22 Questions
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
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
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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