Introduction to Apache Hive

In a recent Big Data Warehousing Meetup in NYC, Caserta Concepts partnered with Datameer to explore big data analytics techniques. In the presentation, we made a Hive vs. Pig Comparison. For more information on our services or this presentation, please visit www.casertaconcepts.com or contact us at info (at) casertaconcepts.com. http://www.casertaconcepts.com

Join Marc Linster and Kachan Mohitey as they show you how to migrate from Oracle to Postgres in the cloud. This hands-on webinar will cover a number of topics including: Highlights include: • Identifying good migration candidates • Reviewing the key capabilities needed to run Postgres reliably in the cloud • Demoing on how to migrate tables, views, stored procedures, data, etc.

This document discusses improving Python and Spark performance and interoperability with Apache Arrow. It begins with an overview of current limitations of PySpark UDFs, such as inefficient data movement and scalar computation. It then introduces Apache Arrow, an open source in-memory columnar data format, and how it can help by allowing more efficient data sharing and vectorized computation. The document shows how Arrow improved PySpark UDF performance by 53x through vectorization and reduced serialization. It outlines future plans to further optimize UDFs and integration with Spark and other projects.

1) Columnar formats like Parquet, Kudu and Arrow provide more efficient data storage and querying by organizing data by column rather than row. 2) Parquet provides an immutable columnar format well-suited for storage, while Kudu allows for mutable updates but is optimized for scans. Arrow provides an in-memory columnar format focused on CPU efficiency. 3) By establishing common in-memory and on-disk columnar standards, Arrow and Parquet enable more efficient data sharing and querying across systems without serialization overhead.

The document discusses authoring and hosting applications on YARN using Slider. It provides an overview of Slider, which allows deploying and managing applications on a YARN cluster. It then covers topics like simplified packaging that makes it easier to run simple applications, application upgrades using rolling upgrades without downtime, security enhancements like application keytabs and certificate stores, and integration with Docker to deploy Dockerized applications on YARN via Slider.

The document discusses enabling diverse workload scheduling in YARN. It covers several topics including node labeling, resource preemption, reservation systems, pluggable scheduler behavior, and Docker container support in YARN. The presenters are Wangda Tan and Craig Welch from Hortonworks who have experience with big data systems like Hadoop, YARN, and OpenMPI. They aim to discuss how these features can help different types of workloads like batch, interactive, and real-time jobs run together more happily in YARN.

This document provides information about Big Data certifications. It discusses why individuals and companies may want to pursue certifications, the various certification options available, what the certification tests entail, and next steps after completing a certification. Certifications can provide benefits like partnerships with vendors, discounts, and publicity for consulting firms and companies. The document outlines certification options for Hadoop developers, administrators, data analysts, and Spark developers from vendors like Cloudera, Hortonworks, and MapR. It provides sample exam objectives and available study materials. The certification tests are remotely proctored and may provide access to a test cluster. Results are typically available the same day, and the document recommends sharing the certification accomplishment with employers and professional networks

Pivotal, la plateforme Big Data signé EMC, embarque des technologies pour gérer des requêtes sql en mémoire très performante et pas que ... Présentation de Alexandre Vasseur et Jérôme Campo de Pivotal

These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book

This document provides an overview of real-time processing capabilities on Hortonworks Data Platform (HDP). It discusses how a trucking company uses HDP to analyze sensor data from trucks in real-time to monitor for violations and integrate predictive analytics. The company collects data using Kafka and analyzes it using Storm, HBase and Hive on Tez. This provides real-time dashboards as well as querying of historical data to identify issues with routes, trucks or drivers. The document explains components like Kafka, Storm and HBase and how they enable a unified YARN-based architecture for multiple workloads on a single HDP cluster.

Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations. View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/

This slide deck is used as an introduction to the Apache Pig system and the Pig Latin high-level programming language, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom. Course website: http://michiard.github.io/DISC-CLOUD-COURSE/ Sources available here: https://github.com/michiard/DISC-CLOUD-COURSE

Big Data" šodien ir viens no populārākajiem mārketinga saukļiem, kas tiek pamatoti un nepamatoti izmantots, runājot par (lielu?) datu uzglabāšanu un apstrādi. Prezentācijā es aplūkošu, kas tad patiesībā ir "big data" no tehnoloģijju viedokļa, kādi ir galvenie izmantošanas scenāriji un ieguvumi. Prezentācijā apskatīšu tādas tehnoloģijas kā Hadoop, HDFS, MapReduce, Impala, Sparc, Pig, Hive un citas. Tāpat tiks apskatīta integrācija ar tradicionālām DBVS un galvenie izmantošanas scenāriji.

Part five in a five-part series, this webcast will be a demonstration of the integration of Apache Zeppelin and Pivotal HDB. Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. This webinar will demonstrate the configuration of the psql interpreter and the basic operations of Apache Zeppelin when used in conjunction with Hortonworks HDB.

This document provides an introduction to Apache Pig, including: - Pig is a system for processing large unstructured data using HDFS and MapReduce. It uses a high-level data flow language called Pig Latin. - Pig aims to increase programmer productivity by abstracting low-level MapReduce jobs and providing a procedural language for parallel data flows. - Pig components include the Pig engine for parsing, optimizing, and executing queries, and the Grunt shell for running interactive commands. - The document then covers Pig data types, input/output, relational operations, user-defined functions, and new features in Pig version 0.10.0.

DeathStar is a system that runs HBase on YARN to provide easy, dynamic multi-tenant HBase clusters via YARN. It allows different applications to run HBase in separate application-specific clusters on a shared HDFS and YARN infrastructure. This provides strict isolation between applications and enables dynamic scaling of clusters as needed. Some key benefits are improved cluster utilization, easier capacity planning and configuration, and the ability to start new clusters on demand without lengthy provisioning times.

S3Guard provides a consistent metadata store for S3 using DynamoDB. It allows file system operations on S3, like listing and getting file status, to be consistent by checking results from S3 against metadata stored in DynamoDB. Mutating operations write to both S3 and DynamoDB, while read operations first check S3 results against DynamoDB to handle eventual consistency in S3. The goal is to improve performance of real workloads by providing consistent metadata operations on S3 objects written with S3Guard enabled.

Description of how Sematext SPM Performance Monitoring service is built and how it works. Originally presented at Berlin Buzzwords 2012.

The document is a presentation on new features in Hadoop 2. Some key highlights include: - Hadoop 2 introduces NameNode high availability to address single point of failure through an active-passive setup using shared storage. - Federation allows spreading metadata over multiple NameNodes for very large clusters. - Snapshots provide point-in-time copies of data for backup and recovery from deletes or disasters. - YARN separates processing from resource management, allowing various types of applications beyond batch processing.

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in handling large amounts of data in a scalable, cost-effective manner. While early adoption was in web companies, enterprises are increasingly adopting Hadoop to gain insights from new sources of big data. However, Hadoop deployment presents challenges for enterprises in areas like setup/configuration, skills, integration, management at scale, and backup/recovery. Greenplum HD addresses these challenges by providing an enterprise-ready Hadoop distribution with simplified deployment, flexible scaling of compute and storage, seamless analytics integration, and advanced management capabilities backed by enterprise support.

This document summarizes a presentation about Adobe Connect for government use. It discusses how government agencies are using Adobe Connect for online training and collaboration. It also outlines Adobe's plans to support HTML5 to allow access without Flash and achieve FedRAMP compliance. The presentation demonstrates current HTML5 capabilities and indicates Adobe is working to fully deliver Adobe Connect via HTML5 as browsers progress.

Hadoop / Spark Conference Japan 2016 キーノート講演資料 The Evolution and Future of Hadoop Storage Cloudera Todd Lipcon氏

This deck gives a sample overview on different pain points while building the infrastructure for big data and solutions to the same.

Making Generator plugins for Photoshop with Node.js - slides for a talk I gave at JSConf Asia in Manila.