The IBM Netezza Data Warehouse ApplianceIBM Sverige
Netezza - Ett enklare sätt till smart analys.
Denna presentation hölls på IBM Data Server Day den 22 maj i Stockholm av Jacques Milman, Datawarehouse Architecture Leader, IBM
Teradata Technology Leadership and InnovationTeradata
Teradata is the world's leader in data warehousing and integrated marketing management through its database software, data warehouse appliances, and enterprise analytics. For more information, visit teradata.com.
Teradata training index ,teradata course content,teradata dba course content,teradata training course content,teradata training material,teradata training syllabus,teradata course outline
Oracle Systems Overview
Engineered systems strategy and overview about exadata, exalitics, superCluster, Exalogic, Oracle virtual appliance, ZFS appliance
As the core SQL processing engine of the Greenplum Unified Analytics Platform, the Greenplum Database delivers Industry leading performance for Big Data Analytics while scaling linearly on massively parallel processing clusters of standard x86 servers. This session reviews the product's underlying architecture, identify key differentiation areas, go deep into the new features introduced in Greenplum Database Release 4.2, and discuss our plans for 2012.
The IBM Netezza Data Warehouse ApplianceIBM Sverige
Netezza - Ett enklare sätt till smart analys.
Denna presentation hölls på IBM Data Server Day den 22 maj i Stockholm av Jacques Milman, Datawarehouse Architecture Leader, IBM
Teradata Technology Leadership and InnovationTeradata
Teradata is the world's leader in data warehousing and integrated marketing management through its database software, data warehouse appliances, and enterprise analytics. For more information, visit teradata.com.
Teradata training index ,teradata course content,teradata dba course content,teradata training course content,teradata training material,teradata training syllabus,teradata course outline
Oracle Systems Overview
Engineered systems strategy and overview about exadata, exalitics, superCluster, Exalogic, Oracle virtual appliance, ZFS appliance
As the core SQL processing engine of the Greenplum Unified Analytics Platform, the Greenplum Database delivers Industry leading performance for Big Data Analytics while scaling linearly on massively parallel processing clusters of standard x86 servers. This session reviews the product's underlying architecture, identify key differentiation areas, go deep into the new features introduced in Greenplum Database Release 4.2, and discuss our plans for 2012.
Teradata - Presentation at Hortonworks Booth - Strata 2014Hortonworks
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.
Introduction to Teradata And How Teradata WorksBigClasses Com
Watch How Teradata works with Introduction to teradata ,How Teradata Visual Explain Works,teradata database and tools,teradata database model,teradata hardware and software architecture,teradata database security,teradata storage based on primary index
The Big Data Analytics Ecosystem at LinkedInrajappaiyer
LinkedIn has several data driven products that improve the experience of its users -- whether they are professionals or enterprises. Supporting this is a large ecosystem of systems and processes that provide data and insights in a timely manner to the products that are driven by it.
This talk provides an overview of the various components of this ecosystem which are:
- Hadoop
- Teradata
- Kafka
- Databus
- Camus
- Lumos
etc.
Teradata Aster: Big Data Discovery Made Easy
Brad Elo, VP, Aster Data, Teradata
ANALYTICS AND VISUALIZATION FOR THE FINANCIAL ENTERPRISE CONFERENCE
June 25, 2013 The Langham Hotel Boston, MA
Trending use cases have pointed out the complementary nature of Hadoop and existing data management systems—emphasizing the importance of leveraging SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing. Many vendors have provided interfaces between SQL systems and Hadoop but have not been able to semantically integrate these technologies while Hive, Pig and SQL processing islands proliferate. This session will discuss how Teradata is working with Hortonworks to optimize the use of Hadoop within the Teradata Analytical Ecosystem to ingest, store, and refine new data types, as well as exciting new developments to bridge the gap between Hadoop and SQL to unlock deeper insights from data in Hadoop. The use of Teradata Aster as a tightly integrated SQL-MapReduce® Discovery Platform for Hadoop environments will also be discussed.
