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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 […]

Optimizing the Data Warehouse

The data warehouse, a relational database technology, makes all enterprise information actionable, and will continue to be prominent as a Data Architecture component. In the 2000s, a typical business would consolidate data from multiple relational databases, centralizing all this information through a data warehouse, and consequently streamlining business tasks. However, the business context has shifted […]

Data Virtualization Use Cases

Data virtualization, in a nutshell, utilizes data integration without replication. In this process, a single “virtual” data layer is created to provide data services to multiple users and applications at the same time. Why Data Virtualization Is a Necessity for Enterprises explains how data virtualization helps tackle data movement challenges by making a virtual dataset […]

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