We have embraced Cloud and Open-Source further enabling the analytics ecosystems by creating new integration capabilities at scale.
Simplifying technology footprints to make it easier to buy
Bringing scale to analytics
in this slide i have tried to explain what an data engineer does and what is the difference between a data engineer and a data analytics and data scientist
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesDATAVERSITY
With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require.
The answer to this widespread problem is a centralized data pipeline orchestration solution.
Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation.
Key Learnings:
- Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud)
- Find out how DataOps teams are empowered with event-based triggers for real-time data flow
- See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require
- Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment
- See what’s possible with the Stonebranch Universal Automation Center (UAC)
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact [email protected]
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
MongoDB World 2019: The Journey of Migration from Oracle to MongoDB at RakutenMongoDB
Find out more about our journey of migrating to MongoDB after using Oracle for our hotel search database for over ten years.
- How did we solve the synchronization problem with the Master Database?
- How to get fast search results (even with massive write operations)?
- How other issues were solved
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
The document discusses Snowflake, a cloud data platform. It covers Snowflake's data landscape and benefits over legacy systems. It also describes how Snowflake can be deployed on AWS, Azure and GCP. Pricing is noted to vary by region but not cloud platform. The document outlines Snowflake's editions, architecture using a shared-nothing model, support for structured data, storage compression, and virtual warehouses that can autoscale. Security features like MFA and encryption are highlighted.
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
The document introduces a new cloud-based data monetization platform called YourDataConnect focused on helping Chief Data Officers. It notes that 68% of Fortune 1000 companies have a CDO but they struggle to measure ROI on data management spending. YourDataConnect is a SaaS platform that can help CDOs quantify the financial benefits of data across revenue growth, cost reduction, and risk mitigation through an automated dashboard. It allows for data valuation, continuous ROI measurement, data sharing in a marketplace, and regulatory compliance tracking.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
The document discusses creating a data-driven culture and organization. It provides advice on building a data-driven culture, developing the right team and skills, adopting an agile approach, efficiently operationalizing insights, and implementing proper data governance. Specific recommendations include establishing executive sponsorship, advocating for data use, developing data science, engineering, and analytics teams, prioritizing work using agile methodologies, and communicating a business roadmap to operationalize insights.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Logical Data Fabric: Architectural ComponentsDenodo
Watch full webinar here: https://bit.ly/39MWm7L
Is the Logical Data Fabric one monolithic technology or does it comprise of various components? If so, what are they? In this presentation, Denodo CTO Alberto Pan will elucidate what components make up the logical data fabric.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Jupyter in the modern enterprise data and analytics ecosystem Gerald Rousselle
Gerald Rousselle presented on Jupyter in the modern enterprise analytical ecosystem. He discussed how Jupyter can help provide a unified access experience to manage increasing data complexity and enable collaboration. Jupyter is emerging as a technology to solve challenges around access, collaboration, and managing complexity. Rousselle showed how Jupyter is moving beyond data science into business analytics by extending its capabilities with tools like a SQL extension. Key takeaways were that Jupyter will be a central part of analytical ecosystems, help democratize access, and is more than just notebooks through its open source protocols.
Teradata empowers companies to achieve high-impact business outcomes through analytics at scale on an agile data foundation. They provide analytic solutions, technology solutions, and managed services using Teradata and third party technologies. Their unified data architecture called Teradata Everywhere allows customers to analyze data across systems, deploy solutions flexibly in multiple environments including on-premises and cloud, move licenses freely across deployments, and purchase technologies in various ways including as a service. This versatility is meant to de-risk customers' analytics decisions and drive superior business insights.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
MongoDB World 2019: The Journey of Migration from Oracle to MongoDB at RakutenMongoDB
Find out more about our journey of migrating to MongoDB after using Oracle for our hotel search database for over ten years.
- How did we solve the synchronization problem with the Master Database?
- How to get fast search results (even with massive write operations)?
- How other issues were solved
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
The document discusses Snowflake, a cloud data platform. It covers Snowflake's data landscape and benefits over legacy systems. It also describes how Snowflake can be deployed on AWS, Azure and GCP. Pricing is noted to vary by region but not cloud platform. The document outlines Snowflake's editions, architecture using a shared-nothing model, support for structured data, storage compression, and virtual warehouses that can autoscale. Security features like MFA and encryption are highlighted.
