Codex of Professional Wisdom in Data Engineering: A Compilation of Insights and Learnings

Welcome to my "Data Engineering Wisdom Codex," a curated collection of invaluable insights and learnings cultivated through years of dedicated practice.

Each entry encapsulates a nugget of knowledge gained from tackling intricate data transformation, architecting resilient pipelines, and optimizing performance.

This repository stands as a testament to growth, offering inspiration and guidance to fellow data enthusiasts. Join me in the pursuit of mastering the craft of data engineering, one concise revelation at a time.

Pandas is a reasonable tool to transform your data with for small dataset sizes

Build frameworks, not pipelines

Ensuring data accuracy and consistency early in the pipeline prevents downstream issues in analysis and decision-making.

Memory accesses are always faster than disk accesses. Spark efficiently reuses data by caching it in memory across the cluster. This prevents costly round-trips to disk access, explaining much of Spark's superiority over MapReduce.

Managing versions of your data is as important as versioning your code, ensuring reproducibility and auditability.

Design data pipelines with scalability in mind to accommodate future data growth without major infrastructure changes.

Treat data artifacts (such as transformations) like code, using version control systems to track changes and ensure reproducibility.

Maintain a comprehensive data catalog to keep track of data sources, transformations, and lineage, aiding collaboration and understanding.

Documenting the origin and transformation of data is essential for troubleshooting and maintaining data quality.

Implement robust security measures to safeguard sensitive data throughout the pipeline, adhering to industry standards.

Efficiently store and transmit data using compression techniques and serialization formats like Parquet or Avro.

Partitioning large datasets optimizes query performance by reducing the amount of data scanned.

Profiling data reveals patterns, anomalies, and inconsistencies, aiding in data quality improvement.

Set up monitoring systems to receive alerts for ETL job failures or data quality issues.

When interviewing for a Senior Data Engineer, you should run away if you see Titanic or Iris datasets in their resume. These two datasets are heavily used by novice learners rather than experienced engineers. By all means, you can consider for an entry-level engineer but not for a senior position. Don't tell me that I don't warn you 😉