A Modern Data Architecture with Apache™ Hadoop® the Journey to a Data Lake
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper
Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices : AWS Whitepaper Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Connect Data Lake Best Practices AWS Whitepaper Table of Contents Abstract and introduction .................................................................................................................... i Abstract .................................................................................................................................... 1 Are you Well-Architected? ........................................................................................................... 1 Introduction .............................................................................................................................. 1 Amazon Connect ................................................................................................................................ 4 Data lake design principles ................................................................................................................. -
Red Hat Data Analytics Infrastructure Solution
TECHNOLOGY DETAIL RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION TABLE OF CONTENTS 1 INTRODUCTION ................................................................................................................ 2 2 RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION ..................................... 2 2.1 The evolution of analytics infrastructure ....................................................................................... 3 Give data scientists and data 2.2 Benefits of a shared data repository on Red Hat Ceph Storage .............................................. 3 analytics teams access to their own clusters without the unnec- 2.3 Solution components ...........................................................................................................................4 essary cost and complexity of 3 TESTING ENVIRONMENT OVERVIEW ............................................................................ 4 duplicating Hadoop Distributed File System (HDFS) datasets. 4 RELATIVE COST AND PERFORMANCE COMPARISON ................................................ 6 4.1 Findings summary ................................................................................................................................. 6 Rapidly deploy and decom- 4.2 Workload details .................................................................................................................................... 7 mission analytics clusters on 4.3 24-hour ingest ........................................................................................................................................8 -
Unlock Bigdata Analytic Efficiency with Ceph Data Lake
Unlock Bigdata Analytic Efficiency With Ceph Data Lake Jian Zhang, Yong Fu, March, 2018 Agenda . Background & Motivations . The Workloads, Reference Architecture Evolution and Performance Optimization . Performance Comparison with Remote HDFS . Summary & Next Step 3 Challenges of scaling Hadoop* Storage BOUNDED Storage and Compute resources on Hadoop Nodes brings challenges Data Capacity Silos Costs Performance & efficiency Typical Challenges Data/Capacity Multiple Storage Silos Space, Spent, Power, Utilization Upgrade Cost Inadequate Performance Provisioning And Configuration Source: 451 Research, Voice of the Enterprise: Storage Q4 2015 *Other names and brands may be claimed as the property of others. 4 Options To Address The Challenges Compute and Large Cluster More Clusters Storage Disaggregation • Lacks isolation - • Cost of • Isolation of high- noisy neighbors duplicating priority workloads hinder SLAs datasets across • Shared big • Lacks elasticity - clusters datasets rigid cluster size • Lacks on-demand • On-demand • Can’t scale provisioning provisioning compute/storage • Can’t scale • compute/storage costs separately compute/storage costs scale costs separately separately Compute and Storage disaggregation provides Simplicity, Elasticity, Isolation 5 Unified Hadoop* File System and API for cloud storage Hadoop Compatible File System abstraction layer: Unified storage API interface Hadoop fs –ls s3a://job/ adl:// oss:// s3n:// gs:// s3:// s3a:// wasb:// 2006 2008 2014 2015 2016 6 Proposal: Apache Hadoop* with disagreed Object Storage SQL …… Hadoop Services • Virtual Machine • Container • Bare Metal HCFS Compute 1 Compute 2 Compute 3 … Compute N Object Storage Services Object Object Object Object • Co-located with gateway Storage 1 Storage 2 Storage 3 … Storage N • Dynamic DNS or load balancer • Data protection via storage replication or erasure code Disaggregated Object Storage Cluster • Storage tiering *Other names and brands may be claimed as the property of others. -
Assessment of Multiple Ingest Strategies for Accumulo Key-Value Store
Assessment of Multiple Ingest Strategies for Accumulo Key-Value Store by Hai Pham A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Master of Science Auburn, Alabama May 7, 2016 Keywords: Accumulo, noSQL, ingest Copyright 2016 by Hai Pham Approved by Weikuan Yu, Co-Chair, Associate Professor of Computer Science, Florida State University Saad Biaz, Co-Chair, Professor of Computer Science and Software Engineering, Auburn University Sanjeev Baskiyar, Associate Professor of Computer Science and Software Engineering, Auburn University Abstract In recent years, the emergence of heterogeneous data, especially of the unstructured type, has been extremely rapid. The data growth happens concurrently in 3 dimensions: volume (size), velocity (growth rate) and variety (many types). This emerging trend has opened a new broad area of research, widely accepted as Big Data, which focuses on how to acquire, organize and manage huge amount of data effectively and efficiently. When coping with such Big Data, the traditional approach using RDBMS has been inefficient; because of this problem, a more efficient system named noSQL had to be created. This thesis will give an overview knowledge on the aforementioned noSQL systems and will then delve into a more specific instance of them which is Accumulo key-value store. Furthermore, since Accumulo is not designed with an ingest interface for users, this thesis focuses on investigating various methods for ingesting data, improving the performance and dealing with numerous parameters affecting this process. ii Acknowledgments First and foremost, I would like to express my profound gratitude to Professor Yu who with great kindness and patience has guided me through not only every aspect of computer science research but also many great directions towards my personal issues. -
