R Jobs on the Cloud

CloudForecast is a system monitoring and visualization tool that uses Perl and RRDTool to collect data from servers and generate graphs. It collects metrics like CPU usage, network traffic, and Gearman worker status. Data is stored in RRD files and a SQLite database. A radar component collects data and a web interface is used to view graphs generated from the collected data.

Provide a system level and pseudo-code level anatomy of Hive, a data warehousing system based on Hadoop.

This document introduces infrastructure as code (IaC) using Terraform and provides examples of deploying infrastructure on AWS including: - A single EC2 instance - A single web server - A cluster of web servers using an Auto Scaling Group - Adding a load balancer using an Elastic Load Balancer It also discusses Terraform concepts and syntax like variables, resources, outputs, and interpolation. The target audience is people who deploy infrastructure on AWS or other clouds.

The document provides information on advanced functions in RHive including UDF, UDAF, and UDTF. It explains: 1) RHive allows writing UDF and UDAF functions in R and deploying them for use in Hive, allowing R functions to be used at lower complexity levels in Hive's MapReduce programming. 2) The rhive.assign, rhive.export, and rhive.exportAll functions are used to deploy R functions and objects to the distributed Hive environment for processing large datasets. 3) An example demonstrates creating a sum function in R, assigning it using rhive.assign, and executing it on the USArrests Hive table using rhive

Terraform can be used to automate the deployment and management of infrastructure as code. It allows defining infrastructure components like VMs, networks, DNS records etc. as code in configuration files. Key benefits include versioning infrastructure changes, consistency across environments, and automation of deployments. The document then provides details on installing Terraform, using common commands like plan, apply and import, defining resources, variables, modules and managing remote state. It also demonstrates creating an EC2 instance using a generated AMI.

The document discusses Terraform, an infrastructure as code tool. It covers installing Terraform, deploying infrastructure like EC2 instances using Terraform configuration files, destroying resources, and managing Terraform state. Key topics include authentication with AWS for Terraform, creating a basic EC2 instance, validating and applying configuration changes, and storing state locally versus remotely.

Vector and ListBuffer have similar performance for random reads. Benchmarking showed no significant difference in throughput, average time, or sample times between reading randomly from a Vector versus a ListBuffer. Vectors are generally faster than Lists for random access due to Vectors being implemented as arrays under the hood.

We are excited to continue our work on BeanStalk with the introduction of a range of great new features. If you are a Python shop you'll learn how BeanStalk now supports Python containers and the Django and Flask frameworks. Hear about BeanStalk integration with RDS and how custom configuration of containers is possible through simple configuration files.

Foursquare uses MongoDB to power their location-based social network. They have over 9 million users generating around 3 million check-ins per day across over 15 million venues. Foursquare chose MongoDB because it is fast, supports rich queries, sharding, replication, and geo-indexes. Foursquare runs 8 MongoDB clusters across around 40 machines storing over 2.3 billion records and handling around 15,000 queries per second. They developed Rogue, a Scala DSL for MongoDB, to make queries type-safe and add features like pagination, logging, and index awareness.

Terraform allows users to define infrastructure as code to provision resources across multiple cloud platforms. It aims to describe infrastructure in a configuration file, provision resources efficiently by leveraging APIs, and manage the full lifecycle from creation to deletion. Key features include supporting composability across different infrastructure tiers, using a graph-based approach to parallelize operations for efficiency, and managing state to track resource unique IDs and allow recreating resources. Providers enable connectivity to different cloud APIs while resources define the specific infrastructure components and their properties.

The document discusses refactoring Terraform configuration files to improve their design. It provides an example of refactoring a "supermarket-terraform" configuration that originally defined AWS resources across multiple files. The refactoring consolidates the configuration into a single file and adds testing using Test Kitchen. It emphasizes starting small by adding tests incrementally and not making changes without tests to avoid introducing errors.

1. Terraform allows users to define infrastructure as code and treat it like versioned code. It uses configuration files that are shared and versioned. 2. Terraform uses providers to manage cloud infrastructure through their APIs. It generates and executes plans to build, change, and destroy infrastructure based on the configuration files. 3. Terraform supports variables, modules, data sources, and workspaces to help manage infrastructure in different environments like dev, staging, and production in an automated and reusable way.

Tips and experiences about building and running Ruby on Solaris OS, with examples of bug fixes for supporting Solaris on Ruby.

It's Dangerous to GC alone. Take this! IBM's talk on work integrating the OMR GC into Ruby. OMR preview: goo.gl/P3yXuy

Scripting Embulk plugins makes plugin development easier drastically. You can develop, test, and productionize data integrations using any scripting languages. It's most suitable way to integrate data with SaaS using vendor-provided SDKs. https://techplay.jp/event/781988

Akka is using the Actors together with STM to create a unified runtime and programming model for scaling both UP (multi-core) and OUT (grid/cloud). Akka provides location transparency by abstracting away both these tangents of scalability by turning them into an ops task. This gives the Akka runtime freedom to do adaptive automatic load-balancing, cluster rebalancing, replication & partitioning

This document discusses an introduction to Terraform infrastructure as code. It covers what Terraform is, its key features like being declarative and reusable, pros and cons like reducing human error but having a high entry barrier. It discusses major providers supported by Terraform including cloud providers, software, and monitoring tools. It concludes with basic steps for getting started with Terraform like installation, AWS profile setup, creating demo files, and executing commands to provision a VPC on AWS.

Abhishek Sinha is a senior product manager at Amazon for Amazon EMR. Amazon EMR allows customers to easily run data frameworks like Hadoop, Spark, and Presto on AWS. It provides a managed platform and tools to launch clusters in minutes that leverage the elasticity of AWS. Customers can customize clusters and choose from different applications, instances types, and access methods. Amazon EMR allows separating compute and storage where the low-cost S3 can be used for persistent storage while clusters are dynamically scaled based on workload.

"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way. In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR;  how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."

Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.

Introduction to Yaetos, an open source tool for data engineers, scientists, and analysts to easily create data pipelines in python and SQL and put them in production in the AWS cloud. Focus on the Spark component.

The document discusses strategies for scaling LAMP applications on cloud computing platforms like AWS. It recommends: 1) Moving static files to scalable services like S3 and using a CDN to distribute load. 2) Using dedicated caching systems like Memcache instead of local caches and storing sessions in Memcache or DynamoDB for scalability. 3) Scaling databases horizontally using master-slave replication or sharding across multiple availability zones for high availability and read scaling. 4) Leveraging auto-scaling and load balancing on AWS with tools like Elastic Load Balancers, CloudWatch, and scaling alarms to dynamically scale application instances based on metrics.

Amazon EMR is a managed service that makes it easy for customers to use big data frameworks and applications like Apache Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3, Amazon’s highly scalable object storage service. In this session, we will introduce Amazon EMR and the greater Apache Hadoop ecosystem, and show how customers use them to implement and scale common big data use cases such as batch analytics, real-time data processing, interactive data science, and more. Then, we will walk through a demo to show how you can start processing your data at scale within minutes.

In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.

This document summarizes Netflix's big data platform, which uses Presto and Spark on Amazon EMR and S3. Key points: - Netflix processes over 50 billion hours of streaming per quarter from 65+ million members across over 1000 devices. - Their data warehouse contains over 25PB stored on S3. They read 10% daily and write 10% of reads. - They use Presto for interactive queries and Spark for both batch and iterative jobs. - They have customized Presto and Spark for better performance on S3 and Parquet, and contributed code back to open source projects. - Their architecture leverages dynamic EMR clusters with Presto and Spark deployed via bootstrap actions for scalability.

This document discusses various options for migrating data and workloads between on-premises environments and AWS. It covers tools like AWS Database Migration Service for database migration, VM Import/Export for virtual machine migration, copying files between S3 buckets, and using services like Route53 for transitioning traffic during a migration. Specific techniques discussed include copying AMIs, EBS snapshots, security groups, and database parameters between regions; using the AWS Schema Conversion Tool; and DynamoDB cross-region replication.

by Dario Rivera, Solutions Architect, AWS Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices.