Amazon Redshift and the Case for Simpler Data Warehouses
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Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26 -
[XIAY]⋙ Big Data Science & Analytics: a Hands-On Approach By
Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti Click here if your download doesn"t start automatically Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti We are living in the dawn of what has been termed as the "Fourth Industrial Revolution", which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. -
Data Warehousing on AWS
Data Warehousing on AWS March 2016 Amazon Web Services – Data Warehousing on AWS March 2016 © 2016, 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. Page 2 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Contents Abstract 4 Introduction 4 Modern Analytics and Data Warehousing Architecture 6 Analytics Architecture 6 Data Warehouse Technology Options 12 Row-Oriented Databases 12 Column-Oriented Databases 13 Massively Parallel Processing Architectures 15 Amazon Redshift Deep Dive 15 Performance 15 Durability and Availability 16 Scalability and Elasticity 16 Interfaces 17 Security 17 Cost Model 18 Ideal Usage Patterns 18 Anti-Patterns 19 Migrating to Amazon Redshift 20 One-Step Migration 20 Two-Step Migration 20 Tools for Database Migration 21 Designing Data Warehousing Workflows 21 Conclusion 24 Further Reading 25 Page 3 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Abstract Data engineers, data analysts, and developers in enterprises across the globe are looking to migrate data warehousing to the cloud to increase performance and lower costs. -
Amazon Documentdb Deep Dive
DAT326 Amazon DocumentDB deep dive Joseph Idziorek Antra Grover Principal Product Manager Software Development Engineer Amazon Web Services Fulfillment By Amazon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is the purpose of a document database? What customer problems does Amazon DocumentDB (with MongoDB compatibility) solve and how? Customer use case and learnings: Fulfillment by Amazon What did we deliver for customers this year? What’s next? © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Purpose-built databases Relational Key value Document In-memory Graph Search Time series Ledger Why document databases? Denormalized data Normalized data model model { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? GET https://api.yelp.com/v3/businesses/{id} { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? response = yelp_api.search_query(term='ice cream', location='austin, tx', sort_by='rating', limit=5) Why document databases? for i in response['businesses']: col.insert_one(i) db.businesses.aggregate([ { $group: { _id: "$price", ratingAvg: { $avg: "$rating"}} } ]) db.businesses.find({ -
Magic Quadrant for Data Warehouse Database Management Systems
Magic Quadrant for Data Warehouse Database Management Systems Gartner RAS Core Research Note G00209623, Donald Feinberg, Mark A. Beyer, 28 January 2011, RV5A102012012 The data warehouse DBMS market is undergoing a transformation, including many acquisitions, as vendors adapt data warehouses to support the modern business intelligence and analytic workload requirements of users. This document compares 16 vendors to help you find the right one for your needs. WHAT YOU NEED TO KNOW Despite a troubled economic environment, the data warehouse database management system (DBMS) market returned to growth in 2010, with smaller vendors gaining in acceptance. As predicted in the previous iteration of this Magic Quadrant, 2010 brought major acquisitions, and several of the smaller vendors, such as Aster Data, Ingres and Vertica, took major strides by addressing specific market needs. The year also brought major market growth from data warehouse appliance offerings (see Note 1), with both EMC/Greenplum and Microsoft formally introducing appliances, and IBM, Oracle and Teradata broadening their appliance lines with new offerings. Although we believe that much of the growth was due to replacements of aging or performance-constrained data warehouse environments, we also think that the business value of using data warehouses for new applications such as performance management and advanced analytics has driven — and is driving — growth. All the vendors have stepped up their marketing efforts as the competition has grown. End-user organizations should ignore marketing claims about the applicability and performance capabilities of solutions. Instead, they should base their decisions on customer references and proofs of concept (POCs) to ensure that vendors’ claims will hold up in their environments. -
Planning and Designing Databases on AWS (AWS-PD-DB)
Planning and Designing Databases on AWS (AWS-PD-DB) COURSE OVERVIEW: In this course, you will learn about the process of planning and designing both relational and nonrelational databases. You will learn the design considerations for hosting databases on Amazon Elastic Compute Cloud (Amazon EC2). You will learn about our relational database services including Amazon Relational Database Service (Amazon RDS), Amazon Aurora, and Amazon Redshift. You will also learn about our nonrelational database services including Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. By the end of this course, you will be familiar with the planning and design requirements of all 8 of these AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. WHO WILL BENEFIT FROM THIS COURSE? • Data platform engineers • Database administrators • Solutions architects • IT professionals PREREQUISITES: We recommend that attendees of this course have previously completed the following AWS courses: • AWS Database Offerings digital training • Data Analytics Fundamentals digital training • Architecting on AWS classroom training COURSE OBJECTIVES: After completion of this course, students will be able to... • Apply database concepts, database management, and data modeling techniques • Evaluate hosting databases on Amazon EC2 instances • Evaluate relational database services (Amazon RDS, Amazon Aurora, and Amazon Redshift) and their features • Evaluate nonrelational database services -
