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11 resultsBuilding a Data Quality Framework
Dimensions of data quality, validation layers, and monitoring in production pipelines.
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.
Data Observability — Detecting Silent Pipeline Failures
Freshness, volume, distribution, schema, and lineage monitoring for data reliability.
GraphQL vs REST — When to Use Each
Comparing query flexibility, over-fetching, tooling, and operational complexity.
Monitoring and Alerting for Data Pipelines
What to monitor, SLIs/SLOs for data, and building effective alerting.
Semantic Versioning — MAJOR.MINOR.PATCH in Practice
When to bump each version number and how to communicate breaking changes.
Apache Spark — Core Concepts and When to Use It
RDDs, DataFrames, Spark SQL, and the use cases where Spark is the right tool.
Testing Strategy for Data Pipelines
Unit tests, integration tests, data contract tests, and regression testing for 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.