Spark Overview - Spark 1.0.0 Documentation. Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Scala, Java, and Python that make parallel jobs easy to write, and an optimized engine that supports general computation graphs. It also supports a rich set of higher-level tools including Shark (Hive on Spark), MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Get Spark by visiting the downloads page of the Apache Spark site. This documentation is for Spark version 1.0.0. The downloads page contains Spark packages for many popular HDFS versions. If you’d like to build Spark from scratch, visit the building with Maven page. Spark runs on both Windows and UNIX-like systems (e.g. For its Scala API, Spark 1.0.0 depends on Scala 2.10.
Spark comes with several sample programs. . You can also run Spark interactively through modified versions of the Scala shell. . Spark also provides a Python interface. . Example applications are also provided in Python. . API Docs: Real-time geoprocessing with GeoTrellis. 7 command-line tools for data science. Update (05-02-2017) My new company Data Science Workshops provides in-company training and coaching on this exciting topic. Update (7-17-2014) You may be interested in my book Data Science at the Command Line, which contains over 70 command-line tools for doing data science. Data science is OSEMN (pronounced as awesome). That is, it involves Obtaining, Scrubbing, Exploring, Modeling, and iNterpreting data. As a data scientist, I spend quite a bit of time on the command-line, especially when there’s data to be obtained, scrubbed, or explored. I would like to continue this discussion by sharing seven command-line tools that I have found useful in my day-to-day work. 1. jq - sed for JSON JSON is becoming an increasingly common data format, especially as APIs are appearing everywhere.
Imagine we’re interested in the candidate totals of the 2008 presidential election. Curl -s ' where -s puts curl in silent mode. <! FLISoL Aguascalientes 2014 | The Inventor's House. El Festival Latinoamericano de Instalación de Software Libre (FLISoL), es el mayor evento de difusión del Software libre que se realiza desde el año 2005 en diferentes países de manera simultánea.
Este año el FLISoL Aguascalientes es organizado por las diferentes comunidades que año con año buscan la difusión del uso de software y hardware de código abierto, contando con la presencia de Pingüinos en el alambre, la Universidad Autónoma de Aguascalientes, The Inventor´s House, g3ek army, CODEAR (DF) y Digital Frags. ¿Porque el 31 de Mayo? Debido a la Feria Nacional de San Marcos, es difícil contar con espacios para llevar a cabo este evento, es por tal motivo que Aguascalientes lo celebra unas cuantas semanas después sin perder el objetivo y sin alejarse de la organización oficial.
Conferencias: “Big Data : Revelando los secretos de las redes sociales.” Talleres: Aula UNO “Pentesting con bugtraq” por el Ing. Aula DOS *Cada taller tiene una duración máxima de dos horas* Installfest: Lambda Architecture: A state-of-the-art | Datasalt. It’s been some time now since Nathan Marz wrote the first Lambda Architecture post. What has happened since then? How has the community reacted to such a concept?
What are the architectural trends in the Big Data space, as well as the challenges and remaining problems? Big Data: Batch processing-only Despite the attractiveness of a dual batch / real-time architecture, there exists a wide variety of problems in Big Data for which a batch layer is good enough, and I think it will continue to be so. The consolidated adoption of Hadoop, together with the dramatic improvement of its available tools, makes it today often the main architectural requirement for solving many Big Data challenges. (The story of Spiderio’s architectural evolution came into my mind, switching from real-time to batch at some point.)
Big Data: Real-time processing-only Targeted to businesses where real-time is crucial, we are starting to see interesting real-time solutions such as Druid. “Unified” Lambda Architectures. Blog » 5 Big Data Ted Talks Everyone Needs to See. Big data may be a big buzzword, but it's implications are bombarding the business world, offering new insights to old problems and connecting the dots where previously no dots were even seen.
It's a changing space out there, where what you like on Facebook can tell a marketer your inner-most desires, where the speed of algorithms concerns us more than the speed of light, where monuments and memorials are built to honor the humanity in us all -- from a data standpoint. So, yes...big data may indeed be a buzzword, but it's influence on our business models, our lives and even on the grography of our planet is only beginning. These five Ted Talks get to the heart of the massive shift in perception when it comes to utilizing data, from the security to the oddities and everything in between. It's time to learn up on the data revolution, and begin to understand your data rights.
Plus, it's Friday -- and all of these talks are just really cool. 1) Jennifer Golbeck: The Curly Fry Conundrum. Ooyala/spark-jobserver. Evankirstel : These are a few of @hyounpark's... A real-time architecture using Hadoop and Storm at Devoxx. Netflix Reveals All (well, at least a lot) Last night I had the distinct pleasure of attending a Data Science Track event sponsored by the LA Machine Learning meetup group: Data Science @ Netflix. Held at the new, much larger, Cross Campus location in Santa Monica, the event attracted 250 people with another hundred-plus on hand at a satellite location in Pasadena using a streaming video link. Presenting were Douglas Twisselmann, Ph.D., Senior Data Scientist, and Kevin Wylie, Director of Content Data Science, from the Netflix content team in Beverly Hills.
Netflix has another data science group in Los Gatos, Calif. The Netflix content team is tasked with the challenge of licensing/purchasing/developing the best TV and movies for its 44 million users in 41 countries. Netflix does it right with both a Data Science Engineering and Science & Algorithms group. One cool slide included in the presentation, and worth the price of admission in my opinion, was a list of machine learning technology Netflix uses in one form or another: Spark SQL Programming Guide - Spark 1.0.0 Documentation. Spark SQL is currently an Alpha component. Therefore, the APIs may be changed in future releases. Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, SchemaRDD. SchemaRDDs are composed Row objects along with a schema that describes the data types of each column in the row.
A SchemaRDD is similar to a table in a traditional relational database. All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell. Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using Spark.
All of the examples on this page use sample data included in the Spark distribution and can be run in the pyspark shell. The entry point into all relational functionality in Spark is the SQLContext class, or one of its descendants. Using Parquet. As MapReduce fades, Apache Spark is now a top-level project. MapReduce was fun and pretty useful while it lasted, but it looks like Spark is set to take the reins as the primary processing framework for new Hadoop workloads. The technology took a meaningful, if not huge, step toward that end on Thursday when the Apache Software Foundation announced that Spark is now a top-level project. Spark has already garnered a large and vocal community of users and contributors because it’s faster than MapReduce (in memory and on disk) and easier to program. This means it’s well suited for next-generation big data applications that might require lower-latency queries, real-time processing or iterative computations on the same data (i.e., machine learning).
Spark’s creators from the University of California, Berkeley, have created a company called Databricks to commercialize the technology. Spark is technically a standalone project, but it was always designed to work with the Hadoop Distributed File System. The ecosystem of Spark projects. Why Apache Spark is a Crossover Hit for Data Scientists. Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.
Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.) Data scientists performing investigative analytics use interactive statistical environments like R to perform ad-hoc, exploratory analytics in order to answer questions and gain insights. By contrast, data scientists building operational analytics systems have more in common with engineers. They build software that creates and queries machine-learning models that operate at scale in real-time serving environments, using systems languages like C++ and Java, and often use several elements of an enterprise data hub, including the Apache Hadoop ecosystem. A World of Tradeoffs.
Abxda : Test Lab of Parallel processing... Abxda : Collecting tweets via... Spark and Elasticsearch. Elastic Sparkle If you work in the Hadoop world and have not yet heard of Spark, drop everything and go check it out. It's a really powerful, intuitive and fast map/reduce system (and some). Where it beats Hadoop/Pig/Hive hands down is it's not a massive stack of quirky DSLs built on top of layers of clunky Java abstractions - it's a simple, pure Scala functional DSL with all the flexibility and succinctness of Scala. And it's fast, and properly interactive - query, bam response snappiness - not query, twiddle fingers, wait a bit.. response. And if you're into search, you'll no doubt have heard of Elasticsearch - a distributed restful search engine built upon Lucene.
They're perfect bedfellows - crunch your raw data and spit it out into a search index ready for serving to your frontend. It so fast, it flies - we can process raw event logs at 250,000 events/s without breaking a sweat on a meagre EC2 m1.large instance. Anyway, enough babbling... to the demo... Download Get Spark from here: Run. SparkR by amplab-extras. SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. SparkR exposes the Spark API through the RDD class and allows users to interactively run jobs from the R shell on a cluster. NOTE: As of April 2015, SparkR has been officially merged into Apache Spark and is shipping in an upcoming release (1.4) due early summer 2015. You can contribute and follow SparkR developments on the Apache Spark mailing lists and issue tracker. NOTE: The API from the upcoming Spark release (1.4) will not have the same API as described here.
Initial support for Spark in R be focussed on high level operations instead of low level ETL. Features SparkR exposes the RDD API of Spark as distributed lists in R. Sc <- sparkR.init("local") lines <- textFile(sc, " wordsPerLine <- lapply(lines, function(line) { length(unlist(strsplit(line, " "))) }) In addition to lapply, SparkR also allows closures to be applied on every partition using lapplyWithPartition. . . . .
Scala as a platform for statistical computing and data science | Darren Wilkinson's research blog. A feature wish list It should: The not-very-surprising punch-line is that Scala ticks all of those boxes and that I don’t know of any other languages that do. But before expanding on the above, it is worth noting a couple of (perhaps surprising) omissions. For example: have excellent data viz capability built-inhave vast numbers of statistical routines in the standard library The above are points (and there are other similar points) where other languages (for example, R), currently score better than Scala. I will now expand briefly on each point in turn. be a general purpose language with a sizable user community and an array of general purpose libraries, including good GUI libraries, networking and web frameworks History has demonstrated, time and time again, that domain specific languages (DSLs) are synonymous with idiosyncratic, inconsistent languages that are terrible for anything other than what they were specifically designed for.
