Analytics Engineering
52 articles tagged with "Analytics Engineering"
Stateful Stream Aggregations: Core Concepts
Keys, windows, watermarks, triggers, and checkpoints for robust stateful stream aggregations in Spark and similar engines.
Version Control Basics for dbt Projects
Branch, test, review, merge, and deploy dbt changes with focused commits, CI validation, protected branches, and rollback plans.
White-Label Embedded Analytics: Guide 2026
How to build secure, branded embedded analytics with tenant isolation, scoped embeds, exports, and staged rollout.
Data Engineer Certs and Salary: 2026 Guide
Certifications can open screens, but salary follows role scope, production ownership, and measurable impact.
Databricks Warehouse HA: 7 Design Patterns
Seven Databricks warehouse HA patterns to meet RTO/RPO: scaling, AZ resilience, failover, retries, stateless compute, standbys, isolation.
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.
12 ClickHouse Architecture Interview Questions
Guide to ClickHouse architecture: columnar storage, MergeTree parts, partitions, shards, replicas, Distributed tables, and query flow.
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.
Presto vs Trino Interview Questions
Explains the PrestoDB vs Trino split, rename, shared architecture, deployment differences, and interview-focused workload guidance.
Snowflake vs. Databricks: Monitoring Features Compared
SQL-first platforms favor low-touch monitoring and credit controls, while Spark-heavy stacks demand deeper job and streaming observability.