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Databases vs. Hadoop vs. Cloud Storage

How can an organization thrive in the 2020s, a changing and confusing time with significant Data Management demands and platform options such as data warehouses, Hadoop, and the cloud? Trying to save money by bandaging and using the same old Data Architecture ends up pushing data uphill, making it harder to use. Rethinking data usage, storage, and computation is […]

Advances in Data Architecture

The continuous growth of data has led to large corporations investing heavily in technologies around large data volumes, allowing them to gain useful business intelligence that was unavailable to their smaller competitors. The evolution of public clouds has made big data technologies accessible to small businesses and startups. By using new advances in Data Architecture, […]

A Brief History of Data Lakes

Data Lakes are consolidated, centralized storage areas for raw, unstructured, semi-structured, and structured data, taken from multiple sources and lacking a predefined schema. Data Lakes have been created to save data that “may have value.” The value of data and the insights that can be gained from it are unknowns and can vary with the […]

The Future of Data Engineering

A Data Engineering Guide reveals that while people often rely on the work of data engineers — depending on Siri for quick solutions or being enchanted by custom recommendations or promos — they often do not realize that these advanced tools can provide accurate results only because of the hard work put in by data […]

Graph Databases vs. Key-Value Databases

Graph databases and key-value databases have very different features and are used for accomplishing different tasks. Key-value databases are streamlined and fast, but are limited and not as flexible. Graph databases, on the other hand, are very flexible and great for research, but not terribly fast. Both typically use a non-relational foundation. The two key […]

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