Data Infrastructure

Streaming Data Platforms: Kafka, Pulsar, and Beyond Streaming data platforms help teams publish and consume a steady flow of events. The two most popular open-source options are Apache Kafka and Apache Pulsar. Both store streams and support real-time processing, but they approach the problem with different design goals. Kafka focuses on a durable log with broad ecosystem support, while Pulsar separates storage and compute, offering strong multi-tenant capabilities and built-in geo-replication. ...

Data Centers and Cloud Infrastructure Explained Data centers are the quiet engines behind our online world. They house servers, storage, and fast networks that run apps, store files, and stream media. A single building can host thousands of devices, all powered and cooled to keep operations stable 24/7. When people talk about cloud services, they are often referring to many such facilities working together. Key components keep a data center working smoothly: ...

Smart Cities Technology: Infrastructure and Data Platforms Cities today rely on a mix of physical systems and digital data. A strong infrastructure, paired with a well managed data platform, helps services run smoothly, cuts waste, and improves safety. When streetlights, transit, water, and waste teams share clean data, city planners can act faster and plan for the future. Building blocks Physical layer: IoT sensors, cameras, meters, and networks that gather real-time data. Data layer: a modern data platform with a data lake or warehouse, streaming, and catalogs to organize information. Application layer: services and apps that turn data into actions, from traffic signals to public dashboards. Governance and security: privacy rules, access controls, and risk management to protect residents. Interoperability and standards help different systems talk to each other. Open data and common APIs invite innovation from startups and researchers while keeping analytics guided by clear policies. ...

Data Storage for Big Data: Lakes, Warehouses, and Lakeshouse Big data teams face a common question: how to store large amounts of data so it is easy to analyze. The choices are data lakes, data warehouses, and the newer lakehouse. Each pattern has strengths and limits, and many teams use a mix to stay flexible. Data lakes store data in its native form. They handle logs, images, tables, and files. They are often cheap and scalable. The idea of schema-on-read means you decide how to interpret the data when you access it, not when you store it. Best practices include a clear metadata catalog, strong access control, and thoughtful partitioning. Example: a streaming app writes JSON logs to object storage, and data engineers index them later for research. ...

Designing Robust Data Centers for a Cloud Era In the cloud era, data centers must be reliable, scalable, and cost-conscious. Robust design reduces outages, speeds deployment, and supports growing workloads. This guide shares practical ideas that teams can apply in real projects, from power to software. Power and reliability Power is the foundation. Build with N+1 paths for critical systems, dependable UPS, and on-site generation as a backup. Use automatic transfer switches to switch sources without interrupting service. Separate IT feeds from facilities feeds, and monitor voltage, currents, and temperatures in real time. Clear alarms help operators act before problems grow. ...

Data Warehouses and Data Marts for Analytics Data warehouses and data marts are two common ways to organize data for analytics. A data warehouse stores integrated data from many sources in a central, consistent schema. A data mart is a smaller, targeted slice of data designed for a specific group or line of business. Together they help teams ask questions, track trends, and make better decisions. Both help turn raw data into insights, but they differ in scope and goals. Key differences include: ...

Scalable Storage Solutions for Big Data As data volumes grow, teams need storage that scales with demand while staying affordable. Big data comes in many forms—log files, images, videos, and sensor streams. A good plan keeps data accessible for analytics, backups, and reporting without slowing down operations. Different data types call for different storage approaches. Active analytics files benefit from fast access, while older data can live in cheaper, long‑term storage. A thoughtful mix reduces cost and keeps the right data close to the tools that analyze it. ...

Data engineering pipelines: from raw data to insights Data teams turn raw data into insights by building pipelines that are reliable and scalable. A well designed pipeline makes data usable for analysts and leaders, turning messy logs and tables into clear answers. The goal is to move data from its source to a form that supports fast, accurate decision making. A typical data pipeline moves data through stages: ingest, quality checks, transformation, storage, and access. Each step adds value and helps prevent errors from showing up in reports. Clear boundaries and small, testable pieces make maintenance easier. ...