Databricks-ML-professional-S03b-Streaming
This Notebook adds information related to the following requirements:
Streaming:
- Describe Structured Streaming as a common processing tool for ETL pipelines
- Identify structured streaming as a continuous inference solution on incoming data
- Describe why complex business logic must be handled in streaming deployments
- Identify that data can arrive out-of-order with structured streaming
- Identify continuous predictions in time-based prediction store as a scenario for streaming deployments
- Convert a batch deployment pipeline inference to a streaming deployment pipeline
- Convert a batch deployment pipeline writing to a streaming deployment pipeline
Download this notebook at format ipynb here.
2. Identify structured streaming as a continuous inference solution on incoming data
Same as batch but more frequent predictions on smaller datasets.
3. Describe why complex business logic must be handled in streaming deployments
N/A
4. Identify that data can arrive out-of-order with structured streaming
N/A
5. Identify continuous predictions in time-based prediction store as a scenario for
streaming deployments
N/A
6. Convert a batch deployment pipeline inference to a streaming deployment pipeline
N/A
7. Convert a batch deployment pipeline writing to a streaming deployment pipeline
N/A