100424 teradata cloud computing 3rd party influencers2c
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The document discusses Teradata's offerings for cloud computing environments. It outlines Teradata Express editions for Amazon EC2 and VMware that allow development, testing, and proof-of-concept workloads in public and private clouds. It also describes the Teradata Agile Analytics Cloud for self-service provisioning of sandboxes and data marts within a private cloud using Teradata databases. Customers benefit from the agility, capacity, and cost advantages of clouds for various analytics workloads.
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Hadoop in the Cloud: Common Architectural Patterns
The document discusses how companies are using Microsoft Azure services like HDInsight, Data Factory, Machine Learning, and others to gain insights from large volumes of data. Specifically, it provides examples of:
1) A large computer manufacturer/retailer analyzing clickstream data with HDInsight to understand customer behavior and provide real-time recommendations to increase online conversions.
2) An industrial automation company partnering with an oil company to use IoT sensors and analytics to monitor LNG fueling stations for proactive maintenance based on sensor data analyzed with HDInsight, Data Factory, and Machine Learning.
3) How data from various industries like retail, oil and gas, manufacturing, and others can be analyzed
Cloud Computing: Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
NYC Open Data Meetup-- Thoughtworks chief data scientist talk
This document summarizes a presentation on data science consulting. It discusses:
1) The Agile Analytics group at ThoughtWorks which does data science consulting projects using probabilistic modeling, machine learning, and big data technologies.
2) Two case studies are described, including developing a machine learning model to improve matching of healthcare product data and using logistic regression for retail recommendation systems.
3) The origins and future of the field are discussed, noting that while not entirely new, data science has grown due to improvements in technology, programming languages, and libraries that have increased productivity and driven new career opportunities in the field.
The Intelligent Thing -- Using In-Memory for Big Data and Beyond
The Briefing Room with John O'Brien and Teradata
Live Webcast on June 11, 2013
http://www.insideanalysis.com
For traditional Data Warehousing and Big Data Analytics, research shows that a small percentage of enterprise data often comprises the lion's share of what's needed for queries. That's hot data, and organizations that know how to effectively harness that data can stay on top of what's happening. Conversely, cold data can certainly provide value at times, but should ideally be stored in ways that minimize cost. The more dynamically a company can manage this hot and cold data, the more efficient its information systems become.
Register for this episode of The Briefing Room to hear veteran database expert John O'Brien of Radiant Advisors as he outlines a strategy for managing hot and cold data. He'll be briefed by Alan Greenspan of Teradata, who will tout his company's Intelligent In-Memory solution, which optimizes the management of hot and cold data to keep analysts fueled with the data they need most. He'll also discuss Teradata Virtual Storage, which helps optimize the storage and provisioning of information assets.
Building your data warehouse with Redshift
Amazon Redshift is a data warehouse service that runs on AWS. It has a leader node that coordinates queries and compute nodes that store and process the data in parallel. The compute nodes can use either HDD storage optimized for large datasets or SSD storage optimized for fast queries. Data is stored in columns and compressed to reduce I/O. Queries are optimized using statistics on the data distribution, sort keys and other metadata. The EXPLAIN command and STL tables provide visibility into query plans and performance.
Teradata Intelligent Memory
In this presentation, Scott Gnau from Teradata Labs presents: Teradata Intelligent Memory.
<blockquote>The introduction of Teradata Intelligent Memory allows our customers to exploit the performance of memory within Teradata Platforms, which extends our leadership position as the best performing data warehouse technology at the most competitive price,” said Scott Gnau, president, Teradata Labs. “Teradata Intelligent Memory technology is built into the data warehouse and customers don’t have to buy a separate appliance. Additionally, Teradata enables its customers to buy and configure the exact amount of in-memory capability needed for critical workloads. It is unnecessary and impractical to keep all data in memory, because all data do not have the same value to justify being placed in expensive memory.” </blockquote>
Teradata Aggregate Join Indices And Dimensional Models
The document discusses using aggregate join indices and dimensional models in Teradata to improve query performance for reporting and analytics workloads while maintaining a normalized 3NF data model. It provides an example comparing querying sales data from the past year versus the current year using the 3NF model versus a dimensional model with and without aggregate join indices. Using the dimensional model and join indices reduced the data volume accessed, eliminated table joins, and improved performance metrics like CPU usage, disk I/O, and elapsed time. Maintaining both models allows enjoying benefits of each while technology like join indices provides dimensional access at different granularities with low overhead.
