PyCascading for Intuitive Flow Processing with Hadoop (gabor szabo)

The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.

Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.

Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens. The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.

Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.

Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.

Slides from Tathagata Das's talk at the Spark Meetup entitled "Deep Dive with Spark Streaming" on June 17, 2013 in Sunnyvale California at Plug and Play. Tathagata Das is the lead developer on Spark Streaming and a PhD student in computer science in the UC Berkeley AMPLab.

This slide introduces Hadoop Spark. Just to help you construct an idea of Spark regarding its architecture, data flow, job scheduling, and programming. Not all technical details are included.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.

Spark Streaming is an extension of the core Spark API that enables continuous data stream processing. It is particularly useful when data needs to be processed in real-time. Carol McDonald, HBase Hadoop Instructor at MapR, will cover: + What is Spark Streaming and what is it used for? + How does Spark Streaming work? + Example code to read, process, and write the processed data

Apache Spark is a fast and general engine for large-scale data processing. It was originally developed in 2009 and is now supported by Databricks. Spark provides APIs in Java, Scala, Python and can run on Hadoop, Mesos, standalone or in the cloud. It provides high-level APIs like Spark SQL, MLlib, GraphX and Spark Streaming for structured data processing, machine learning, graph analytics and stream processing.

Created at the University of Berkeley in California, Apache Spark combines a distributed computing system through computer clusters with a simple and elegant way of writing programs. Spark is considered the first open source software that makes distribution programming really accessible to data scientists. Here you can find an introduction and basic concepts.

This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.

This document provides an agenda for an advanced Spark class covering topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, shuffle operations, and Spark Streaming. The class will be held in March 2015 and include lectures, labs, and Q&A sessions. It notes that some slides may be skipped and asks attendees to keep Q&A low during the class, with a dedicated Q&A period at the end.

kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderaba www.kellytechno.com

This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.

AngularJS, together with Node.js, is an extremely powerful combination for building single page applications. Unfortunately, its development requires writing HTML and Javascript, which is tedious and error prone. By using vibe.d, HTML is no longer necessary, and the developers can use the full power of a static-typed language for the development of the backend. Substituting Javascript with Typescript in addition to a little bit of CTFE D magic then removes the need for redundant data type declarations, and makes everything statically typed. At the end of the talk, the attendee will have witnessed the creation of a statically typed, asynchronous single page application that required little extra typing than its dynamically typed equivalent. Additionally, the attendees will be motivated to explore the presented combination of frameworks as a viable desktop application UI framework.

ADAM is an open source, high performance, distributed platform for genomic analysis built on Apache Spark. It defines a Scala API and data schema using Avro and Parquet to store data in a columnar format, addressing the I/O bottleneck in genomics pipelines. ADAM implements common genomics algorithms as data or graph parallel computations and minimizes data movement by sending code to the data using Spark. It is designed to scale to processing whole human genomes across distributed file systems and cloud infrastructure.

Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping. In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.

In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop. Finally we introduce spark using a docker image and we show how to use anonymous function in spark. The topics of the next slides will be - Spark Shell (Scala, Python) - Shark Shell - Data Frames - Spark Streaming - Code Examples: Data Processing and Machine Learning

Hadoop Streaming allows any executable or script to be used as a MapReduce job. It works by launching the executable or script as a separate process and communicating with it via stdin and stdout. The executable or script receives key-value pairs in a predefined format and outputs new key-value pairs that are collected. Hadoop Streaming uses PipeMapper and PipeReducer to adapt the external processes to the MapReduce framework. It provides a simple way to run MapReduce jobs without writing Java code.

This document discusses experiences using Hadoop and HBase in the Perf-Log project. It provides an overview of the Perf-Log data format and architecture, describes how Hadoop and HBase were configured, and gives examples of using MapReduce jobs and HBase APIs like Put and Scan to analyze log data. Key aspects covered include matching Hadoop and HBase versions, running MapReduce jobs, using column families in HBase, and filtering Scan results.

The document discusses using Python with Hadoop frameworks. It introduces Hadoop Distributed File System (HDFS) and MapReduce, and how to use the mrjob library to write MapReduce jobs in Python. It also covers using Python with higher-level Hadoop frameworks like Pig, accessing HDFS with snakebite, and using Python clients for HBase and the PySpark API for the Spark framework. Key advantages discussed are Python's rich ecosystem and ability to access Hadoop frameworks.

This document presents m2r2, a framework for materializing and reusing results in high-level dataflow systems for big data. The framework operates at the logical plan level to be language-independent. It includes components for matching plans, rewriting queries to reuse past results, optimizing plans, caching results, and garbage collection. An evaluation using the TPC-H benchmark on Pig Latin showed the framework reduced query execution time by 65% on average by reusing past query results. Future work includes integrating it with more systems and minimizing materialization costs.

The document provides an overview of data science with Python and integrating Python with Hadoop and Apache Spark frameworks. It discusses: - Why Python should be integrated with Hadoop and the ecosystem including HDFS, MapReduce, and Spark. - Key concepts of Hadoop including HDFS for storage, MapReduce for processing, and how Python can be integrated via APIs. - Benefits of Apache Spark like speed, simplicity, and efficiency through its RDD abstraction and how PySpark enables Python access. - Examples of using Hadoop Streaming and PySpark to analyze data and determine word counts from documents.

SF Bay Area Azure Developers meetup at Microsoft, SF on 2013-06-11 http://www.meetup.com/bayazure/events/120889902/

Features and Working with PIG

This document provides an overview of Apache Cassandra, a distributed database designed for managing large amounts of structured data across commodity servers. It discusses Cassandra's data model, which is based on Dynamo and Bigtable, as well as its client API and operational benefits like easy scaling and high availability. The document uses a Twitter-like application called StatusApp to illustrate Cassandra's data model and provide examples of common operations.

