```python from pyspark import SparkContext
sc = SparkContext(appName="WordCount") lines = sc.textFile("hdfs:///data/myfile.txt") spark 2 workbook answers
## 7. Putting It All Together – A Mini‑Project Blueprint | | **State assumptions** | “Assume the input
| Tip | How to Apply | |-----|--------------| | **Show Spark’s lazy evaluation** | Mention that transformations build a DAG, actions trigger execution. | | **Explain the physical plan** | Use `df.explain()` in a note to demonstrate understanding of shuffle, broadcast, etc. | | **State assumptions** | “Assume the input file fits in HDFS and each line is a UTF‑8 string.” | | **Edge‑case handling** | Talk about empty files, null values, or malformed CSV rows. | | **Performance hints** | Suggest `repartition` before a heavy shuffle or using `broadcast` for small lookup tables. | | **Testing** | Show a tiny local test (e.g., `sc.parallelize(["a b","b c"]).flatMap(...).collect()`). | | **Clean code** | Use meaningful variable names, consistent indentation, and short comments. | | | **Clean code** | Use meaningful variable
If the workbook includes a **mini‑project** (e.g., “process a log dataset and produce a daily report”), you can outline the full pipeline: