Betting the Company on a Graph Database - Aseem Kishore @ GraphConnect Boston 2013

Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazon’s cloud environment. And a thorough performance evaluation in Amazon’s EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.

This document summarizes a student's submission of Peer Assessment 1 for a course on reproducible research. The analysis involves loading step count data, then calculating and visualizing: [1] the total number of steps taken each day, [2] the average steps by 5-minute interval across all days, and [3] whether there are differences in activity levels between weekdays and weekends. Code is shown for each step of loading data, performing calculations, and creating visualizations using R Markdown, knitr, dplyr and ggplot2.

Mack Hardy @mackaffinity from Affinity Bridge @affinitybridge discusses server side mapping tools for drupal, using PostGIS as a spatial backend, generating tiles and managing large sets of geodata and displaying it in Drupal CMS

This document provides an overview of Spark concepts and techniques for machine learning including naive Bayes classification, word2vec, k-means clustering, and semi-supervised learning. It discusses using RDD transformations like map, reduceByKey, and treeAggregate for counting word frequencies. It also covers configuring PySpark memory and using the EM algorithm to incorporate unlabeled data into naive Bayes classification.

This document provides an overview of Spark SQL and its architecture. Spark SQL allows users to run SQL queries over SchemaRDDs, which are RDDs with a schema and column names. It introduces a SQL-like query abstraction over RDDs and allows querying data in a declarative manner. The Spark SQL component consists of Catalyst, a logical query optimizer, and execution engines for different data sources. It can integrate with data sources like Parquet, JSON, and Cassandra.

Title: Neo4j: The World's Leading Graph DB Speaker: George Eleftheriadis (https://gr.linkedin.com/in/george-eleftheriadis-4526ba51/) Date: Monday, April 18, 2016 Event: https://meetup.com/Athens-Big-Data/events/229812890/

NoSQL Graph Databases - Why, When And Where should we use it. Graph DB - The new era of understanding data

This document summarizes machine learning concepts in Spark. It introduces Spark, its components including SparkContext, Resilient Distributed Datasets (RDDs), and common transformations and actions. Transformations like map, filter, join, and groupByKey are covered. Actions like collect, count, reduce are also discussed. A word count example in Spark using transformations and actions is provided to illustrate how to analyze text data in Spark.

Why R is popular for data science? Its important packages for each steps in data mining. Examples with R codes are included.

The document summarizes key Spark API operations including transformations like map, filter, flatMap, groupBy, and actions like collect, count, and reduce. It provides visual diagrams and examples to illustrate how each operation works, the inputs and outputs, and whether the operation is narrow or wide.

R is a powerful language for data analysis and visualization. Some key advantages of R include its data-centric approach, large collection of packages, and powerful data visualization capabilities like ggplot2. The document discusses various R concepts like its functional programming style, object-oriented programming using S3 classes, and non-standard evaluation. It also provides examples of how to access R functions and libraries from Python using rpy2.

R is a language and environment for statistical computing and graphics. It is based on S, an earlier language developed at Bell Labs. R features include being cross-platform, open source, having a package-based repository, strong graphics capabilities, and active user and developer communities. Useful URLs and books for learning R are provided. Instructions for installing R and RStudio on different platforms are given. R can be used for a wide range of statistical analyses and data visualization.

The document discusses Spark GraphX and Pregel algorithms for graph processing. It introduces GraphX and how it represents graphs as RDDs. It then covers the feedback vertex set algorithm for finding cycles in a graph and how it can be parallelized in GraphX. Finally, it discusses Pregel and how it allows for large-scale distributed graph computations through message passing between vertices.

The document discusses the MapReduce framework in Hadoop for processing large amounts of structured and unstructured data in parallel across clusters. It describes how MapReduce works by splitting input, mapping tasks, shuffling, and reducing results. It also explains the HDFS architecture with NameNode, DataNodes, and block replication. Finally, it outlines the overall Hadoop architecture including JobClient, JobTracker, TaskTracker, and their roles in managing jobs.

Overview of the Doradus database open source project and the Cassandra database on which it is based. This presentation was given to the Orange County Big Data Meetup group on July 16, 2014.

The most popular batch processing framework is Apache Hadoop's MapReduce. MapReduce is a Java based system for processing large datasets in parallel. It reads data from the HDFS and divides the dataset into smaller pieces.

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It consists of HDFS for distributed storage and MapReduce for distributed processing. HDFS stores large files across multiple machines and provides high throughput access to application data. MapReduce allows processing of large datasets in parallel by splitting the work into independent tasks called maps and reduces. Companies use Hadoop for applications like log analysis, data warehousing, machine learning, and scientific computing on large datasets.

Rattle is Free (as in Libre) Open Source Software and the source code is available from the Bitbucket repository. We give you the freedom to review the code, use it for whatever purpose you like, and to extend it however you like, without restriction, except that if you then distribute your changes you also need to distribute your source code too. Rattle - the R Analytical Tool To Learn Easily - is a popular GUI for data mining using R. It presents statistical and visual summaries of data, transforms data that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets. One of the most important features (according to me) is that all of your interactions through the graphical user interface are captured as an R script that can be readily executed in R independently of the Rattle interface. Rattle clocks between 10,000 and 20,000 installations per month from the RStudio CRAN node (one of over 100 nodes). Rattle has been downloaded several million times overall.

Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial: 1) What is RDD - Resilient Distributed Datasets 2) Creating RDD in Scala 3) RDD Operations - Transformations & Actions 4) RDD Transformations - map() & filter() 5) RDD Actions - take() & saveAsTextFile() 6) Lazy Evaluation & Instant Evaluation 7) Lineage Graph 8) flatMap and Union 9) Scala Transformations - Union 10) Scala Actions - saveAsTextFile(), collect(), take() and count() 11) More Actions - reduce() 12) Can We Use reduce() for Computing Average? 13) Solving Problems with Spark 14) Compute Average and Standard Deviation with Spark 15) Pick Random Samples From a Dataset using Spark

This document provides an agenda for a session on reporting and analytics options in MongoDB, including Map Reduce, the Aggregation Framework, and examples using geospatial and text search features. It discusses building reports in an application, tuning aggregation pipelines with explain plans, and computing aggregations on the fly or pre-computing and storing them. The next session will cover operational topics like scaling out, high availability, production preparation, and sizing.

Talk given at ClojureD conference, Berlin Apache Spark is an engine for efficiently processing large amounts of data. We show how to apply the elegance of Clojure to Spark - fully exploiting the REPL and dynamic typing. There will be live coding using our gorillalabs/sparkling API. In the presentation, we will of course introduce the core concepts of Spark, like resilient distributed data sets (RDD). And you will learn how the Spark concepts resembles those well-known from Clojure, like persistent data structures and functional programming. Finally, we will provide some Do’s and Don’ts for you to kick off your Spark program based upon our experience. About Paulus Esterhazy and Christian Betz Being a LISP hacker for several years, and a Java-guy for some more, Chris turned to Clojure for production code in 2011. He’s been Project Lead, Software Architect, and VP Tech in the meantime, interested in AI and data-visualization. Now, working on the heart of data driven marketing for Performance Media in Hamburg, he turned to Apache Spark for some Big Data jobs. Chris released the API-wrapper ‘chrisbetz/sparkling’ to fully exploit the power of his compute cluster. Paulus Esterhazy Paulus is a philosophy PhD turned software engineer with an interest in functional programming and a penchant for hammock-driven development. He currently works as Senior Web Developer at Red Pineapple Media in Berlin.

This document summarizes Jeff Thompson's contributions of Spark API visual diagrams to the Spark community under an open source license. It also describes how Databricks further developed the diagrams and commissioned Adam Breindel for this work. The document briefly mentions Databricks' background and products.

The document discusses using MapReduce and NoSQL databases like MongoDB and Accumulo to solve challenges of analyzing large datasets by allowing distributed processing and incremental updates compared to traditional analytical systems. It provides examples of using MapReduce on MongoDB and Accumulo to perform analytics and maintain running aggregates or results. The document also discusses tradeoffs between different approaches and best practices for optimizing performance when using MapReduce and NoSQL databases together.

This document provides an introduction to Apache Spark, including its core components, architecture, and programming model. Some key points: - Spark uses Resilient Distributed Datasets (RDDs) as its fundamental data structure, which are immutable distributed collections that allow in-memory computing across a cluster. - RDDs support transformations like map, filter, reduce, and actions like collect that return results. Transformations are lazy while actions trigger computation. - Spark's execution model involves a driver program that coordinates tasks on worker nodes using an optimized scheduler. - Spark SQL, MLlib, GraphX, and Spark Streaming extend the core Spark API for structured data, machine learning, graph processing, and stream processing

Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.

Gursev Pirge, PhD Senior Data Scientist - JohnSnowLabs

Tomaz Bratanic Graph ML and GenAI Expert - Neo4j

Katja Glaß OpenStudyBuilder Community Manager - Katja Glaß Consulting Marius Conjeaud Principal Consultant - Neo4j

Dmitrii Kamaev, PhD Senior Product Owner - QIAGEN

Atelier - Architecture d’applications de Graphes Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.