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
The document introduces a new cloud-based data monetization platform called YourDataConnect focused on helping Chief Data Officers. It notes that 68% of Fortune 1000 companies have a CDO but they struggle to measure ROI on data management spending. YourDataConnect is a SaaS platform that can help CDOs quantify the financial benefits of data across revenue growth, cost reduction, and risk mitigation through an automated dashboard. It allows for data valuation, continuous ROI measurement, data sharing in a marketplace, and regulatory compliance tracking.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
The document discusses creating a data-driven culture and organization. It provides advice on building a data-driven culture, developing the right team and skills, adopting an agile approach, efficiently operationalizing insights, and implementing proper data governance. Specific recommendations include establishing executive sponsorship, advocating for data use, developing data science, engineering, and analytics teams, prioritizing work using agile methodologies, and communicating a business roadmap to operationalize insights.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Logical Data Fabric: Architectural ComponentsDenodo
Watch full webinar here: https://bit.ly/39MWm7L
Is the Logical Data Fabric one monolithic technology or does it comprise of various components? If so, what are they? In this presentation, Denodo CTO Alberto Pan will elucidate what components make up the logical data fabric.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Jupyter in the modern enterprise data and analytics ecosystem Gerald Rousselle
Gerald Rousselle presented on Jupyter in the modern enterprise analytical ecosystem. He discussed how Jupyter can help provide a unified access experience to manage increasing data complexity and enable collaboration. Jupyter is emerging as a technology to solve challenges around access, collaboration, and managing complexity. Rousselle showed how Jupyter is moving beyond data science into business analytics by extending its capabilities with tools like a SQL extension. Key takeaways were that Jupyter will be a central part of analytical ecosystems, help democratize access, and is more than just notebooks through its open source protocols.
Teradata empowers companies to achieve high-impact business outcomes through analytics at scale on an agile data foundation. They provide analytic solutions, technology solutions, and managed services using Teradata and third party technologies. Their unified data architecture called Teradata Everywhere allows customers to analyze data across systems, deploy solutions flexibly in multiple environments including on-premises and cloud, move licenses freely across deployments, and purchase technologies in various ways including as a service. This versatility is meant to de-risk customers' analytics decisions and drive superior business insights.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
This document summarizes Veritas' experience moving their MongoDB deployment from an on-premise Enterprise edition to MongoDB Atlas on the cloud. Some key points:
- Veritas migrated to improve scalability, simplify administration through Atlas self-service tools, and move to an OpEx model.
- The migration process took only 10 minutes to correct some initial issues and was completed with minimal downtime.
- Moving to Atlas reduced administrative time by around 90% and simplified management tasks like creating new environments.
- Performance monitoring and optimization tools in Atlas helped Veritas identify query improvements.
- Overall, Atlas solved Veritas' challenges around licensing, staffing, and infrastructure management while improving performance.
Maximizing Business Value: Optimizing Technology InvestmentTeradata
The document summarizes Teradata's data warehousing solutions and capabilities. It highlights Teradata's ability to provide unmatched performance, scalability, and manageability. It also emphasizes Teradata's architectural flexibility to meet various requirements, optimized decisioning through superior in-database analytics, and driving superior operational execution with better insights.
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.
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
PARTNERS 2013 - Dr. Stefan Schwarz - Big Data Analytics as a Service Stefan Schwarz
T-Systems and Teradata are partnering to provide big data analytics as a service (AaaS). They outline their big data AaaS architecture, which includes acquiring data from multiple sources, integrating and ensuring data quality, and generating insights through descriptive, predictive, and behavioral analytics. The architecture is delivered through a multi-tenant cloud environment that ensures security, performance, and support. Customers can choose between public, private or hybrid cloud delivery models depending on their priorities around cost, security, availability, and other factors.
Teradata Listener™: Radically Simplify Big Data StreamingTeradata
Teradata Listener™ is an intelligent, self-service solution for ingesting and distributing extremely fast moving data streams throughout the analytical ecosystem. Listener
is designed to be the primary ingestion framework for organizations with multiple data streams. Listener reliably delivers data without loss and provides low-latency ingestion for near real-time applications.
