Why graph databases are so effective in analytics projects - TechRepublic

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By all definitions, a graph database is not big data technology; it is a NoSQL database that in a growing number of cases is beginning to supplant traditional relational databases. "What makes a graph database so effective is its ability to be a highly intuitive data model and also to reflect how the world really operates by being able to find the relational connections between objects and data," said Ryan Boyd, head of developer relations North America for Neo4j, a graph database solutions provider. Boyd said that graph databases are being adopted in companies because these databases can so effectively and intuitively describe the world through their data handling; because graphs can be very high-performance databases when compared with the performance of traditional relational databases; and because graph databases are agile and can easily optimize new and existing data models with less work. "In a relational database, every JOIN statement requires the application to look at another index to another dataset," said Boyd. "We have enterprise clients that tell us that some of their SQL queries might require over 20 of these JOINs--and this can make data queries really slow. With a graph database, you find a logical starting point and you branch out from there and identify the relationships.


Review: Neo4j supercharges graph analytics

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Neo4j is both the original graph database and the continued leader in the graph database market. Designed to store entities and relationships, and optimized to perform graph operations such as traversals, clustering, and shortest-path calculations, Neo4j shines at exploring data that consists of huge numbers of many-to-many relationships.


Managing Big Data with MySQL Coursera

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The Coursera Specialization, "Managing Big Data with MySQL" is about how'Big Data' interacts with business, and how to use data analytics to create value for businesses. This specialization consists of four courses and a final Capstone Project, where you will apply your skills to a real-world business process. You will learn to perform sophisticated data-analysis functions using powerful software tools such as Microsoft Excel, Tableau, and MySQL. To learn more about the specialization, please review the first lesson below, "Specialization Introduction: Excel to MySQL: Analytic Techniques for Business." In this fourth course of this specialization, "Managing Big Data with MySQL" you will learn how relational databases work and how they are used in business analysis.


From Documents to Content to Data

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Since the 1980s, relational databases have been used to store business information. They were a huge step forward over hierarchical databases, which organized data into rigid tree-like structures with connections between data elements defined by the links in the structures. Relational databases typically store information in rows and columns in tables, with column names providing the linkages between tables. The relational model works terrifically well within a particular database, but can create challenges across databases, particularly at scale when linking vast volumes of data with inconsistent naming conventions. For over two decades, the disciplines of content management and data management have existed in somewhat parallel universes.


SQL - MySQL for Data Analytics and Business Intelligence

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SQL that will get you hired – SQL for Business Analysis, Marketing, and Data Management This is why now is the time to learn SQL and gain a competitive advantage in the job market. Remember, the average salary of a SQL developer is $92,000! Well, when you can work with SQL, it means you don't have to rely on others sending you data and executing queries for you. You can do that on your own. This allows you to be independent and dig deeper into the data to obtain the answers to questions that might improve the way your company does its business.