Government Contracting M&A Update
Government Contracting M&A Update “Market Intelligence for Business Owners” Q3 2013 Capstone Partners Investment Banking Advisors BOSTON | CHICAGO | LONDON | LOS ANGELES | PHILADELPHIA | SAN DIEGO | SILICON VALLEY Government Contracting Coverage Report MERGERS & ACQUISITIONS UPDATE With the nation’s attention focused on reducing government spending and sequestration, one would expect mergers & acquisitions in the government contracting space to come CAPSTONE PARTNERS LLC to a standstill. But such is not the case, with the number of acquisitions announced 200 South Wacker Drive through June totaling more than 250. 31st Floor Chicago, IL 60606 M&A Activity: Government Contractors www.capstonellc.com 1000 964 900 852 800 786 772 786 732 751 700 568 Ted Polk 600 521 Transactions Managing Director 500 of 398 (312) 674‐4531 400 [email protected] 300 256 Number 200 100 Lisa Tolliver 0 Director 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 YTD (312) 674‐4532 2013 [email protected] YTD 2013 through June 30, 2013 Source: Capital IQ, Capstone Partners LLC research While the year’s activity is currently on‐track to come in under the 2012 figure, that trend is reflective of what we are seeing in mergers and acquisitions in general. M&A activity across the board has been down in early 2013 compared to 2012, primarily the result of the market continuing to absorb the rash of transactions that were closed at the end of 2012 in anticipation of rising capital gains tax rates. But, while the number of closed transactions has slowed this year, M&A activity continues to be supported by strong market fundamentals, namely reasonably high transaction valuations; strategic acquirers with strong balance sheets; abundant private equity capital; an accessible and affordable debt market; and a modestly expanding U.S. -
Lyft Goes All-In on AWS | Business Wire
5/6/2019 Lyft Goes All-In on AWS | Business Wire Lyft Goes All-In on AWS Lyft uses the breadth of functionality and highly reliable infrastructure of AWS to enhance rider experience and accelerate self-driving technologies in the cloud February 26, 2019 09:00 AM Eastern Standard Time SEATTLE--(BUSINESS WIRE)--Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced that Lyft, Inc. is going all-in on the world’s leading cloud—to enhance Lyft’s ridesharing marketplace which is available to an estimated 95 percent of the U.S. population, as well as in select Canadian cities, drive growth of its bike and scooters businesses, and enable its self-driving technology. Since its inception, Lyft has leveraged AWS’s unmatched performance and scalability to power its on-demand transportation platform, which facilitates more than 50 million rides a month. Lyft runs its operations, which provides a multimodal platform for drivers and riders in the U.S. and Canada, as well as backend platform systems, financial applications, and website on AWS. The company is leveraging the breadth and depth of AWS’s services, including database, serverless, machine learning, and analytics, to automate and enhance on- demand, multimodal transportation for riders and drive innovation in its autonomous vehicles business. Running on AWS’s fault-tolerant, highly performant infrastructure, helps support Lyft’s everyday business, and scales easily for peak periods, where demand can skyrocket on weekends and holidays. In addition, Lyft relies on Amazon DynamoDB, a database that delivers high performance at scale, to support its mission-critical workloads, including a ride- tracking system that enables the company to provide more precise vehicle routing. -
All Services Compute Developer Tools Machine Learning Mobile
AlL services X-Ray Storage Gateway Compute Rekognition d Satellite Developer Tools Amazon Sumerian Athena Machine Learning Elastic Beanstalk AWS Backup Mobile Amazon Transcribe Ground Station EC2 Servertess Application EMR Repository Codestar CloudSearch Robotics Amazon SageMaker Customer Engagement Amazon Transtate AWS Amplify Amazon Connect Application Integration Lightsail Database AWS RoboMaker CodeCommit Management & Governance Amazon Personalize Amazon Comprehend Elasticsearch Service Storage Mobile Hub RDS Amazon Forecast ECR AWS Organizations Step Functions CodeBuild Kinesis Amazon EventBridge AWS Deeplens Pinpoint S3 AWS AppSync DynamoDe Amazon Textract ECS CloudWatch Blockchain CodeDeploy Quicksight EFS Amazon Lex Simple Email Service AWS DeepRacer Device Farm ElastiCache Amazont EKS AWS Auto Scaling Amazon Managed Blockchain CodePipeline Data Pipeline Simple Notification Service Machine Learning Neptune FSx Lambda CloudFormation Analytics Cloud9 AWS Glue Simple Queue Service Amazon Polly Business Applications $3 Glacer AR & VR Amazon Redshift SWF Batch CloudTrail AWS Lake Formation Server Migration Service lot Device Defender Alexa for Business GuardDuty MediaConnect Amazon QLDB WorkLink WAF & Shield Config AWS Well. Architected Tool Route 53 MSK AWS Transfer for SFTP Artifact Amazon Chime Inspector lot Device Management Amazon DocumentDB Personal Health Dashboard C MediaConvert OpsWorks Snowball API Gateway WorkMait Amazon Macie MediaLive Service Catalog AWS Chatbot Security Hub Security, Identity, & Internet of Things loT -