12Nov13 Webinar: Big Data Analysis with Teradata and Revolution AnalyticsRevolution Analytics
Revolution R Enterprise is a big data analytics platform based on the open source statistical programming language R. It allows for high performance, scalable analytics on large datasets across enterprise platforms. The presentation discusses Revolution R Enterprise and how it addresses challenges with big data and accelerating analytics, including data volume, complex computation, enterprise readiness, and production efficiency. It also highlights how Revolution R Enterprise integrates with Teradata to enable in-database analytics for further performance improvements.
- The presentation provides an overview of Teradata's business for investors, including key highlights from the first quarter of 2014.
- It discusses Teradata's product portfolio and data warehouse platforms, positioning Teradata as a leader in data warehousing and big data analytics.
- The presentation shows that Teradata has a diversified customer base across many industries and has experienced steady revenue growth in recent years.
Teradata Technology Leadership and InnovationTeradata
Teradata is a global leader in data warehousing and analytics. It provides a range of products including data warehouse appliances, an enterprise data warehouse, and database technology. Teradata's solutions leverage the latest technology and are optimized for performance, flexibility, and integrated analytics to deliver insights faster.
Product Keynote: Denodo 8.0 - A Logical Data Fabric for the Intelligent Enter...Denodo
Watch full webinar here: https://bit.ly/2O9gcBT
Denodo 8 expands data integration and management to data fabric with advanced data virtualization capabilities. What are they? Denodo CTO Alberto Pan will touch upon the key Denodo 8 capabilities.
Cheryl Wiebe - Advanced Analytics in the Industrial WorldRehgan Avon
2018 Women in Analytics Conference
https://www.womeninanalytics.org/
Cheryl will talk about her consulting practice in Industrial Solutions, Analytic solutions for industrial IoT-enabled businesses, including connected factory, connected supply chain, smart mobility, connected assets. Her path to this practice has bounced between hands on systems development, IT strategy, business process reengineering, supply chain analytics, manufacturing quality analytics, and now Industrial IoT analytics. She spent time working in industry as a developer, as a management consultant, started and sold a company, before settling in to pursue this topic as a career analytics consultant. Cheryl will shed light on what's happening in industrial companies struggling to make the transition to digital, what that means, and what barriers they're challenged with. She'll touch on how/where artificial intelligence, deep learning, and machine learning technologies are being used most effectively in industrial companies, and what are the unique challenges they are facing. Reflecting on what's changed over the years, and her journey to witness this, Cheryl will pose what she considers important ideas to consider for women (and men) in pursuing an analytics career successfully and meaningfully.
The document discusses embedding machine learning in business processes using the example of baking cakes. It notes that while bakers follow exact recipes and processes, the results are not always perfect due to various factors. It then discusses how manufacturers are "data rich but information poor" as they cannot derive meaningful insights from their operational data. The document advocates generating "actionable intelligence" through deep analysis of production data to determine the root causes of issues like cracked cakes, rather than just reporting what problems occurred. This would help manufacturers diagnose and address process flaws more precisely.
RCG has developed a unique approach to helping its clients solve business problems using data. Whether you are interested in learning how to use technology to expose more value from your data through analytics solutions or understanding whether statistical analysis would surface new insights, RCG is ready to help with its Data & Analytics Practice.
Data Integration for Both Self-Service Analytics and IT Users Senturus
See a cloud solution that enables data integration for applications such as Salesforce, NetSuite, Workday, Amazon Redshift and Microsoft Azure. View the webinar video recording and download this deck: http://www.senturus.com/resources/data-integration-tool-for-both-business-and-it-users/.
The rapid growth in self-service business analytics has created tremendous value for organizations, but in many cases has created tension between technical and business users. Technical teams have built solid data warehouses filled with trusted data from source systems such as sales, finance, and operations. Business teams are gaining tremendous insights by analyzing data warehouse information with traditional and new data discovery tools such as Cognos, Business Objects, Tableau, and Power BI.