Understanding System Performance
- The document discusses understanding system performance and knowing when it's time for a system tune-up. It covers monitoring tools like DBQL and Viewpoint, establishing performance baselines, using real-time alerts, and examining growth patterns.
- It emphasizes the importance of regular benchmarks to compare performance over time, especially before and after upgrades. Successful benchmarks require consistency in data, queries, indexing, and concurrency levels.
- The document outlines various aspects of performance tuning like query tuning, load techniques, compression, and utilizing new database features. It stresses automating processes and educating developers on database technologies.
Teradata memory management - A balancing act
The document discusses memory management in Teradata systems. It explains that memory is partitioned between operating system (OS) managed memory and File Segment Group (FSG) cache. The FSG cache percentage can be adjusted to control how much memory is allocated to each. Other topics covered include monitoring memory usage, tuning the FSG cache threshold, understanding hash join memory usage, and sizing redistribution buffers. The presentation provides guidance on diagnosing and addressing memory depletion issues through tools like adjusting configuration parameters and disabling memory intensive features if needed.
ABC of Teradata System Performance Analysis
The document discusses analyzing and optimizing the performance of a Teradata system. It covers generating reports on CPU utilization, memory usage, disk I/O, and parallel efficiency across nodes and processors. The reports can identify issues such as CPU bottlenecks, memory depletion, disk contention, and load imbalances that impact performance. Addressing problems revealed in the reports, such as through query optimization or system reconfiguration, can improve the overall parallel efficiency and throughput of the Teradata platform.
Hadoop and the Data Warehouse: When to Use Which
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
Getting Maximum Performance from Amazon Redshift (DAT305) | AWS re:Invent 2013
Get the most out of Amazon Redshift by learning about cutting-edge data warehousing implementations. Desk.com, a Salesforce.com company, discusses how they maintain a large concurrent user base on their customer-facing business intelligence portal powered by Amazon Redshift. HasOffers shares how they load 60 million events per day into Amazon Redshift with a 3-minute end-to-end load latency to support ad performance tracking for thousands of affiliate networks. Finally, Aggregate Knowledge discusses how they perform complex queries at scale with Amazon Redshift to support their media intelligence platform.
Migration to Redshift from SQL Server
RealityMine collects digital user behavior data to help companies with marketing, product development, and analyzing user patterns. They are migrating from an on-premise SQL Server data warehouse to Amazon Redshift to handle doubling data volumes. Redshift provides better performance and scalability at lower cost compared to other options. It requires extracting raw data from SQL Server without encoding issues, loading to S3, and transforming in Redshift using a star schema with careful consideration of distribution and sort keys for query performance. Ongoing database maintenance and backups are also different in Redshift.
Teradata - Presentation at Hortonworks Booth - Strata 2014
Hortonworks and Teradata have partnered to provide a clear path to Big Analytics via stable and reliable Hadoop for the enterprise. The Teradata® Portfolio for Hadoop is a flexible offering of products and services for customers to integrate Hadoop into their data architecture while taking advantage of the world-class service and support Teradata provides.
A Reference Architecture for ETL 2.0
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
AWS re:Invent 2016: Metering Big Data at AWS: From 0 to 100 Million Records i...