Nexus User Guide (Pdf)
The Best Query Tool Works on all Systems When you possess a tool like Nexus, you have access to every system in your enterprise! The Nexus Query Chameleon is the only tool that works on all systems. Its Super Join Builder allows for the ERwin Logical Model to be loaded, and then Nexus shows tables and views visually. It then guides users to show what joins to what. As users choose the tables and columns they want in their report, Nexus builds the SQL for them with each click of the mouse. Nexus was designed for Teradata and Hadoop, but works on all platforms. Nexus even converts table structures between vendors, so querying and managing multi-vendor platforms is transparent. Even if you only work with one system, you will find that the Nexus is the best query tool you have ever used. If you work with multiple systems, you will be even more amazed. Download a free trial at www.CoffingDW.com. The Tera-Tom Video Series Lessons with Tera-Tom Teradata Architecture and SQL Video Series These exciting videos make learning and certification much easier Four ways to view them: 1. Safari (look up Coffing Studios) 2. CoffingDW.com (sign-up on our website) 3. Your company can buy them all for everyone to see (contact [email protected]) 4. YouTube – Search for CoffingDW or Tera-Tom. The Tera-Tom Genius Series The Tera-Tom Genius Series consists of ten books. Each book is designed for a specific audience, and Teradata is explained to the level best suited for that audience. -
The Directory of Experimentation and Personalisation Platforms Contents I
The Directory of Experimentation and Personalisation Platforms Contents i Why We Wrote This e-book ..................................................................................................... 3 A Note on the Classification and Inclusion of Platforms in This e-book ........................................ 4 Platform Capabilities in a Nutshell ............................................................................................ 5 Industry Leaders AB Tasty: In-depth Testing, Personalisation, Nudge Engagement, and Product Optimisation ............10 Adobe Target: Omnichannel Testing and AI-based Personalisation ...............................................15 Dynamic Yield: Omnichannel Testing, AI-Based Personalisation, and Data Management .................19 Google Optimize: In-depth Testing, Personalisation, and Analytics .............................................. 24 Monetate: Omnichannel Optimisation Intelligence, Testing, and Personalisation ........................... 27 Optimizely: Experimentation, Personalisation, and Feature-flagging .............................................31 Oracle Maxymiser: User Research, Testing, Personalisation, and Data Management ...................... 38 Qubit: Experimentation and AI-driven Personalisation for e-commerce ......................................... 43 Symplify: Omnichannel Communication and Conversion Suites .................................................. 47 VWO: Experience Optimisation and Growth ..............................................................................51 -
Modernize Your Data Warehouse
Modernize your data warehouse Isabel Huerga Ayza Senior Developer Advocate © 2020, Amazon Web Services, Inc. or its Affiliates. Agenda • Cloud data warehouses • Amazon Redshift architecture and features • Accelerating your data warehouse migration • Demo © 2020, Amazon Web Services, Inc. or its Affiliates. Benefits of a cloud data warehouse Get insights Scale, elasticity, Increases in No infrastructure from all your data and flexibility productivity costs and pay-as-you-go © 2020, Amazon Web Services, Inc. or its Affiliates. © 2020, Amazon Web Services, Inc. or its Affiliates. JustGiving Supports 24 Million Users on Charity Site Using AWS • Needed a new platform to support general “ Using the new AWS tools, we can operations and new analytics service extract much finer-grained data points • Moved to AWS, using a wide range of services based on millions of donations and • Can scale system faster in response to billions of visits, and then use that unanticipated spikes in traffic information to provide a better platform • Receives query results in seconds compared to 30 for our visitors. minutes under old system Richard Atkinson Chief Information Officer, JustGiving • Obtains deeper insights into billions of data points, using information to deliver better services JustGiving is a major online platform that supports charitable giving. The organization is based in London.” © 2020, Amazon Web Services, Inc. or its Affiliates. Amazon Redshift Benefits Integrated catalog & security Massively parallel processing Usage-based pricing Exabyte data lake querying Columnar data storage Predictable costs Result caching Virtually unlimited AWS-grade security Easy to provision & manage elastic linear scaling Certifications such as SOC, PCI, DSS, ISO, Automated administrative tasks FedRRAMP, HIPAA © 2020, Amazon Web Services, Inc.