The Informatica Cloud is a best-of-both-worlds solution that combines data integration for both business and IT users. It allows the following: 1) IT incorporates the business analyst’s data integration routines into the core, trusted data warehouse, 2) Business analysts can do data integration from both cloud-based and on-premise data sources, 3) Business analyst can use the industrial-strength data integration engine that IT teams have loved for years and 4) Integration for apps such as Salesforce, NetSuite, Workday, Amazon Redshift, Microsoft Azure, Marketo, SAP, Oracle and SQL Server.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...Cloudera, Inc.
What if…
…your data stores were limitless and accessible?
…data discovery was fast… really fast?
…connectivity was so seamless you could almost take it for granted?
And what if you could do all this with your preferred BI tool?
Learn how to integrate Cloudera Enterprise with SAP Lumira via embedded connectivity from Simba Technologies.
In this interactive webinar, experts from Cloudera, SAP, and Simba Technologies will introduce strategies for overcoming current data-discovery challenges, show you how to achieve powerful analytical insight, and demonstrate how to integrate Cloudera Enterprise with SAP Lumira.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Learn the Basics of Agile Development: Your Step-by-Step GuideMarcel David
New to Agile? This step-by-step guide is your perfect starting point. "Learn the Basics of Agile Development" simplifies complex concepts, providing you with a clear understanding of how Agile can improve software development and project management. Discover the benefits of iterative work, team collaboration, and flexible planning.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Leading AI Innovation As A Product Manager - Michael JidaelMichael Jidael
Unlike traditional product management, AI product leadership requires new mental models, collaborative approaches, and new measurement frameworks. This presentation breaks down how Product Managers can successfully lead AI Innovation in today's rapidly evolving technology landscape. Drawing from practical experience and industry best practices, I shared frameworks, approaches, and mindset shifts essential for product leaders navigating the unique challenges of AI product development.
In this deck, you'll discover:
- What AI leadership means for product managers
- The fundamental paradigm shift required for AI product development.
- A framework for identifying high-value AI opportunities for your products.
- How to transition from user stories to AI learning loops and hypothesis-driven development.
- The essential AI product management framework for defining, developing, and deploying intelligence.
- Technical and business metrics that matter in AI product development.
- Strategies for effective collaboration with data science and engineering teams.
- Framework for handling AI's probabilistic nature and setting stakeholder expectations.
- A real-world case study demonstrating these principles in action.
- Practical next steps to begin your AI product leadership journey.
This presentation is essential for Product Managers, aspiring PMs, product leaders, innovators, and anyone interested in understanding how to successfully build and manage AI-powered products from idea to impact. The key takeaway is that leading AI products is about creating capabilities (intelligence) that continuously improve and deliver increasing value over time.
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
Hands On: Create a Lightning Aura Component with force:RecordDataLynda Kane
Slide Deck from the 3/26/2020 virtual meeting of the Cleveland Developer Group presentation on creating a Lightning Aura Component using force:RecordData.
24. 24
Proven Analytics
Communications Compliance
Analytics of Things
Customer Satisfaction Index
Anti-Fraud Analytics
Digital / eCommerce Analytics
Many, Many More!
Analytic
Solutions
Customer Journey
Finance Foundation
Product Quality and Maintenance
Packaged Analytics
Marketing Attribution
Predictive Early Warning
Analytic Calculator
Innovation Analytics
Rapid Analytic Consulting Engagement
Opportunity Identification
Business Value Framework
Analytic Solutions
Leveraging Analytic IP for High-Impact Business Outcomes
Analytic Applications
Customer Interaction Manager
Real-Time Interaction Manager
Demand Chain Management
Teradata Analytics for SAP
Master Data Management
25. 25
Approach to Enterprise Analytic Architecture
Architecture
consulting
is not about technology
Leverage reference
architecture for
UDA deployment
Architecture combines
open source and
commercial technology
Leverage the
combination of
business & technology
consulting
28. 28
Why Teradata Is the Right Partner
Company
• Highly ethical, trusted
partner
• The industry leader in
DW and Analytics
• Laser-focused on
driving business value
from data & analytics
Best-in-class
Technology
• Superior performance
& throughput
• Unmatched
scalability,
concurrency &
workload
management
Unmatched
Experience
• DW founder with 37
years’ experience &
innovation
• Proven ability to
execute
• 10,000+ employees
with dedicated focus
on analytics
Flexible
Deployment
Options
• De-risk decisions
• Teradata
Everywhere™ —
on-premises, public
cloud, private
cloud, hybrid
Leading Vision
& Strategy
• Leverage the right
technology for the
job
If you think only hardware or data warehouse when you think of Teradata…
…it’s time to think again.