Learn how AWS processes millions of records per second to support accurate metering across AWS and our customers. This session shows how we migrated from traditional frameworks to AWS managed services to support a large processing pipeline. You will gain insights on how we used AWS services to build a reliable, scalable, and fast processing system using Amazon Kinesis, Amazon S3, and Amazon EMR. Along the way we dive deep into use cases that deal with scaling and accuracy constraints. Attend this session to see AWS’s end-to-end solution that supports metering at AWS.
Large scale ETL with Hadoop
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
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(ISM303) Migrating Your Enterprise Data Warehouse To Amazon Redshift
(ISM303) Migrating Your Enterprise Data Warehouse To Amazon Redshift
Hadoop in the Cloud: Common Architectural Patterns
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NYC Open Data Meetup-- Thoughtworks chief data scientist talk
NYC Open Data Meetup-- Thoughtworks chief data scientist talk
The Intelligent Thing -- Using In-Memory for Big Data and Beyond
The Intelligent Thing -- Using In-Memory for Big Data and Beyond
Teradata Aggregate Join Indices And Dimensional Models
Teradata Aggregate Join Indices And Dimensional Models
Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop
Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop
Getting Maximum Performance from Amazon Redshift (DAT305) | AWS re:Invent 2013
Getting Maximum Performance from Amazon Redshift (DAT305) | AWS re:Invent 2013
Teradata - Presentation at Hortonworks Booth - Strata 2014
Teradata - Presentation at Hortonworks Booth - Strata 2014
AWS re:Invent 2016: Metering Big Data at AWS: From 0 to 100 Million Records i...
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Cloud Computing and your Data Warehouse
This document provides an overview of cloud computing. It defines cloud computing as "a mechanism for delivering scalable business services that execute on a decentralized computing fabric composed of commodity software and hardware." The document discusses why organizations use cloud computing, including that it allows for faster infrastructure on demand, lower costs by moving to an operational expenditure model rather than capital expenditure, and the ability to focus on core business needs by making infrastructure "someone else's problem." It also outlines some of the major players in cloud computing and different cloud computing models including private, public, hybrid and community clouds. The document discusses concepts like cloud stratification, APIs, and virtualization. It provides examples of how Teradata delivers analytics capabilities in private, public and hybrid
ClickHouse on Plug-n-Play Cloud, by Som Sikdar, Kodiak Data
Kodiak provides a private cloud solution called MemCloud that offers faster performance and lower costs compared to public clouds like AWS. MemCloud can be deployed on-premises or at the edge to power analytics, big data, and AI/ML workloads. Benchmarks show the Kodiak solution is up to 5x faster than AWS for similar configurations. It also reduces the complexity, costs, and maintenance challenges of building and tuning physical data lake clusters that combine different software like HDFS, Kafka, Spark and ClickHouse.
Securing your Big Data Environments in the Cloud
Big Data tools are becoming a critical part of enterprise architectures and as such securing the data, at rest, and in motion is a necessity. More so, when you’re implementing these solutions in the cloud and the data doesn't reside within the confines of your trusted data center. Also, there is a fine balance between implementing enterprise-grade security and negotiating utmost performance given the overheads of encryption and/or identity management.
This session is designed to tackle these challenges head on and explain the various options available in the cloud. The focal points are the implementation of tools like Ranger and Knox for cloud deployments, but we also pay attention to the security features offered in the cloud that complement this process and secure the data in unprecedented ways.
Cloud Security + OSS Security tools are a deadly combination, when it comes to securing your Data Lake.
How to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
Cloud Computing
Oracle's cloud computing strategy is to support both public and private clouds to give customers choice. Oracle offers the technology to build private clouds or run workloads in public clouds. It also offers applications deployed in private shared services environments or via public SaaS. The strategy is based on Oracle's existing virtualization, grid computing, shared services, and management technologies and provides customers the most complete, open, and integrated cloud vision and offerings.
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Hadoop has made it into the enterprise mainstream as Big Data technology. But, what about Hadoop as a private or public cloud service on a shared infrastructure? This session looks at a Hadoop solution with virtualization, shared storage, and multi-tenancy, and discuss how service providers can use Pivotal Hadoop Distribution, Isilon, and Serengeti to offer Hadoop-as-a-Service.