Leveraging your hadoop cluster better - running performant code at scaleMichael Kopp
Somebody once said that hadoop is a way of running highly unperformant code at scale. In this talk I want to show how we can change that and make map reduce jobs more performant. I will show how to analyze them at scale and optimize the job itself, instead of just tinkering with hadoop options. The result is a much better utilized cluster and jobs that run in a fraction of the original time running performant code at scale! Most of the time when speaking about Hadoop people only consider scale, however, when looking at it it very often runs highly unperformant jobs. By actually looking at the performance characteristics of the jobs themselves and optimizing and tuning those far better results can be achieved. Examples include small changes that cut jobs down from 15 hours to 2 hours without adding any more hardware. The concepts and techniques explained in the talk will be applicable regardless which tool is used to identify the performance characteristics, what is important is that by applying performance analysis and optimization techniques that we have used on other applications for a long time we can make hadoop jobs much more effective and performant! The attendees will be able to understand those techniques and apply them to their map/reduce/PIG/hive or other mapreduce jobs.
High Performance Big Data Loading for AWS: Deep Dive and Best Practices from ...Amazon Web Services
Companies are increasingly dealing with large data sets and looking for ways to increase the scale and lower the cost of Big Data analysis with AWS. In this interactive session, you’ll learn how to:
* Integrate massive data volumes, from any on-premises or cloud data sources into AWS with Informatica’s high performance cloud integration connectors and Vibe Secure Agent technology.
* Transform and load data into RDS, Redshift, and S3 without the need for coding.
* Automate streaming data collection into Kinesis with built-in high availability and failover features.
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...Data Con LA
As integrated web analytics evolves to both a service oriented and event based model, there will be higher emphasis on moving toward event based analytics. Business analytics is moving from purely counts of analytics to time-series, relationship and usage analytics. Examples of web analytics that can take advantage of this architecture are conversions analytics or cross channel marketing.
The advantage of storing raw event data is that you have maximum flexibility for analysis. For example, you can trace the sequence of pages that one person visited over the course of their session. You can’t do that if you’ve squashed all the events into e.g. counters. That sort of analysis is really important for some offline processing tasks, such as training a recommender system (“people who bought X also bought Y”, that sort of thing). For such use cases, it’s best to simply keep all the raw events, so that you can later feed them all into your shiny new machine learning system.
In this session we are going to elaborate on using Kafka, an Event Processing framework (e.g. Storm or Spark Streaming) and either Hadoop or EDW for building an Event Driven Architecture.
This is an MBA thesis, my aim is to understand the fascinating topic of Dig Data more thoroughly and to try to differentiate realities and myths about Big Data. At the same time, I’m hoping to suggest a practical framework that can be used by ambitious organizations to evaluate and guide their performance in terms of Big Data. Critical literature review about the topic, synthesizing inputs from subject matter experts and review successful implementation case studies in contemporary organizations were conducted to build up main pillars for this framework.
This presentation was given by SHEKHAR IYER, GENERAL MANAGER EMEA & AP at SAS High Performance Analytics Event held in India on 9th July 2012. It was an executive briefing on High-Performance Analytics (Big data Analytics) with Demo.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The Most Trusted In-Memory database in the world- AltibaseAltibase
Life is a database. How you manage data defines business. ALTIBASE HDB with its Hybrid architecture combines the extreme speed of an In-Memory Database with the storage capacity of an On-Disk Database’ in a single unified engine.
ALTIBASE® HDB™ is the only Hybrid DBMS in the industry that combines an in-memory DBMS with an on-disk DBMS, with a single uniform interface, enabling real-time access to large volumes of data, while simplifying and revolutionizing data processing. ALTIBASE XDB is the world’s fastest in-memory DBMS, featuring unprecedented high performance, and supports SQL-99 standard for wide applicability.
Altibase is provider of In-Memory data solutions for real-time access, analysis and distribution of high volumes of data in mission-critical environments.
Please visit our website (www.altibase.com) to learn more about our products and read more about our case studies. Or contact us at info@altibase.com. We look forward to helping you!
