Practical Data Analysis with pandas for Developers
Learn how to use pandas DataFrames for cleaning, selecting, combining, aggregating, plotting, importing, and exporting tabular data with practical Python examples.
Learn how to use pandas DataFrames for cleaning, selecting, combining, aggregating, plotting, importing, and exporting tabular data with practical Python examples.
Learn how tracing, spans, Jaeger, OpenTracing, APM, and service monitoring help Java teams connect user requests to sub-calls, metrics, logs, and business KPIs.
Learn how MicroProfile-style metrics and health checks give Java services real-time visibility for performance, alerts, readiness, liveness, and Kubernetes self-healing.
Learn how Java logging levels, appenders, rotation, log formats, security rules, and centralized aggregation make production troubleshooting practical in distributed systems.
Learn how to combine unit, integration, end-to-end, performance, and UAT testing with JUnit, mocks, coverage metrics, and disposable test environments.
Learn how to design security into Java applications with input validation, least privilege, sandboxing, firewalls, IPS tools, static analysis, dependency checks, and security standards.
Learn how authentication, authorization, IAM, SSO, user stores, tokens, RBAC, and PBAC fit together when designing secure Java applications.
Learn how to design gRPC services in Go with Protobuf contracts, generated code, streaming RPCs, interceptors, graceful shutdown, secure clients, and production-ready observability.
Learn how to use events to decouple Go services, define stable message contracts, build reusable producers and consumers, handle failed messages, tune broker clients, and observe the system in production.
Learn how to test Go persistence code against real containerized services, separate integration tests from fast unit tests, reuse containers safely, and keep CI feedback reliable.