling event streams more expensive than forwarding them?
What is the purpose of rebalancing in Flink?
What is the purpose of rebalancing in Flink?
What is an example of a use case for Apache Flink?
What is an example of a use case for Apache Flink?
What is a characteristic of batch programs in Flink?
What is a characteristic of batch programs in Flink?
What is the drawback of rebalancing in Flink?
What is the drawback of rebalancing in Flink?
What is the alternative to implementing the example using Flink's APIs?
What is the alternative to implementing the example using Flink's APIs?
What is a challenge of processing unbounded streams of data?
What is a challenge of processing unbounded streams of data?
What type of processing does Flink support?
What type of processing does Flink support?
What is the first step to write a Flink program?
What is the first step to write a Flink program?
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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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