Data Engineering
104 articles tagged with "Data Engineering"
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.
How to Build AI Streams with Kafka and Flink
Low-latency AI streams: use Kafka to ingest, Flink to build features and score, with replay, lateness handling, and exactly-once delivery.
7 Signs Python Pipelines Need Performance Profiling
Seven practical signals that reveal when your Python data pipeline needs profiling to find code, orchestration, or infrastructure bottlenecks.
Serverless Allocation Models For ETL And AI Workloads
Match serverless settings to each workload's tightest limit: latency for inference, throughput for ETL, dbt warehouse, joins for features.
Lakehouse Governance for PII: Best Practices
Layered lakehouse PII controls: column tags, catalog-driven masking/ABAC, lineage, audit logs, and automated deletion.
Apache Spark Certification Exam Topics List
Checklist of Spark exam essentials: DataFrames/SQL, joins, partitions, performance, streaming, and testing for hands-on prep.
What Makes Great Analytics Pull Requests
Keep analytics PRs small: state the change and impact, list affected metrics/models, and attach tests/screenshots for fast, accurate reviews.