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
AWS re:Invent 2016: Billions of Rows Transformed in Record Time Using Matilli...Amazon Web Services
Billions of Rows Transformed in Record Time Using Matillion ETL for Amazon Redshift
GE Power & Water develops advanced technologies to help solve some of the world’s most complex challenges related to water availability and quality. They had amassed billions of rows of data on on-premises databases, but decided to migrate some of their core big data projects to the AWS Cloud. When they decided to transform and store it all in Amazon Redshift, they knew they needed an ETL/ELT tool that could handle this enormous amount of data and safely deliver it to its destination. In this session, Ryan Oates, Enterprise Architect at GE Water, shares his use case, requirements, outcomes and lessons learned. He also shares the details of his solution stack, including Amazon Redshift and Matillion ETL for Amazon Redshift in AWS Marketplace. You learn best practices on Amazon Redshift ETL supporting enterprise analytics and big data requirements, simply and at scale. You learn how to simplify data loading, transformation and orchestration on to Amazon Redshift and how build out a real data pipeline. Get the insights to deliver your big data project in record time.
Enterprise Data World 2018 - Building Cloud Self-Service Analytical SolutionDmitry Anoshin
This session will cover building the modern Data Warehouse by migration from the traditional DW platform into the cloud, using Amazon Redshift and Cloud ETL Matillion in order to provide Self-Service BI for the business audience. This topic will cover the technical migration path of DW with PL/SQL ETL to the Amazon Redshift via Matillion ETL, with a detailed comparison of modern ETL tools. Moreover, this talk will be focusing on working backward through the process, i.e. starting from the business audience and their needs that drive changes in the old DW. Finally, this talk will cover the idea of self-service BI, and the author will share a step-by-step plan for building an efficient self-service environment using modern BI platform Tableau.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back – and the roadblock that stops many promising machine learning projects. After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Syncsort software helps the data engineer every step of the way.
Building on the process of finding and matching duplicates to resolve entities, the next step is to set up a continuous streaming flow of data from data sources so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow – into the arms of machine learning models.
Some of your sources may already be streaming, but the rest are sitting in transactional databases that change hundreds or thousands of times a day. The challenge is that you can’t affect performance of data sources that run key applications, so putting something like database triggers in place is not the best idea. Using Apache Kafka or similar technologies as the backbone to moving data around doesn’t solve the problem of needing to grab changes from the source pushing them into Kafka and consuming the data from Kafka to be processed. If something unexpected happens – like connectivity is lost on either the source or the target side, you don’t want to have to fix it or start over because the data is out of sync.
View this 15-minute webcast on-demand to learn how to tackle these challenges in large scale production implementations.
Enterprise Architecture in the Era of Big Data and Quantum ComputingKnowledgent
Deck from April 2014 Big Data Palooza Meetup sponsored by Knowledgent. Enterprise Architect James Luisi spoke
Summary: Several characteristics identify the presence of big data. Invariably as new use cases emerge, new products emerge to address them. At this point, there are so many use cases, and so many products, that frameworks to organize and manage them are necessary. A couple of examples of useful frameworks to manage and organize include families of use cases and architectural disciplines.
Vibrant Technologies is headquarted in Mumbai,India.We are the best Business Analyst training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Business Analyst classes in Mumbai according to our students and corporators
This presentation is about -
History of ITIL,
ITIL Qualification scheme,
Introduction to ITIL,
For more details visit -
http://vibranttechnologies.co.in/itil-classes-in-mumbai.html
This presentation is about -
Create & Manager Users,
Set organization-wide defaults,
Learn about record accessed,
Create the role hierarchy,
Learn about role transfer & mass Transfer functionality,
Profiles, Login History,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This presentation is about -
Based on as a service model,
• SAAS (Software as a service),
• PAAS (Platform as a service),
• IAAS (Infrastructure as a service,
Based on deployment or access model,
• Public Cloud,
• Private Cloud,
• Hybrid Cloud,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This presentation is about -
Introduction to the Cloud Computing ,
Evolution of Cloud Computing,
Comparisons with other computing techniques fetchers,
Key characteristics of cloud computing,
Advantages/Disadvantages,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This presentation is about -
Designing the Data Mart planning,
a data warehouse course data for the Orion Star company,
Orion Star data models,
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
This presentation is about -
Working Under Change Management,
What is change management? ,
repository types using change management
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
This presentation is about -
Overview of SAS 9 Business Intelligence Platform,
SAS Data Integration,
Study Business Intelligence,
overview Business Intelligence Information Consumers ,navigating in SAS Data Integration Studio,
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
What is dimension modeling? ,
Difference between ER modeling and dimension modeling,
What is a Dimension? ,
What is a Fact?
