Core Transformations⏱️ ~8 mins
10. Text Normalization
Enterprise Architecture Context
In production stream-processing architectures (Google Cloud Dataflow / Flink), pipeline stages must handle parallel transformations without data loss, managing schema mutations and aggregations across distributed worker workers.
Problem Statement
### Business Context
Log aggregation systems normalize application state logs to uppercase format before writing to audit warehouses.
### Problem Statement
Write a function `uppercase_strings(input_pcoll)` that converts all string records to their uppercase equivalent.
Key Learning Objectives
- Understand distributed Apache Beam execution DAG stages and pipeline lifecycle.
- Apply idiomatic functional Python transforms using the pipe operator
|. - Ensure data consistency and idempotency across distributed stream workers.
Sample Data Fixtures
Sample Example 1
Input Stream:
['hello', 'world']
Expected Output:
['HELLO', 'WORLD']
Sample Example 2
Input Stream:
['beam']
Expected Output:
['BEAM']
Topics:#Map#Strings
solution.pyPython 3.11 (Apache Beam)
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Input PCollection2 elements
| # | Element / Payload |
|---|---|
| 1 | "hello" |
| 2 | "world" |
Expected Output PCollection2 elements
| # | Output Element |
|---|---|
| 1 | "HELLO" |
| 2 | "WORLD" |
Core Transformations⏱️ ~8 mins
10. Text Normalization
Enterprise Architecture Context
In production stream-processing architectures (Google Cloud Dataflow / Flink), pipeline stages must handle parallel transformations without data loss, managing schema mutations and aggregations across distributed worker workers.
Problem Statement
### Business Context
Log aggregation systems normalize application state logs to uppercase format before writing to audit warehouses.
### Problem Statement
Write a function `uppercase_strings(input_pcoll)` that converts all string records to their uppercase equivalent.
Key Learning Objectives
- Understand distributed Apache Beam execution DAG stages and pipeline lifecycle.
- Apply idiomatic functional Python transforms using the pipe operator
|. - Ensure data consistency and idempotency across distributed stream workers.
Sample Data Fixtures
Sample Example 1
Input Stream:
['hello', 'world']
Expected Output:
['HELLO', 'WORLD']
Sample Example 2
Input Stream:
['beam']
Expected Output:
['BEAM']
Topics:#Map#Strings