Data-Platform

Data Warehousing vs Lakehouse: Modern Data Architecture In modern data work, teams balance speed, scale, and governance. A traditional approach uses a data warehouse for clean, structured data that supports fast dashboards. A data lake stores raw, diverse data from many sources, including logs and sensor streams. The idea of a lakehouse adds a unified platform that tries to mix both worlds: strong SQL, flexible data types, and built‑in governance in one place. This blend helps teams move from isolated silos to a shared data truth without burning time on repetitive modeling. ...

Streaming Data Lakes: Real-Time Insights at Scale Streaming data lakes blend continuous data streams with a scalable storage layer. They unlock near real-time analytics, quicker anomaly detection, and faster decision making across product, marketing, and operations. A well designed pipeline ingests events, processes them as they arrive, and stores results in a lake that analysts and machines can query anytime. A practical stack has four layers. Ingest collects events from apps, devices, and databases. Processing transforms and joins streams with windowing rules. Storage keeps raw, clean, and curated data in columnar formats. Serving makes data available to dashboards, notebooks, and small apps through a lakehouse or data warehouse. Governance and metadata help teams stay coordinated and trustworthy. ...