#3: Better Analytics Solutions, Better Business Outcomes
Teradata has demonstrated that we are a sustainable business that is committed to the long-term. We operate with integrity in every market where we serve customers and employ workers. In fact, we are consistently ranked among the world’s most ethical and sustainable companies.
Once again in 2018, Teradata was named one of the World’s Most Ethical Companies by Ethisphere.
And, Teradata was also listed in the Dow Jones North America Index.
#7: Single view of the business
Movement away from siloed data
Single version of the data for customer, financials, supply chain, risk and fraud…
• Need for new and/or complex queries
Fraud reduction, cross-sell up-sell
Ability to answer any question any time because you don’t always know what you want to ask.
• Evolution of workloads—both strategic and tactical
Real-time insight into business processes
• Financial
Decreasing overall server count—by consolidating on a single Teradata platform
Decreasing amount of storage – eliminating duplicate data and storage contained in marts
Decreasing licensing amount – single platform licensed vs. numerous marts
Decrease ETL pieces – number of loads and load times
Decrease overall infrastructure management costs – (hardware, DBMS, OS etc.)
#9: We have embraced Cloud and Open-Source
Enabling the analytics ecosystems by creating new integration capabilities
Simplifying technology footprints to make it easier to buy
Bringing scale to analytics
Extending our leadership position
#11: Being Business Outcome led means we lead with Business Solutions. We identify What problem needs to be solved, and then we leverage architectural expertise and Technology solutions when need to help solve the unique customer problem.
By leveraging the power of a thoughtful and strong data and analytics strategy, Teradata can help business unleash these high impact business outcomes.
#12: (CLICK) Analyze Anything--Enables analytic users throughout the organization to use their preferred analytic tools and engines across data sources, at scale
(CLICK) Deploy Anywhere--Provides analytic processing across flexible deployment options, including the Teradata Cloud and public clouds, as well as on-premises on Teradata hardware or commodity hardware
(CLICK) Buy Any Way--Empowers companies to purchase software in more accommodating ways based on specific use cases through simplified pricing bundles, subscription-based licenses, and as-a-service options
(CLICK) Move Anytime--Future-proofs buying decisions by taking advantage of our software license portability that provides flexibility to run analytics across deployment options
#14: Daily Performance Benchmarking
Teradata database superior performance on IntelliFlex
IntelliBase offering delivers lower price point for Teradata Database
Best price/performance SQL engine
#16: IntelliCloud subscriptions – which include the software, services, infrastructure, and support all rolled into one complete package – are available today with deployment choice between…
… the Public Cloud, which consists of both AWS and Azure…
… and Teradata Cloud, which is Teradata infrastructure in Teradata data centers.
Later this year we will add on-premises options to IntelliCloud featuring Teradata Hardware in your data center.
#17: Let me talk about the as-a-service features
Performance – We have the ability to offer a wide range of performance options- a) with multiple types of instances b) with multiple regions for enabling low-latency access of our customers application environments
Security – we provide audit compliance like ISO 27001, SOC 1 and 2 and HIPAA standards, 24x7 monitoring of our customers Encryption for data in motion and at rest.
Availability – provides the SLA for definite uptime & daily backups for rollback and business continuity ensuring mission critical analytics are run as planned.
Environmental operations are taken care of such as software patches, version upgrades and account administration through our as a service web support portal
#18: Earlier I covered the core value proposition and features of IntelliCloud services, let me share with you our family of service offerings available now and what is coming in future.
(CLICK) Analytics Platform as a Service is a self-service analytics environment that can scale on-demand, is a subscription based offering with variable terms from monthly to multi-year contracts. Available now
(CLICK) Back up As a Service – this enables seamless backup management of the Teradata Analytics Platform through a self-service user experience. Available in Q1
(CLICK) Disaster recovery as a service - We are building upon our backup as a service and extending it to provide our customers with the ability to quickly recover from a disaster with minimal interaction. This will be available in Q4.
