Apache Spark MasterClass Chapter 1 – Episode 6

  1. Design a Spark-based e-commerce pipeline across bronze silver gold and describe quality, privacy, and publication steps. Show answer

    An e-commerce data pipeline using Spark can be designed using the medallion architecture.

  2. Governance: Throughout the pipeline, data governance is crucial. You'd document data owners, lineage, and SLAs in a data catalog to ensure data trustworthiness.How would you implement data quality (DQ) checks in Spark and fail fast when rules are violated? Show answer

    To implement data quality checks in Spark and fail fast, you can:

  3. Describe a data scientist s notebook workflow with Spark and how results flow back to the user. Show answer

    A data scientist’s workflow with Spark typically starts in a notebook environment like Jupyter or Databricks. The notebook code is sent to the Spark Driver. The Driver then orchestrates the execution on the cluster’s Executors. Data is loaded from a source, and the data scientist applies transformations using Spark’s APIs. When an action is called (e.g., show(), count(), collect()), Spark computes the result and sends it back to the Driver, which then renders it in the notebook. This allows the data scientist to iteratively transform, visualize, and refine their data interactively. For large results, Spark will typically return a sample or an aggregation to keep the notebook responsive.

  4. Batch vs streaming for the e-commerce scenario: when to choose each and how to wire Structured Streaming. Show answer
  5. How do you optimize file-based data lakes for Spark (partitioning, file size, pruning, pushdown)? Show answer

    To optimize file-based data lakes for Spark:

  6. Explain schema evolution in Spark (new columns, type changes) and safe rollout. Show answer

    Schema evolution in Spark involves safely changing a dataset’s schema without breaking downstream jobs.

  7. What s your strategy to protect PII in Spark pipelines? Show answer

    To protect PII (Personally Identifiable Information):

  8. Discuss common join strategies and when to broadcast in Spark. Show answer
  9. How would you design an ML pipeline in Spark MLlib for predicting customer churn? Show answer

    To design a customer churn prediction pipeline in Spark MLlib:

  10. Explain at-least-once delivery and how to achieve effectively-once outcomes in Structured Streaming. Show answer

    At-least-once delivery means that after a failure, a record may be reprocessed, potentially leading to duplicates in the sink. To achieve an effectively-once outcome, you need to combine at-least-once delivery with idempotent writes. This means designing your sink to handle duplicate records without creating duplicate results. You can use MERGE or UPSERT operations keyed by a unique identifier (like a record ID and a window timestamp) in your sink table. Spark’s checkpointing mechanism ensures that it resumes from the last successfully committed offset, preventing re-reading old data.

  11. What monitoring and observability would you set up for Spark jobs in production? Show answer

    For production Spark jobs, I would set up the following monitoring:

  12. How do you keep notebook-driven exploration reliable and reproducible? Show answer

    To keep notebook exploration reliable and reproducible, you should:

  13. What would you do to reduce costs in a cloud Spark environment without sacrificing SLAs? Show answer

    To reduce costs without sacrificing SLAs:

  14. How do you publish 'gold' data for both BI and downstream services? Show answer

    To publish gold data:

  15. Describe a small end-to-end PoC that showcases Spark for stakeholders. Show answer

    A good end-to-end PoC for Spark would involve:

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