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11 resultsBuilding a Data Quality Framework
Dimensions of data quality, validation layers, and monitoring in production pipelines.
Apache Spark — Core Concepts and When to Use It
RDDs, DataFrames, Spark SQL, and the use cases where Spark is the right tool.
Semantic Versioning — MAJOR.MINOR.PATCH in Practice
When to bump each version number and how to communicate breaking changes.
Data Observability — Detecting Silent Pipeline Failures
Freshness, volume, distribution, schema, and lineage monitoring for data reliability.
How long does a typical project take?
Timeline expectations from kick-off to launch.
LLM Guardrails: keeping AI outputs safe in production
Techniques for input/output filtering, content policies, and hallucination mitigation.
Monitoring and Alerting for Data Pipelines
What to monitor, SLIs/SLOs for data, and building effective alerting.
GraphQL vs REST — When to Use Each
Comparing query flexibility, over-fetching, tooling, and operational complexity.
API Gateway — Responsibilities and Implementation Patterns
Authentication, rate limiting, routing, request aggregation, and when not to use a gateway.
Testing Strategy for Data Pipelines
Unit tests, integration tests, data contract tests, and regression testing for pipelines.
API Testing Strategy — Unit, Integration, Contract, and E2E
Building a test pyramid that catches real bugs without slowing delivery.