Graph Databases - RedisGraph and RedisInsight

Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.

The document is a presentation by Manash Ranjan Rautray on introducing graph databases and Neo4j. It discusses what a graph and graph database are, provides examples to illustrate graphs, and covers the basics of using Neo4j including its data model, query language Cypher, and real-world use cases for graph databases. The presentation aims to explain the concepts and capabilities of Neo4j for storing and querying connected data.

The RAPIDS Accelerator for Apache Spark is a plugin that enables the power of GPUs to be leveraged in Spark DataFrame and SQL queries, improving the performance of ETL pipelines. User-defined functions (UDFs) in the query appear as opaque transforms and can prevent the RAPIDS Accelerator from processing some query operations on the GPU. This presentation discusses how users can leverage the RAPIDS Accelerator UDF Compiler to automatically translate some simple UDFs to equivalent Catalyst operations that are processed on the GPU. The presentation also covers how users can provide a GPU version of Scala, Java, or Hive UDFs for maximum control and performance. Sample UDFs for each case will be shown along with how the query plans are impacted when the UDFs are processed on the GPU.

Noah Davis & Luke Melia of Weplay share a series of examples of Redis in the real world. In doing so, they cover a survey of Redis' features, approach, history and philosophy. Most examples are drawn from the Weplay team's experience using Redis to power features on Weplay.com, a social site for youth sports.

This document provides an overview of a Neo4j basic training session. The training will cover querying graph patterns with Cypher, designing and implementing a graph database model, and evolving existing graphs to support new requirements. Attendees will learn about graph modeling concepts like nodes, relationships, properties and labels. They will go through a modeling workflow example of developing a graph model to represent airport connectivity data from a CSV file and querying the resulting graph.

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.

These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.

Spark is a distributed data processing framework that uses RDDs (Resilient Distributed Datasets) to represent data distributed across a cluster. RDDs support transformations like map, filter, and actions like reduce to operate on the distributed data in a parallel and fault-tolerant manner. Key concepts include lazy evaluation of transformations, caching of RDDs, and use of broadcast variables and accumulators for sharing data across nodes.

Graph algorithms can be expressed using linear algebra operations on matrices. Common graph operations like breadth-first search, shortest paths, and connectivity can be implemented using matrix-vector and matrix-matrix multiplications over semirings. This allows graph problems to be solved using high-performance linear algebra libraries and exploits parallelism.

An introduction of the Neo4j Graph database. Introduces the NOSQL space, the Graph Database concept, and Neo4j with examples.

This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.

The document discusses how property graph databases like Neo4j can model and query relationship data more effectively than relational or other NoSQL databases. It provides examples of modeling user, movie, and product data as graphs and executing queries in Cypher. It also discusses using the Java Core API and Traversal API to navigate graph data and developing recommendation systems and applications for fraud detection by analyzing patterns in user behaviors and connections.

This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data. During this webinar you'll learn: -Basic Cypher syntax -How to construct graph patterns using Cypher -Querying existing data -Data import with Cypher -Using aggregations such as statistical functions -Extending the power of Cypher using procedures and functions

This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.

Presented at JavaOne 2013, Tuesday September 24. "Data Modeling Patterns" co-created with Ian Robinson. "Pitfalls and Anti-Patterns" created by Ian Robinson.

This document outlines an agenda and logistics for a training on Neo4j fundamentals and Cypher. It introduces graph concepts like nodes, relationships, and properties. It discusses why graphs are useful and shows examples of real-world domains that can be modeled as graphs. The training will cover introductory Cypher concepts like creating and matching patterns, and modeling exercises like representing a social network or movie genres graph. Logistics are provided like the WiFi password and a suggestion to work together in pairs on exercises.

This document summarizes the key concepts and components of Gremlin's graph traversal machinery: - Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains. - Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine. - The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal. - Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.

This document introduces graph databases and Neo4j. It discusses different database types and how graph databases are better suited to store connected data compared to relational databases. It provides an overview of Neo4j's core concepts like nodes, relationships, and properties. It also demonstrates how to query graph data using the Cypher query language, including finding friends, friends of friends, and calculating Bacon numbers. Resources for learning more about Neo4j and Cypher are also listed.

This document discusses property graphs and how they are represented and queried using Morpheus, a graph query engine for Apache Spark. Morpheus allows querying property graphs using Cypher and represents property graphs using DataFrames, with node and relationship data stored in tables. It integrates with various data sources and supports federated queries across multiple property graphs. The document provides examples of loading property graph data from sources like JSON, SQL databases and Neo4j, creating graph projections, running analytical queries, and recommending businesses based on graph algorithms.

Big Data with Hadoop & Spark Training: http://bit.ly/2sf2z6i This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide: 1) Introduction to DataFrames 2) Creating DataFrames from JSON 3) DataFrame Operations 4) Running SQL Queries Programmatically 5) Datasets 6) Inferring the Schema Using Reflection 7) Programmatically Specifying the Schema

Fuse graph, document and relational data from transactional and analytic data sources, into a property graph “bird’s eye view”. The property graph data model is Chen’s “entity relationship” model, without clutter. Use “ASCII Art” visual property graph schemas to define “graph data lifts”, mapping from data lake, RDBMS, RDF or graph data cloud services into Spark. Graphs in Spark draw on multiple data sources. Leverage the Cypher query language to combine, split, and project graphs in Spark memory. Graph data is “woven” in Spark without altering or copying the original source. The results of graph workloads can be written back into HDFS or other file systems. Graphs can be read from, stored and merged into a Neo4j transactional database. And tabular datasets can be extracted from graphs. Data scientists and engineers load, wrangle and analyze mixed model data through Morpheus transformations. Enterprises use graphs to catalogue their disparate data assets and processes. They store graph datasets in the data lake. In a world of concern about data protection, see how graph data lifts allow tailored, canonical data views to be realized, in Spark, without remodeling and moving data. Morpheus combines SparkSQL and Cypher queries, and table/graph functions.Choose the right language for the job: eliminate cumbersome multi-joins for connected-data traversals by using super-concise Cypher patterns for sub-graph detection and graph projection; use the power of table projection, grouping, aggregation in SparkSQL, all in one application. Feel free to “dismantle your graph”: expose your graph nodes or relationships as dataframes, or as Hive tables. Key Takeaways Graph technology meets Big Data and Spark Analytics Property graphs: the superset data model Graph, relational and document data, interwoven Lift, split, combine, and create new graphs, from any data source Get your data fit to exploit graph compute, without losing any of your existing tools undefined undefined undefined undefined undefined

This document provides an overview of Neo4j, a graph database, and the Cypher query language. It discusses how graphs are useful for modeling connected data and provides examples showing Neo4j outperforming a relational database for social network queries. It also summarizes key aspects of the Cypher language, including MATCH, WHERE, CREATE, DELETE clauses and functions. Code examples are given for embedded Neo4j usage and shortest path queries.

Spark Datasets are an evolution of Spark DataFrames which allow us to work with both functional and relational transformations on big data with the speed of Spark.

NoSQL database have grown popularity in recent years due to the flexibility of data modeling and scaling up capabilities. NoSQL database also have been using in big data landscape. The demo rich session will elaborate difference between SQL and NoSQL. And end to end solution for data moving capabilities from NoSQL database MongoDB by using Azure data factory.