Key Exchange Authentication Protocol for Nfs Enabled Hdfs Client
Journal of Theoretical and Applied Information Technology 15 th April 2017. Vol.95. No 7 © 2005 – ongoing JATIT & LLS ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 KEY EXCHANGE AUTHENTICATION PROTOCOL FOR NFS ENABLED HDFS CLIENT 1NAWAB MUHAMMAD FASEEH QURESHI, 2*DONG RYEOL SHIN, 3ISMA FARAH SIDDIQUI 1,2 Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea 3 Department of Software Engineering, Mehran UET, Pakistan *Corresponding Author E-mail: [email protected], 2*[email protected], [email protected] ABSTRACT By virtue of its built-in processing capabilities for large datasets, Hadoop ecosystem has been utilized to solve many critical problems. The ecosystem consists of three components; client, Namenode and Datanode, where client is a user component that requests cluster operations through Namenode and processes data blocks at Datanode enabled with Hadoop Distributed File System (HDFS). Recently, HDFS has launched an add-on to connect a client through Network File System (NFS) that can upload and download set of data blocks over Hadoop cluster. In this way, a client is not considered as part of the HDFS and could perform read and write operations through a contrast file system. This HDFS NFS gateway has raised many security concerns, most particularly; no reliable authentication support of upload and download of data blocks, no local and remote client efficient connectivity, and HDFS mounting durability issues through untrusted connectivity. To stabilize the NFS gateway strategy, we present in this paper a Key Exchange Authentication Protocol (KEAP) between NFS enabled client and HDFS NFS gateway. The proposed approach provides cryptographic assurance of authentication between clients and gateway. -
HFAA: a Generic Socket API for Hadoop File Systems
HFAA: A Generic Socket API for Hadoop File Systems Adam Yee Jeffrey Shafer University of the Pacific University of the Pacific Stockton, CA Stockton, CA [email protected] jshafer@pacific.edu ABSTRACT vices: one central NameNode and many DataNodes. The Hadoop is an open-source implementation of the MapReduce NameNode is responsible for maintaining the HDFS direc- programming model for distributed computing. Hadoop na- tory tree. Clients contact the NameNode in order to perform tively integrates with the Hadoop Distributed File System common file system operations, such as open, close, rename, (HDFS), a user-level file system. In this paper, we intro- and delete. The NameNode does not store HDFS data itself, duce the Hadoop Filesystem Agnostic API (HFAA) to allow but rather maintains a mapping between HDFS file name, Hadoop to integrate with any distributed file system over a list of blocks in the file, and the DataNode(s) on which TCP sockets. With this API, HDFS can be replaced by dis- those blocks are stored. tributed file systems such as PVFS, Ceph, Lustre, or others, thereby allowing direct comparisons in terms of performance Although HDFS stores file data in a distributed fashion, and scalability. Unlike previous attempts at augmenting file metadata is stored in the centralized NameNode service. Hadoop with new file systems, the socket API presented here While sufficient for small-scale clusters, this design prevents eliminates the need to customize Hadoop’s Java implementa- Hadoop from scaling beyond the resources of a single Name- tion, and instead moves the implementation responsibilities Node. Prior analysis of CPU and memory requirements for to the file system itself. -
Extended Version
Sina Sheikholeslami C u rriculum V it a e ( Last U pdated N ovember 2 0 18) Website: http://sinash.ir Present Address : https://www.kth.se/profile/sinash EIT Digital Stockholm CLC , https://linkedin.com/in/sinasheikholeslami Isafjordsgatan 26, Email: si [email protected] 164 40 Kista (Stockholm), [email protected] Sweden [email protected] Educational Background: • M.Sc. Student of Data Science, Eindhoven University of Technology & KTH Royal Institute of Technology, Under EIT-Digital Master School. 2017-Present. • B.Sc. in Computer Software Engineering, Department of Computer Engineering and Information Technology, Amirkabir University of Technology (Tehran Polytechnic). 2011-2016. • Mirza Koochak Khan Pre-College in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.61/20. 2010-2011. • Mirza Koochak Khan Highschool in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.17/20, Final Year's GPA: 19.66/20. 2007-2010. Research Fields of Interest: • Distributed Deep Learning, Hyperparameter Optimization, AutoML, Data Intensive Computing Bachelor's Thesis: • “SDMiner: A Tool for Mining Data Streams on Top of Apache Spark”, Under supervision of Dr. Amir H. Payberah (Defended on June 29th 2016). Computer Skills: • Programming Languages & Markups: o F luent in Java, Python, Scala, JavaS cript, C/C++, A ndroid Pr ogram Develop ment o Familia r wit h R, SAS, SQL , Nod e.js, An gula rJS, HTM L, JSP • -
Decentralising Big Data Processing Scott Ross Brisbane