Start Schema ,
Snow Flake Schema ,
Difference between Star and snow flake schema ,
Fact Table ,
Different types of facts
Dimensional Tables,
Fact less Fact Table ,
Confirmed Dimensions ,
Unconfirmed Dimensions ,
Junk Dimensions ,
Monster Dimensions ,
Degenerative Dimensions ,
What are slowly changing Dimensions? ,
Different types of SCD's,
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
4. Teradata Company Highlights
• Founded 1979 – West LA
• First product to market – 1984
• First Terabyte system – 1987
• Acquired by AT&T and
merged with acquired NCR – 1992
• Tri-vested as part of NCR - 1997
• Teradata Corporation – (re)Launched October 1, 2007
– Global Leader in Enterprise Data Warehousing
• EDW/ADW Database Technology
• Analytic Solutions
– Positioned in Gartner’s Leaders Quadrant
in data warehousing since 1999
• Top 10 U.S. publicly-traded software company
– S&P 500 Member
– Listed NYSE: “TDC”
– 2007 - $1.7B revenue
7. Continuous (R)evolution
Sell the HW, give everything else
away
Sell the SW with some HW to
run on
Sell solving business problems – and technology to
solve them
Sell applications with consulting, SW
and HW inside
9. Scale
• Every dimension of the technology must scale to meet today’s requirements
– Data, Data model complexity, Users, Performance, queries, Data loading, …
• What is a big Data Warehouse?
• Total spinning disk?
– 2.5 Petabytes
• Big table?
– 150 billion rows
• Number of tables?
– 300,000
• Insert/Update per day?
– 5 billion records
• Identified users?
– 100,000
• Queries per day?
– 5 million
• Data Turnover rate?
– 1TB per 5 seconds
10. The Problem
10 > 09/2009
Accts. Payable
Accts. Receivable
Invoicing
Sales/Orders
Finance G/L
Customer Support
HR
Payroll
Purchasing
Order Fulfillment
Manufacturing
Inventory …
Marketing
Supply Chain
Finance
Risk Management
Maintenance
Sales
Operations
Inventory
Call Center …
Operational Systems Decision Makers
11. The EDW Solution
Accts. Payable
Accts. Receivable
Invoicing
Sales/Orders
Finance G/L
Customer Support
HR
Payroll
Purchasing
Order Fulfillment
Manufacturing
Inventory …
EnterpriseEnterprise
DataData
WarehouseWarehouse
(EDW)(EDW)
Marketing
Supply Chain
Finance
Risk Management
Maintenance
Sales
Operations
Inventory
Call Center …
Operational Systems Decision Makers
12. Active Enterprise Intelligence™
An Obvious Trend: More Speed, More Users
Strategic Intelligence Operational Intelligence
Enterprise Data Warehouse
BI Tools & reports
Analysis & visualization
Predictive Analytics
EDW Enterprise Integration
Mixed workload management
SOA, BPMS, IDEs
Portals/composite applications
Days
Seconds
13. Active Enterprise Intelligence™ enabled by an
Active Data Warehouse™
STRATEGIC INTELLIGENCEOPERATIONAL INTELLIGENCE
Business Intelligence
Tools and Applications
Teradata Warehouse
Workflow & Applications
Active EventsActive Access
Suppliers Customers Call
Center
Logistics MarketingFinanceProduct/
Services
Executive
Active Enterprise Integration
Active
Availability
Active
Workload
Management
Active
Load
14. Active Enterprise Intelligence™ in Retail
Detecting Retail Fraud
Situation
Thieves make copies of cash register receipts, walk into
the store, pick up merchandise, and return items for
cash.
