Data Engineering
98 articles tagged with "Data Engineering"
Provisioned Concurrency vs On-Demand: Latency Tradeoffs
Measure P95/P99 before paying for provisioned concurrency — choose on‑demand or provisioned to balance tail latency, cost, and spikes.
Data Engineer Certs and Salary: 2026 Guide
Certifications can open screens, but salary follows role scope, production ownership, and measurable impact.
How AWS IoT Core Processes IoT Event Streams
Explains how IoT Core ingests, filters, and routes device events, plus guidance on scaling, replay, and reliability.
Databricks Warehouse HA: 7 Design Patterns
Seven Databricks warehouse HA patterns to meet RTO/RPO: scaling, AZ resilience, failover, retries, stateless compute, standbys, isolation.
Airflow DAGs: Core Concepts and Task Design
Design Airflow DAGs for safe retries and idempotent publishing: task boundaries, sensors, schedules, timeouts, and testing.
Fraud Detection with Streaming Data: Use Cases
Use streaming events plus historical context to detect fraud across banking, e‑commerce, insurance, and telecom—making fast, accurate risk decisions.
Security and Compliance in Data Migration
Checklist for secure data migration: planning, encryption, access controls, logging, testing, cleanup, and post-move governance.
Securing Event-Driven Architecture: Best Practices
Checklist to secure event-driven systems: map flows, enforce least privilege, validate schemas, protect identities, control retries, and audit.
Handling Schema Drift in Event Streams
Prevent and contain schema drift in event streams: pin contracts, run compatibility and staging tests, use DLQs and staged rollouts.
12 ClickHouse Architecture Interview Questions
Guide to ClickHouse architecture: columnar storage, MergeTree parts, partitions, shards, replicas, Distributed tables, and query flow.
Champion-Challenger Guide for Lakehouse ML
Repeatable champion-challenger process for lakehouse ML: shared features, registry aliases, shadow/canary tests, logging, and rollback.
LIME for ML Models: 5-Step Guide
Use LIME to explain single-model predictions: build local samples, fit a weighted surrogate, and verify stability, fidelity, and scope.