School of Computer Science and Engineering Faculty of Engineering The University of New South Wales Decentralising Big Data Processing by Scott Ross Brisbane Thesis submitted as a requirement for the degree of Bachelor of Engineering (Software) Submitted: October 2016 Student ID: z3459393 Supervisor: Dr. Xiwei Xu Topic ID: 3692 Decentralising Big Data Processing Scott Ross Brisbane Abstract Big data processing and analysis is becoming an increasingly important part of modern society as corporations and government organisations seek to draw insight from the vast amount of data they are storing. The traditional approach to such data processing is to use the popular Hadoop framework which uses HDFS (Hadoop Distributed File System) to store and stream data to analytics applications written in the MapReduce model. As organisations seek to share data and results with third parties, HDFS remains inadequate for such tasks in many ways. This work looks at replacing HDFS with a decentralised data store that is better suited to sharing data between organisations. The best fit for such a replacement is chosen to be the decentralised hypermedia distribution protocol IPFS (Interplanetary File System), that is built around the aim of connecting all peers in it's network with the same set of content addressed files. ii Scott Ross Brisbane Decentralising Big Data Processing Abbreviations API Application Programming Interface AWS Amazon Web Services CLI Command Line Interface DHT Distributed Hash Table DNS Domain Name System EC2 Elastic Compute Cloud FTP File Transfer Protocol HDFS Hadoop Distributed File System HPC High-Performance Computing IPFS InterPlanetary File System IPNS InterPlanetary Naming System SFTP Secure File Transfer Protocol UI User Interface iii Decentralising Big Data Processing Scott Ross Brisbane Contents 1 Introduction 1 2 Background 3 2.1 The Hadoop Distributed File System . -
Poweredge R640 Apache Hadoop
A Principled Technologies report: Hands-on testing. Real-world results. The science behind the report: Run compute-intensive Apache Hadoop big data workloads faster with Dell EMC PowerEdge R640 servers This document describes what we tested, how we tested, and what we found. To learn how these facts translate into real-world benefits, read the report Run compute-intensive Apache Hadoop big data workloads faster with Dell EMC PowerEdge R640 servers. We concluded our hands-on testing on October 27, 2019. During testing, we determined the appropriate hardware and software configurations and applied updates as they became available. The results in this report reflect configurations that we finalized on October 15, 2019 or earlier. Unavoidably, these configurations may not represent the latest versions available when this report appears. Our results The table below presents the throughput each solution delivered when running the HiBench workloads. Dell EMC™ PowerEdge™ R640 Dell EMC PowerEdge R630 Percentage more throughput solution solution Latent Dirichlet Allocation 4.13 1.94 112% (MB/sec) Random Forest (MB/sec) 100.66 94.43 6% WordCount (GB/sec) 5.10 3.45 47% The table below presents the minutes each solution needed to complete the HiBench workloads. Dell EMC PowerEdge R640 Dell EMC PowerEdge R630 Percentage less time solution solution Latent Dirichlet Allocation 17.11 36.25 52% Random Forest 5.55 5.92 6% WordCount 4.95 7.32 32% Run compute-intensive Apache Hadoop big data workloads faster with Dell EMC PowerEdge R640 servers November 2019 System configuration information The table below presents detailed information on the systems we tested. -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo........................................................................................................................................................... -
TR-4744: Secure Hadoop Using Apache Ranger with Netapp In
Technical Report Secure Hadoop using Apache Ranger with NetApp In-Place Analytics Module Deployment Guide Karthikeyan Nagalingam, NetApp February 2019 | TR-4744 Abstract This document introduces the NetApp® In-Place Analytics Module for Apache Hadoop and Spark with Ranger. The topics covered in this report include the Ranger configuration, underlying architecture, integration with Hadoop, and benefits of Ranger with NetApp In-Place Analytics Module using Hadoop with NetApp ONTAP® data management software. TABLE OF CONTENTS 1 Introduction ........................................................................................................................................... 4 1.1 Overview .........................................................................................................................................................4 1.2 Deployment Options .......................................................................................................................................5 1.3 NetApp In-Place Analytics Module 3.0.1 Features ..........................................................................................5 2 Ranger ................................................................................................................................................... 6 2.1 Components Validated with Ranger ................................................................................................................6 3 NetApp In-Place Analytics Module Design with Ranger.................................................................. -
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility July 2017 © 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Contents Introduction 1 Amazon S3 as the Data Lake Storage Platform 2 Data Ingestion Methods 3 Amazon Kinesis Firehose 4 AWS Snowball 5 AWS Storage Gateway 5 Data Cataloging 6 Comprehensive Data Catalog 6 HCatalog with AWS Glue 7 Securing, Protecting, and Managing Data 8 Access Policy Options and AWS IAM 9 Data Encryption with Amazon S3 and AWS KMS 10 Protecting Data with Amazon S3 11 Managing Data with Object Tagging 12 Monitoring and Optimizing the Data Lake Environment 13 Data Lake Monitoring 13 Data Lake Optimization 15 Transforming Data Assets 18 In-Place Querying 19 Amazon Athena 20 Amazon Redshift Spectrum 20 The Broader Analytics Portfolio 21 Amazon EMR 21 Amazon Machine Learning 22 Amazon QuickSight 22 Amazon Rekognition 23 Future Proofing the Data Lake 23 Contributors 24 Document Revisions 24 Abstract Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace.