ETL
35 bites tagged ETL — interview questions with model answers, and 60-second explainers.
Schema-on-Read vs. Write: Pay for Structure Now or Later?
Schema-on-Write pays to structure data upfront for fast, consistent reads. Schema-on-Read defers this cost to query time for flexible ingestion. This choice underpins relational databases (write) vs. data lakes (read). The footgun is creating a data swamp.
Data Pipelines: From Raw Data to Actionable Insights
A data pipeline is the plumbing for your data, moving it from raw sources to a refined state for analysis. It feeds dashboards and ML models by cleaning data from APIs and databases. The key footgun is choosing batch processing for real-time needs.
Spark DataFrame API: SQL Smarts on Distributed Data
The DataFrame API is like giving Spark a schema for your distributed data, letting its Catalyst optimizer plan queries like a database would. Use it for structured data processing with column-based operations.
Data Pipeline Orchestration: Beyond Cron Jobs
Data pipeline orchestration is the conductor for your data workflows, ensuring tasks run in the right order with full dependency awareness. It manages complex chains, like triggering analytics only after an ETL job succeeds.
Regular Expressions for Data Cleaning
Regex is a mini-language for describing text patterns, letting you find and fix messy data at scale. It's used to standardize phone numbers or extract zip codes from addresses. The footgun: complex regex is often unreadable and a maintenance nightmare.
Handling Duplicate Data
Finding duplicate records is a key part of data cleansing. It's not just about deleting rows with the same ID; duplicates can be subtle and require careful handling to avoid corrupting your dataset. The footgun is assuming all duplicates are safe to delete.
Apache Beam: Write-Once, Run-Anywhere Data Pipelines
Apache Beam is a universal remote for big data engines. You write your pipeline logic once using its SDK, and it translates your code to run on different "runners" like Spark or Flink. The footgun is thinking Beam is an engine; it's an abstraction that.
Data Cleansing: Fixing Your Data Before It Fails You
Data cleansing is quality control for your dataset, finding and fixing errors before they skew your analysis. It's a crucial first step in any data pipeline, from training an ML model to generating business reports. The footgun is assuming data is clean.
Reverse ETL: From Warehouse Insights to Operational Action
Reverse ETL pushes clean data from your central warehouse back into the operational tools business teams use daily. This powers sales with customer scores in their CRM or marketing with personalized segments, all from a single source of truth.
Data Vault Modeling: An Audit-First Data Warehouse
Data Vault modeling builds a warehouse like a financial ledger, where every entry is permanent and traceable. It excels at storing historical data from multiple systems for auditing.
Data Warehouse: The Single Source of Truth for Analytics
A data warehouse is a central database optimized for analytics, not transactions. It integrates historical data from disparate sources like sales and marketing to create a single source of truth for business intelligence.
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