Introduction to Graph Databases

An Introduction to Neo4j Graph database, Cypher query language. Comparison between Neo4j and Graph Database

This document provides an overview of graph databases and Neo4j. It begins with an introduction to graph databases and their advantages over relational databases for modeling connected data. Examples of real-world use cases that are well-suited for graph databases are given. The document then describes the core components of the graph data model including nodes, relationships, properties, and labels. It provides examples of how to model data as a graph and query graphs using Cypher, the query language for Neo4j. The document concludes by discussing Neo4j as an example of a graph database and its key features and capabilities.

This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.

This document provides an overview of graph databases and their use cases. It begins with definitions of graphs and graph databases. It then gives examples of how graph databases can be used for social networking, network management, and other domains where data is interconnected. It provides Cypher examples for creating and querying graph patterns in a social networking and IT network management scenario. Finally, it discusses the graph database ecosystem and how graphs can be deployed for both online transaction processing and batch processing use cases.

This document discusses graph databases and introduces DataStax Enterprise Graph. It defines a graph database as one that prioritizes relationships between entities over the entities themselves. It provides examples of problems well-suited for graph databases, such as customer 360 views, recommendations, and fraud detection. The document contrasts graph and relational databases, noting graphs are better for highly connected data. It then introduces DataStax Enterprise Graph as a native graph implementation built on Apache TinkerPop and integrated with Cassandra for scale-out performance and DSE's enterprise features.

This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.

Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.

It is a power point presentation, Prepared by Hyphen. This ppt is attached another doc file which is a Report

GraphFrames provides a unified API for graph queries and algorithms in Apache Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materialized views can greatly improve performance of graph queries by enabling efficient join elimination and reordering. An evaluation found GraphFrames outperforms Neo4j for unanchored queries and approaches performance of GraphX for graph algorithms using whole-stage code generation in Spark SQL. Future work includes automatically suggesting optimal views and exploiting data partitioning.

This document summarizes a presentation about graph databases and Neo4j. It includes case studies of companies like Walmart and Adidas using Neo4j for real-time recommendations. It also discusses how graph databases are better suited than relational databases for recommendation systems because they can easily model relationships between users, products, and transactions. A demo is shown of using Cypher queries to build a recommendation engine in Neo4j by loading product, customer, and order data. The document concludes by providing resources for moving forward with Neo4j.

In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database. ---------------------------------------------------------- Get Socialistic Our website: http://valuebound.com/ LinkedIn: http://bit.ly/2eKgdux Facebook: https://www.facebook.com/valuebound/ Twitter: http://bit.ly/2gFPTi8

MongoDB is a document-oriented NoSQL database written in C++. It uses a document data model and stores data in BSON format, which is a binary form of JSON that is lightweight, traversable, and efficient. MongoDB is schema-less, supports replication and high availability, auto-sharding for scaling, and rich queries. It is suitable for big data, content management, mobile and social applications, and user data management.

The four categories of NoSQL databases When to Use NoSQL When NOT to use NoSQL Use cases NoSQL (Each Category)

Graph databases are well suited for complex, interconnected data. Neo4j is a graph database that represents data as nodes connected by relationships. It allows for complex queries and traversals of graph structures. Unlike relational databases, graph databases can directly model real world networks and relationships without needing to flatten the data.

This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.

Data Science Life Cycle - With Examples. These slides were used for Data Science Meetup - Dubai (November 25, 2017)

This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.

Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.

The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.

This document discusses graph databases and provides examples of how the Neo4j graph database can be used. It shows how Neo4j supports social, spatial, financial and other types of connected data. It also summarizes Neo4j's REST API, support for object-oriented programming, routing algorithms, multiple indexes, recommendation systems, and other use cases. The document advocates for graph databases for any problem involving multiple relationships and connections between entities.

Max De Marzi gave a presentation on Gremlin, a graph traversal language used for traversing property graphs. He explained that Gremlin is implemented by most graph database vendors and is primarily used with Groovy. He demonstrated how to use Gremlin to query a Neo4j graph database, showing examples of traversing nodes and relationships. Finally, he presented a Gremlin script for providing movie recommendations based on a user's genres and ratings.

This document compares relational and non-relational databases. It discusses how in 2003 the main databases were relational, but by 2010 non-relational databases grew popular in the "NoSQL movement". However, the document argues that there are no truly new database designs and that relational and non-relational databases can be combined. It advises to choose a database based on the specific problem and features needed rather than general classifications. The document provides examples of which types of databases fit certain data and access needs.

In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.

There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.

This document provides an introduction to graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for certain types of connected data. It uses social network and movie recommendation examples to demonstrate how to model and query data in a graph database using the Cypher query language.

Ian closely looks at design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.g

Recommendation and personalization systems are an important part of many modern websites. Graphs provide a natural way to represent the behavioral data that is the core input to many recommendation algorithms. Thomas Pinckney and his colleagues at Hunch (recently acquired by eBay) built a large scale recommendation system, and then ported the technology to eBay. Thomas will be discussing how his team uses Cassandra to provide the high I/O storage of their fifty billion edge graphs and how they generate new recommendations in real time as users click around the site.

Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database? Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model. In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.

This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution. The tutorial will give you an understanding of: • Graph theory - origins and concepts • Benefits of graph databases • Different types of graph databases • Typical graph database API • Programming basics • Use cases Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products. To participate in the hands-on portion of the graph tutorial users must have: • Java programming experience • Java Developer Kit (JDK) • Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph) • HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code) Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.