Objective 1: Understand Hadoop and its deployment challenges.
After this session you will be able to:
Objective 2: Understand the EMC HDaaS solution architecture and the use cases it addresses.
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Self-service Big Data Analytics on Microsoft Azure
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Cloud Native Applications on OpenShift
This document discusses cloud native development and DevOps using OpenShift Container Platform. It begins by defining cloud native as involving both application architecture and the development, deployment and management processes used. It then discusses how containers evolve application delivery and how container platforms are part of the DevOps tool kit. The document outlines the path to DevOps, emphasizing culture, automation and using the right platform. It also notes that DevOps and containers often go hand in hand, with many DevOps adopters using containers. The document then discusses various capabilities of OpenShift and how it supports cloud native development.
A deep dive into running data analytic workloads in the cloud
This document discusses running data analytic workloads in the cloud using Cloudera Altus. It introduces Altus, which provides a platform-as-a-service for analyzing and processing data at scale in public clouds. The document outlines Altus features like low cost per-hour pricing, end-user focus, and cloud-native deployment. It then describes hands-on examples using Altus Data Engineering for ETL and the Altus Analytic Database for exploration and analytics. Workload analytics capabilities are also introduced for troubleshooting and optimizing jobs.
Oracle GoldenGate Roadmap Oracle OpenWorld 2020
The document provides an agenda and overview for an Oracle event on GoldenGate. It discusses GoldenGate use cases, features for databases and big data, and streaming analytics. It also summarizes new capabilities in GoldenGate 19.1 like improved performance, security and manageability for microservices, and support for Oracle Database 19c. The document outlines Oracle's vision of GoldenGate as a real-time data fabric ecosystem enabling streaming, databases, cloud, and big data use cases.
Cloud Computing Integration Introduction
Cloud computing and Integration consists of hardware and software resources made available on the Internet as managed third-party services, in a pay-per-use model , offering scalability and close alignment to actual demand.
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
Private cloud with z enterprise
What can you do with a Private Cloud on System z? Many things. It is the premier hybrid computing manager.
Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT30...
The AWS suite of managed services for IoT enables companies to quickly and easily deploy devices to the edge and synchronize their industrial time-series data from multiple sites to the AWS Cloud, where advanced analytics and machine learning can generate valuable insights about their business. In this session, learn how EDF Renewables used AWS Greengrass, AWS IoT Core, AWS IoT Analytics, and AWS Lambda to facilitate the collection, aggregation, and quality assurance of operational data from solar installations. Hear how working with AWS Professional Services transformed its approach to product development, and learn what challenges and solutions came with choosing leading-edge services form AWS.
Amazon Elastic Map Reduce - Ian Meyers
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Cybera Summit
The document discusses smart IT choices for small businesses using cloud computing. It introduces Everett Toews and his background in operations and as a developer advocate. It then covers the key characteristics of cloud computing like on-demand self-service, broad network access, resource pooling, and rapid elasticity. The document also discusses different cloud models including SaaS, PaaS, and IaaS both public and private. It demonstrates WordPress and Magento deployments on Rackspace and discusses tradeoffs of ease of use, cost, scale, complexity and control for different business needs.
Cloudify Open PaaS Stack for DevOps
This presentation describes how Cloudify an Open PaaS Stack can be used for DevOps purposes enabling continuous deployment and smooth migration from development to production.
Delta Lake with Azure Databricks
Dustin Vannoy presented on using Delta Lake with Azure Databricks. He began with an introduction to Spark and Databricks, demonstrating how to set up a workspace. He then discussed limitations of Spark including lack of ACID compliance and small file problems. Delta Lake addresses these issues with transaction logs for ACID transactions, schema enforcement, automatic file compaction, and performance optimizations like time travel. The presentation included demos of Delta Lake capabilities like schema validation, merging, and querying past versions of data.