Think about our IntelliCloud growth in 3 areas:
Improving the Foundation
Enriching it by providing Value Added Services and
Extending the Market to drive more TCore
#20: Our vision for how we capture these analytic workloads is through the Teradata Analytics Platform.
The Teradata Analytics Platform is built around 4 key principles:
First, we need to let our customers use their favorite analytic tools and we need to provide APIs to make this easy.
Second, we need to allow our customers to use the most suitable languages for any given analytic task.
Third, we need to provide support for different data formats like JSON and Avro, and access to a range of data stores like HDFS and S3.
And finally, we need to enable our customers to take advantage of the best analytic engines and functions whether by putting Aster functions inside Teradata or through integration with analytic frameworks like Spark or Tensorflow.
You will hear more about this on Wednesday. This is an exciting vision, but it is still very early days. So, you need to be clear on what we can sell today versus what is longer-term direction.
#22: Note animation
Historically we priced each feature independently. Starting with the SQL Engine
As we added new features the pricing model would change
Now we have bundled all core features into one price
On top of that we have bundled all of the supporting Eco-system features into an add on bundle
This provides much greater pricing simplicity but also allows you to have features at your finger tip instead of having to guess in advance what you may need.
#24: To deliver on this perspective, our strategy is to provide proven world class consulting and best in class technology
And our offering portfolio is designed to deliver this business outcome led and technology enabled approach
Our consulting offers are the key way that we deliver business outcomes for our customers
We have consulting practices that deliver a range of customer needs - from analytic architecture, to data science, to building analytic software to meet specific customer needs
And these consulting practices are enabled by intellectual property and methodologies that prove our value and allow us to bring our expertise in a repeatable way
Together, our consulting capabilities and practices allow us to deliver differentiated business results for our customers
You will hear more about this from Rick and his team on Thursday
Our technology offers are built around a Teradata Everywhere foundation
Allowing our customers to analyze anything through our Analytics Platform
And giving them the flexibility of deployment and purchase options
Through Teradata Everywhere, we deliver an agile data foundation for our customers
You will be hearing more about this from Oliver and his team on Wednesday
#25: NOTE under Analytic Applications, Demand Chain Management is sunsetting over the next two years.
Innovation Analytics (CLICK)
Proven Analytics (CLICK)
Packaged Analytics (CLICK)
Analytic Applications (CLICK)
Analytic Solutions
#33: Gartner Critical Capabilities for Data Warehouse and Data Management Solutions for Analytics
Gartner MQ “Ability to Execute” includes ability to execute against vendor strategy. Oracle has a focus on their large installed base.
Gartner MQ “reference survey data indicates that for the past five years
approximately 70% of existing Oracle customers have simply selected Oracle for data
warehouse by default.“
#35: One of the great things about Intellicloud services on the public cloud is uniform global availability.
Thus a key benefit here is our customers can deploy the same software across multiple regions
IntelliCloud on AWS is available NOW in all USA AWS commercial regions, but not yet in the US GovCloud.
IntelliCloud on AWS is also available NOW in Europe in AWS Frankfurt, AWS Ireland, and AWS London.
IntelliCloud on Azure WILL BE available in Q1 in all the commercial Azure regions in the United States, but not yet in the US Department of Defense or US Gov.
Our plan by the end of this year is to make it available with prioritization based on customer demand
For AWS in canada , Australia and Asia region
For Azure in Europe, Canada,Australia,Asia Regions
CLICK
#37: - Founded Google Brain project (DL AI). VP & Chief scientist at Baidu. Co-Founder of Coursera. Taught online ML class to 100,000 students, Stanford prof
- Google his name, ~4x Elon Musk
- AI needs lots of data. Clean, structured data
- BTW, we are good at that
(CLICK to reveal quote)
25
#38: - google paper from 2015
- ML – the models
- necessary scaffolding
(CLICK to reveal paper, CLICK again to reveal chart.)
26
#39: Teradata Analytics Platform is designed for both current and future challenges