Be free, open-source and platform independent Summary. The Analytics Maturity Spectrum | The Nomadic Developer. There is no doubt “Big Data” has taken the tech world by storm. I have spent much of 2013 talking about analytics and data science with people all around the US, going to conferences like Strata, and immersing myself in this world for the last 12 months. Over the course of this journey, I have started to notice some patterns about how various people in various kinds of organizations understand and invest in analytics. The analytics led company is a concept I will define here as a company that seeks to use analytics (predictive, prescriptive, or descriptive) as one of their chief competitive weapons.
The canonical example is Amazon, whose use of analytics is part of the DNA of the company. However, there are other more traditional companies that are analytics led, such as Walmart, Proctor and Gamble, Kolhs, and dozens of others. In companies that are analytics led, analytics capabilities are spread throughout the company. The next category in the continuum are analytics aware companies. MapReduce and Spark | Cloudera VISION. About a week ago, I posted an article on Cloudera’s strategy on SQL in the Apache Hadoop ecosystem. In the article, I argued that a special-purpose distributed query processing engine will perform better than one that translates work into a general-purpose MapReduce framework, even if MapReduce is improved to trim latency and improve throughput.
Notwithstanding that bet, we simultaneously believe that the ecosystem needs a high-performance alternative to the current MapReduce implementation. That view is shared by the community generally. In this piece, I want to walk through the history, the current status and the short- and long-term future of the Hadoop platform, concentrating especially on MapReduce. Where We Came From The earliest instance of the architecture at Google combined flexible, scalable storage with a single processing framework — MapReduce — to handle a wide variety of processing and analytic workloads. Where We Are Enter Spark Why not Improve MapReduce? The Near Future. 5 lessons we learned about data science in 2013 - VentureBeat.
How can big data and smart analytics tools ignite growth for your company? Find out at DataBeat, May 19-20 in San Francisco, from top data scientists, analysts, investors, and entrepreneurs. Register now and save $200! Most people know what marketing executives do every day. They try to catch people’s attention through email, ads, tweets, and press releases. As for data scientists, well, their work is not nearly as well understood. That’s been slowly changing this year as companies slowly loosen up about letting their hard-won data scientists talk about their work. This year, VentureBeat has learned a lot about these fawned-over specimens. Data scientists should be creative This point became clear as Jeremy Howard, the former president of data science competition-holder Kaggle, spoke with fellow luminaries in the field at VentureBeat’s 2013 DataBeat/Data Science Summit event a few weeks ago.
Choose a business problem and then the tools, not the other way around What’s coming in 2014. Bajozocalo : Miren qué joya nos llegó... Ml.pdf. Data Scientists and Data Engineers like Python and Scala. Scala eXchange 2013: Jan Macháček on #M. Abxda : 1.2 millions of blocks stratified... Desmesura. Que es big data huejutla uaeh. ¿Qué es big data? ¿Qué es Big Data? Abxda: 1.2 Millions of Blocks stratified... Abxda : 1.2 Millions of Blocks stratified... Abxda : Online Data Analysis =... Coursera.org. Abxda : Eclecticism, the #bigdata soil... NodeXL: Network Overview, Discovery and Exploration for Excel - Home. Abxda : Visual Summary of #BigData... Abxda : Visual Summary of #BigData... e91a32d0-2bac-11e3-bfe2-00144feab7de. Five habits of highly successful analysts. To Hadoop or Not to Hadoop? @BestofAnalytics #bigdata. Abxda : Una aplicación de #BigData...
Busting 10 myths about Hadoop. Zeichick’s Take: Ignore Hadoop at your peril. ¿Qué es Big Data? ¿Qué es Big Data? Green Tea Press: Free Computer Science Books. Abxda : #BigData Tiene que ver con... Abxda : #BigData la perspectiva de... Teukufarhan : Anatomy of A Data Scientist... Cassandra vs HBase. Abxda : Big Data: The new natural... Welcome to Forbes. Micro Jobs. Detalle Artículo TELOS. Big Data. Curious » Eucalyptus: Setting up a private infrastructure cloud. Blog « Aurelius. Designing a Secure REST (Web) API without OAuth. Confused About Map/Reduce? Prudence: Scalable REST/JVM Web Development Platform - Three Crickets. 3.4 Scaling Web Applications. Suggesions on large scale web applications architecture. Deploying the Aurelius Graph Cluster.
Marko A. Rodriguez. Titan: Big Graph Data with Cassandra. Getting Started · thinkaurelius/titan Wiki. The Benefits of Titan · thinkaurelius/titan Wiki. Titan. Graph database. Products & Solutions. Static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/es-419//pubs/archive/36632. Wiki.apache.org/incubator/DrillProposal?action=AttachFile&do=get&target=Drill+slides.pdf. New Apache project will Drill big data in near real time. For fast, interactive Hadoop queries, Drill may be the answer — Cloud Computing News.