Problem
Associates in returns department did not have historical
POS receipt retrieval access to verify against previously
“returned” receipts or to do returns without receipts.
Solution
Associates query Teradata to quickly check if a return
has already occurred on that receipt number. Also used
by analysts to understand and prevent excessive
returns.
Impact
(for 500-store chain)
• 100% ROI in 5 months
• Stopped a crime ring on the
first day of rollout
• “Cost savings have been
huge”
15. Active Enterprise Intelligence™ in Retail
Single View of the Customer Across All Channels
Situation
Needed to add Web channel for selling shoes.
Problem
Too much time and cost to keep multiple customer
systems synchronized. Realized they needed just
one customer database, not one more for the Web,
in addition to Call Center, and POS/Store databases.
Solution
Adopted an ADW strategy, moved all customer data
to one Teradata system, revised data models to
cover all channels, added web channel for
commerce, used web services, added TASM to
handle multiple workload types
Impact
• 1M tactical hits to the
EDW per day from the
POS, Call Center, and
Web with 0.11 sec
response time
• Runs simultaneously
with back-office BI,
reports, and ETL
workloads
• Eliminated all other
customer data systems
16. What is the Measure of a Great
Architecture?
Handle huge changes of underlying technologies and
dependent components while continuing to deliver the
key value proposition.
17.
18. Processor RoadmapCPU power radically increasing
2003 2005 2009 2011
90nm
process
45nm
process
65nm
process
32nm
process
22nm
process
Hyper-Threading Dual Core Multi Core
20002000 2008+2008+
SPECInt2000SPECInt2000
5X5X
SINGLE-CORESINGLE-CORE
PERFORMANCEPERFORMANCE
DUAL/MULTI-CORE
PERFORMANCE
2007
20042004
20. Teradata MPP Server Architecture
• Nodes
– Incrementally scalable to 1024
nodes
• Operating System
– Linux, Windows, Unix
• Storage
– Independent I/O
– Scales per node
• BYNET Interconnect
– Fully scalable bandwidth
• Connectivity
– Fully scalable
– Channel – ESCON/FICON
– LAN, WAN
• Server Management
– One console to view
the entire system
SMP Node1 SMP Node2 SMP Node3 SMP Node4
Server
Management
Dual BYNET Interconnects
CPU1 CPU2
Memory
Operating Sys
CPU1 CPU2
Memory
Operating Sys
CPU1 CPU2
Memory
Operating Sys
CPU1 CPU2
Memory
Operating Sys
21. Shared Nothing - Dividing the Work
• “Virtual processors” (vprocs) do the work
• Two types
– AMP: owns and operates on the data
– PE: handles SQL and external interaction
• Configure multiple vprocs per hardware node
– Take full advantage of SMP CPU and memory
• Each vproc has many threads of execution
– Many operations executing concurrently
– Each thread can do work for any user, transaction
• Software is equivalent regardless of configuration
– No user changes as system grows from small SMP to huge MPP
22. Shared Nothing - Dividing the Work
• Basis of Teradata scalability
– Each AMP owns an equal slice of the disk
– Only that AMP reads that slice
• No single point of control for any operation
– I/O, Buffers, Locking, Logging, Dictionary
– Nothing centralized
– Exponential communication costs avoided
AMPsLogs
Locks
Buffers
I/O
# Nodes
Coordination
cost
Teradata
23. Teradata Data Distribution
• Rows automatically distributed evenly by hash partitioning
– Even distribution results in scalable performance
– Done in real-time as data are loaded, appended, or changed.
– Hash map defined and maintained by the system
• 2**32 hash codes, 64K buckets distributed to AMPs
– Prime Index (PI) column(s) are hashed
– Hash is always the same - for the same values
– No reorgs, repartitioning, space management
Table A Table B Table C
AMP1 AMP2 AMP3 AMP4 ……………………………………………………… AMPn
Primary Index
Teradata Parallel Hash Function
P
DM
P
DM
P
DM
P
DM
P
DM
P
DM
P
DM
P
DM
P
DM
RowHash (Hash Bucket) Data Fields
24. Disk Capacity Exploding
with Little Increase in Performance
36 GB
5.5
73 GB
6.0
146 GB
6.4
.044
.080
.155
PerformanceperCapacity
MB/Sec/GB
DiskDriveBandwidth(MB/Sec)
1
2
3
4
5
6
7
8
Disk Drive Capacity
25. Platform Change
• Focus used to be
– Optimization of expensive CPU cycles
– Micro-management of precious disk space
• Now
– Manage I/O
– Balance CPU power to the I/O capacity
– Find new ways to optimize I/O, trading for CPU use as necessary
– Pulling 2.5GB/sec per node continuous
• Discontinuity coming
– SSDs become price competitive and reliable
26. File System
• Teradata wrote a new rule book
– Old one written by IBM 35 years ago, used by all mainstream DBMSs today - except Teradata
• File system built of raw slices
• Rows stored in blocks
– Variable length
– Grow and shrink on demand
– Rows located dynamically
• May be moved to reclaim space, defrag
– Maximum block size is configurable
• System default or per table
• 8K to 128K
• Change dynamically
• Indexes are just rows in tables
• Has evolved from direct management of single spindles to completely virtualized storage, not even
knowing spindle location
27. Workload Management Evolution
• 1984 – pure timeshare
• 1987 – 4 priorities, defined by user
• 1995 – multiple priorities in multiple partitions
• 2000 – weighted workload groups
• 2004 – queuing, reserved resources, focus on tactical work
• 2009 – Visualization and detailed workgroup management
• Future – Set service level goals, our job to deliver
28. Active Workload Management
• Manage workloads
– Reduce server congestion
• Dynamically adjust
in-flight task priority
– Turn the dial – change priorities
• Fast active access queries
– Performance, performance,
performance
• Get maximum throughput
Speed
10
Active
Events
Active
Access
Query and
ReportingActive Load
Active Data
Warehouse
Speed
60
Speed
75
Speed
25
30. Availability Requirements
IT, Finance,
Planners, Power
Users,
Data Miners
Executives,
Middles
Managers,
Marketing
1000000
100000
10000
1000
100
10
Consumers
Suppliers
B2B
Operational
Employees
Category Mgr,
Line Managers,
Service Managers
Users
Mission Critical
Dual
Active
Strategic Intelligence Operational Intelligence
31. “Always ON” – An Elusive Challenge
• Unplanned downtime
– Hardware faults
– Software faults
– Hangs
• Planned downtime
– Software upgrade
– Hardware upgrade
– Data center maintenance
• “Disasters”
– Multi-component failures
– Building disasters
– Area disasters
• And optimize resource value to the business
• And avoid hidden costs and surprises
– Eg Major performance variations
• Major opportunity for research – but must be holistic
– Reaches far beyond core database
32. Real time Operational Actions
Strategic
Intelligence
Operational
Intelligence
1. Customer makes
multi-segment
travel reservation
2. Flight rerouted
causing missed
connections.
“Active”
Enterprise Data
Warehouse
3. What are the customers’
flying history?
4. How profitable is each
customer?
5. Which customers
experienced delays or
other problems in last 6
months?
WebSphere MQ,
Oracle AQ,
Microsoft MSMQ
6. Customer re-booked
and notified.
7. Airport operations
adjusted
33. Real Time Customer Management
Strategic
Intelligence
Operational
Intelligence
4. Is this customer
approaching the
predicted loss rate for
their segment?
5. What offers are
available for this
customer?6. Message sent to floor
Luck Ambassador with
customer offer to
prevent additional
losses.
TIBCO
2. What is the customer’s past
spending history in all our
casinos?
3. What is a significant loss
for this person based on
market segment, past and
predicted behavior?“Active”
Enterprise Data
Warehouse
1. Customer inserts
Total Rewards
Card at Slot
Machine
34. That’s a Wrap!
• Business requires a new level of decision making
– Many more decisions by many more people much faster
– Current representation of the state of the enterprise
• Data Warehouse must evolve to support the requirements of Active
Enterprise Intelligence
• Technology must evolve to deal with the new requirements
– Rich area for research and innovation
– Change view of what data warehouse/BI means
• Teradata driving an aggressive roadmap to meet real business
requirements
35.
36. For More Information click below link:
Follow Us on:
http://vibranttechnologies.co.in/teradata-classes-in-mumbai.html
Thank You !!!
Editor's Notes
[Enter any extra notes here; leave the item ID line at the bottom]
Avitage Item ID: {{E3648B2F-FB1B-499B-B91B-8871943BA5EE}}
Retail Fraud is a $16 B year problem in the USA alone. With web receipts and better copying capabilities, thieves can make multiple copies of a single receipt and make multiple returns for cash or other merchandise. Or they can bring back shoplifted items and try to exchange for cash.
The problem is that often the associates in Returns department don’t have access to past sales information and can’t keep track easily of returned merchandise. This is especially problematic if the policy is to make returns without receipts.
So the solution is straightforward: hook up the Point of Sale systems so within seconds, the Teradata data warehouse is updated with sales, return, exchange, and void data, and provide the Returns department with the entire history of purchases by that customer,, so they can ensure that a sold product can only be returned once.
<Click>
The impact? Huge, according to one Teradata customer who has already built this system. They stopped a crime ring in the first day of their rollout, a group that had defrauded the company of thousands of dollars. They saw a 100% payback on their investment in just 5 months, and continue to reap the benefits of this example use of Active Enterprise Intelligence.
[Enter any extra notes here; leave the item ID line at the bottom]
Avitage! Item ID: {{33DC1405-7316-423E-B269-8F92054D20CE}}
(CLICK)
In this chart, we have 3 different disk drive sizes, and you can see that per generation, disk drive bandwidth hasn’t increased very much.
(CLICK)
As disk capacities get larger (36 GB 73 GB 146 GB) the performance per capacity ratio (Capacity vs. Disk Bandwidth on right side of chart) declines significantly.
The key metric on this slide is performance per capacity (MB/ SEC/ GB)
Look at this slide! Capacity is doubling, but throughput is diminishing! If you fill all the drives up with data, you will not have enough I/O or bandwidth!
Choosing twice as much storage capacity in a configuration, but not increasing the number of physical disks (to keep I/O constant), will result in performance degradation.
Assuming workloads are categorized, this illustration shows “speed limits” which are actually resource limits for each workload. Each workload is allowed to consume a limited amount of resources at any given time to ensure other workloads get their rightful share.
Dynamic Resource Prioritization
Inside every fully utilized active data warehouse, there’s a major turf battle going on. Each job in the database is engaged in an ongoing struggle for more and more resources for its own work, often competing against other diverse activities. In most databases, these me-first conflicts result in short, resource-light queries falling victim to the heavier jobs. Those batch fraud-detection reports and long-running market share analysis queries essentially take ownership of the database and all it has to give. But Teradata Database lets your specific business needs determine how your precious database resources are divided. Once a definition for equitable sharing of database assets is in place, it automatically controls what percent of the CPU and disk I/O those batch reports and complex queries, as well as those vulnerable short queries, will receive. When there’s a handful of users on the system, Teradata Database spreads available resources out relative to the priorities and assignments that have been made to those particular users, without a single sub-second of CPU being wasted.
Teradata Database has made job scheduling and prioritization of the work a core competency since 1988. And recently, that technology has deepened and matured offering even more flexibility. Teradata’s Priority Scheduler can be used to ensure that the event-driven work coming from the web is allowed to cut into line to grab the CPU it needs to get that promotion back to the client quickly. For example, if the tactical query that comes up with that promotion returns an answer in 1 second when running alone in the database, that same query, if armed with a high Teradata Database priority, can maintain a similar turnaround even if multiple complex inventory adjustment queries begin executing at the same time. For the active data warehouse, it will be critical to keep more resource-hungry complex queries from dominating the resources in the system, starving out the shorter tactical work. Teradata’s Dynamic Workload Manager will play a big role in enabling favored work to be as near to real time as it needs to be.
While no 2 dimensional drawing can accurately portray such complex issues, this graphic frames the discussion around when to move to mission critical and dual active solutions. In general, the type of users often correlates with the population of users. For example, we know that the consumer population for many industries can mean 10 of thousands to millions of possible users via the internet . Similarly, for some industries, the population of supplier employees who access your data warehouse can be enormous, maybe not always in concurrent users but certainly in potential users. At the other end of the spectrum, planning, analysis, and power users tend to be a small community albeit an influential one. In the middle of the graphic we see overlaps of many kinds because line managers (category managers, sales managers, service managers, etc.) often bounce between strategic decisio0ns and operational decisions, with probably more time spent in the operational tasks.
Business critical is not a well defined term in our industry. It tends to mean anything less than mission critical. These users can often tolerate downtime, from a few hours perhaps even an entire day. But many data warehouse sites have become so dependent on the EDW, that they have “hardened” the server, software, and procedures to a mission critical level. This means the executives realize how many decisions are made daily based on BI Tools based reporting that they are willing to fund the project to increase system availability.
Mission critical can begin in the EDW and certainly extends all the way to the end of the graphic. These clients understand that large populations of front line users will demand 24X7 data availability. With operational employees you MIGHT be able to tolerate a 10-20 minute outage every month. It depends very much on the business use of the EDW. As the EDW evolves to larger populations and more operational ACTIVE tasks, outrages become increasingly expensive so additional investments in availability become mandatory. In some cases, an active data warehouse begins being so critical to the operational employee that it becomes necessary to step up to a dual active configuration. This is particularly true in retail with 100s of concurrent employees and suppliers using the data, but it may also occur with large call centers or sales staff.
Finally, we hope it is obvious that when consumers gain access to the data warehouse, it is typically for eCommerce purchasing. No downtime is tolerated in this case because the loss of revenue cannot be tolerated.
Problem:
Lack of ability to track customer gaming behavior and Comp redemption.
No mechanism to communicate or react to specific behaviors and trends
Solution:
Player Contact System - when a patron swipes his/her card at a casino that information is sent to Teradata.
The player profile is accessed and it is determined if the casino should make personal contact with that player.
Allows Harrah’s to provide real-time offers to customers at each gaming point
Enables Harrah’s to track the redemption of any comp provided to a guest as the comp is redeemed or partially redeemed. Allows them not to “over-comp” guests.
Future:
“Marketing At The Slots” initiative. This implementation has a BusinessWorks process receiving inbound card-swipes from the Slot Data System and building an EDW query. It then makes a Request/Reply call to Teradata to solicit and compile an XML message which is then published back out on the TIB for consumption by other applications.
This will drive CRM to a new “real-time” level allowing interaction with the customer while they are gaming.
Problem:
Lack of ability to track customer gaming behavior and Comp redemption.
No mechanism to communicate or react to specific behaviors and trends
Solution:
Player Contact System - when a patron swipes his/her card at a casino that information is sent to Teradata.
The player profile is accessed and it is determined if the casino should make personal contact with that player.
Allows Harrah’s to provide real-time offers to customers at each gaming point
Enables Harrah’s to track the redemption of any comp provided to a guest as the comp is redeemed or partially redeemed. Allows them not to “over-comp” guests.
Future:
“Marketing At The Slots” initiative. This implementation has a BusinessWorks process receiving inbound card-swipes from the Slot Data System and building an EDW query. It then makes a Request/Reply call to Teradata to solicit and compile an XML message which is then published back out on the TIB for consumption by other applications.
This will drive CRM to a new “real-time” level allowing interaction with the customer while they are gaming.