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