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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 […]

So You Want to be a Data Manager?

A Data Manager develops and governs data-oriented systems designed to meet the needs of an organization or research team. Data Management includes accessing, validating, and storing data that is needed for research and day-to-day business operations. Currently, a wide array of organizations are using Big Data to gain insights into customer behavior and to provide […]

Knowledge Graphs: Context, Compliance, and Connections

“Graph is leaving a larger and larger footprint. And that is good,” said Thomas Frisendal in Knowledge Graphs and Data Modeling. Gartner named knowledge graphs as part of an emerging trend toward digital ecosystems, showing relationships among enterprises, people, and things, and enabling seamless, dynamic connections across geographies and industries. Elisa Kendall and Deborah McGuinness, […]

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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