Roadmap to Enterprise Cloud Computing
As enterprise adoption of cloud computing accelerates, driven by compelling advantages of higher efficiency and lower costs, rapid deployment and elastic scalability, organizations must have a strategy and plan for moving to the cloud. How can organizations get started on the evolution to cloud computing? This presentation explores how enterprise IT can move toward a cloud computing model, building on a foundation of virtualization, engineered systems and management automation.
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ClickHouse on Plug-n-Play Cloud, by Som Sikdar, Kodiak Data
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https://bit.ly/Automation_Student_Kickstart
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The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Day 2 - Intro to UiPath Studio Fundamentals
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
High performance Serverless Java on AWS- GoTo Amsterdam 2024
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
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100424 teradata cloud computing 3rd party influencers2c
- 1. Teradata in the Clouds
- 4. Public and Private Clouds > 05/12/10 Private Cloud Virtualization SLAs Public Cloud Elasticity and no CAPEX Line of Business users Internet Company LAN SaaS
- 6. Clouds Use Any Commodity Hardware > 05/12/10 My momma always said, “ Clouds are like a box of chocolates. You never know what you’re gonna get.” Forrest Gump (1994)
- 8. Teradata in the Cloud
- 12. Adding Low Cost Public Clouds > 05/12/10 Data sources Developers Line of Business EBS Teradata System Bynet Bynet
- 13. VMware: Internal Private Clouds > 05/12/10 Racks of Intel Servers SANs/NAS LAN VMware images Teradata Express for VMware Developer
- 14. Teradata Agile Analytics Cloud
- 15. Teradata Agile Analytics Cloud An Internal Private Cloud > 05/12/10 Teradata Server and Storage Active Workload Management Data Warehouse base tables Sandboxes, marts, etc. Power Users and Developers Self service provisioning Upload Excel Capacity On demand Advanced Analytics
- 16. Self Service Provisioning: Viewpoint Portlets > 05/12/10 Free source code: http://downloads.teradata.com/download/viewpoint/elastic-marts-builder
- 17. Simple, Self Service Provisioning > 05/12/10 Verify Data Table Name Integer Date Char Decimal Elastic Marts POC Sand box Virtual mart Enterprise Data Warehouse xyz Col3 PI Type 123 abc Sample Col2 Col1 CSV Upload File Upload Password Myfile.csv File name User Name Elastic Mart Builder Create UserName MySpecialID Password Perm space 500MB Import 1 2 3 4 5
- 18. Teradata Agile Analytics Cloud > 05/12/10 ETL Enterprise Data Warehouse Teradata Server and Storage Teradata Active System Management Power Users Join Join Elastic mart XL-tbl Elastic mart XL-tbl
- 20. Agility and Innovation from Clouds > 05/12/10 Development Proof of Concept Production Concept Business User IT User Easy provisioning, Teradata Database throughout Agile Analytics Cloud Agile Analytics Cloud
- 21. RailInc Using Teradata Express for VMware > 05/12/10 “ We’ve been using Teradata Express for VMware as a sand box. Before we used to do all this on our production Teradata system. We’ve been testing the geospatial features in the sand box and using Teradata Manager and load utilities. We installed some of the geospatial tools on our laptops too. We use it to test multiple scenarios for new product ideas. The sand box runs on an HP DL385 with quad cores, 1TB storage, 32GB of memory and other VMs on the same machine too. We are running SAS with the sand box to process a lot of event data. Why is TD Express a great deal? Well, it’s free. It’s a good platform for innovation and experimentation . It’s also good for product demos which we don’t always want to do on the production system. ”
Editor's Notes
- Author: [email_address] Teradata Active Data Warehouse Marketing Don’t hesitate to contact Dan-as-a-Service (DaaS). He’s elastic. He’s on-demand. He’s pay-per-use. And he’s virtual!