This document summarizes a presentation by Erin LeDell from H2O.ai about machine learning using the H2O software. H2O is an open-source machine learning platform that provides APIs for R, Python, Scala and other languages. It allows distributed machine learning on large datasets across clusters. The presentation covers H2O's architecture, algorithms like random forests and deep learning, and how to use H2O within R including loading data, training models, and running grid searches. It also discusses H2O on Spark via Sparkling Water and real-world use cases with customers.

"Apache Spark is today’s fastest growing Big Data analysis platform. Spark workloads typically maintain a persistent data set in memory, which is accessed multiple times over the network. Consequently, networking IO performance is a critical component in Spark systems. RDMA’s performance characteristics, such as high bandwidth, low latency, and low CPU overhead, offer a good opportunity for accelerating Spark by improving its data transfer facilities." "In this talk, we present a Java-based, RDMA network layer for Apache Spark. The implementation optimized both the RPC and the Shuffle mechanisms for RDMA. Initial benchmarking shows up to 25% improvement for Spark Applications." Watch the video presentation: http://wp.me/p3RLHQ-gzN Learn more: http://mellanox.com Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

This document provides an introduction to Hadoop, including: - An overview of big data and the challenges it poses for data storage and processing. - How Hadoop addresses these challenges through its distributed, scalable architecture based on MapReduce and HDFS. - Descriptions of key Hadoop components like MapReduce, HDFS, Hive, and Sqoop. - Examples of how to perform common data processing tasks like word counting and friend recommendations using MapReduce. - Some best practices, limitations, and other tools in the Hadoop ecosystem.

A short overview presentation on Emerging technologies /frameworks in Big Data covering Apache Parquet, Apache Flink, Apache Drill with basic concepts of Columnar Storage and Dremel.

Description of some of the elements that go in to creating a PostgreSQL-as-a-Service for organizations with many teams and a diverse ecosystem of applications and teams.

Johnny Miller – Cassandra + Spark = Awesome This talk will discuss how Cassandra and Spark can work together to deliver real-time analytics. This is a technical discussion that will introduce the attendees to the basic principals on Cassandra and Spark, why they work well together and examples usecases.

Impala is an open source SQL query engine for Apache Hadoop that allows real-time queries on large datasets stored in HDFS and other data stores. It uses a distributed architecture where an Impala daemon runs on each node and coordinates query planning and execution across nodes. Impala allows SQL queries to be run directly against files stored in HDFS and other formats like Avro and Parquet. It aims to provide high performance for both analytical and transactional workloads through its C++ implementation and avoidance of MapReduce.

PDF and Keynote version of the presentation available here: https://github.com/h2oai/h2o-meetups/tree/master/2017_04_04_HarvardMed_Scalable_Ensembles

This document outlines the steps to build your own natural language processing (NLP) system, beginning with creating a streaming consumer, launching a message queue service, creating a data pre-processing service, serving an ML model, and publishing predictions to a messaging app. It discusses separating components for modularity and ease of testing/extensibility. The presenter recommends tools like Anaconda, Docker, Redis, Fast.ai and SpaCy and walks through setting up the environment and each step in a Jupyter notebook. The goal is to experiment with building your own end-to-end NLP system in a modular, reusable way.

In the same way that we need to make assertions about how code functions, we need to make assertions about data, and unit testing is a promising framework. In this talk, we'll explore what is unique about unit testing data, and see how Two Sigma's open source library Marbles addresses these unique challenges in several real-world scenarios.

TileDB is an open-source storage manager for multi-dimensional sparse and dense array data. It has a novel architecture that addresses some of the pain points in storing array data on “big-data” and “cloud” storage architectures. This talk will highlight TileDB’s design and its ability to integrate with analysis environments relevant to the PyData community such as Python, R, Julia, etc.

In this talk I will discuss exponential family embeddings, which are methods that extend the idea behind word embeddings to other data types. I will describe how we used dynamic embeddings to understand how data science skill-sets have transformed over the last 3 years using our large corpus of jobs. The key takeaway is that these models can enrich analysis of specialized datasets.

How many newspapers should be distributed to each store for sale every day? The data science group at The New York Times addresses this optimization problem using custom time series modeling and analytical solutions, while also incorporating qualitative business concerns. I'll describe our modeling and data engineering approaches, written in Python and hosted on Google Cloud Platform.

This document provides an introduction to graph theory concepts and working with graph data in Python. It begins with basic graph definitions and real-world graph examples. Various graph concepts are then demonstrated visually, such as vertices, edges, paths, cycles, and graph properties. Finally, it discusses working with graph data structures and algorithms in the NetworkX library in Python, including graph generation, analysis, and visualization. The overall goal is to introduce readers to graph theory and spark their interest in further exploration.

To productionize data science work (and have it taken seriously by software engineers, CTOs, clients, or the open source community), you need to write tests! Except… how can you test code that performs nondeterministic tasks like natural language parsing and modeling? This talk presents an approach to testing probabilistic functions in code, illustrated with concrete examples written for Pytest.

Those of us who use TensorFlow often focus on building the model that's most predictive, not the one that's most deployable. So how to put that hard work to work? In this talk, we'll walk through a strategy for taking your machine learning models from Jupyter Notebook into production and beyond.

In September 2017, dockless bikeshare joined the transportation options in the District of Columbia. In March 2018, scooter share followed. During the pilot of these technologies, Python has helped District Department of Transportation answer some critical questions. This talk will discuss how Python was used to answer research questions and how it supported the evaluation of this demonstration.