Traditional BI promises security and scale, but at what cost? Often, working with data, finding answers and sharing them can be laborious and time intensive. The rapid growth and maturation of cloud technologies offers an easier path.
With Tableau and AWS you can move your BI to the cloud and deliver the security and scale of your traditional BI, but with accessibility, flexibility, and speed. Take a closer look at the benefits of cloud BI, and how you can get started today.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Power BI Advanced Data Modeling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Solution Architect, Doug McClurg, to learn how to create professional, frustration-free data models that engage your customers.
This presentation contains an introduction of tableau software and in a particular way in Connecting to data, Visual Analytics, Dashboard and stories, Calculations, Mapping and Tableau Online & Competitors.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Power BI Advanced Data Modeling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Solution Architect, Doug McClurg, to learn how to create professional, frustration-free data models that engage your customers.
This presentation contains an introduction of tableau software and in a particular way in Connecting to data, Visual Analytics, Dashboard and stories, Calculations, Mapping and Tableau Online & Competitors.
With the new Power BI Preview Microsoft brings more Self-service BI functionality to the users. In this session we will look from a different perspective to the offering: What about Governance, Application LifeCycle, Enterprise Integration? We will review what is currently possible in the preview for sharong querys, integrating the cloud offering with your enterprise data sources, monitoring datasources and gateways and what is possible to use it on Windows Mobile devices.
An overview of the different sets of functionality of Tableau solution suite, and how it can address the many facets of a comprehensive data mining solution.
Enable the business and make Artificial Intelligence accessible for everyone! Marc Lelijveld
Microsoft is doing a great job in enabling every user to apply Artificial intelligence in his or her daily business by implementing AI functionality in Power BI, Microsoft's end-user BI and analytics tool. Finding insights from the data can be challenging with the massive volumes of data generated today. This is where AI can help to automatically find patterns, help users understand what the data means, and predict future outcomes. But most important of all, enabling the business to make data driven decisions!
In this session I will tell you all about the AI capabilities which Microsoft offers and made available for each and every user within the organization. I'll show you how business users will be able to work with this, without writing a line of code. A session with an overview of AI and a bunch of live demos on how you can implement AI to your daily business.
In this session:
- Azure Cognitive Services
- Auto ML (Machine Learning)
- Power BI Dataflows
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Enabling Governed Data Access with Tableau Data Server Tableau Software
Data Server is one of the most powerful tools within Tableau Server to promote security, governance, data exploration, and collaboration—all while hiding the complexity of your data architecture from business users. It allows you to centrally manage live connections or extracted data sets as well as database drivers. At the same time, Data Server enables business users to have trust and confidence that they are using the right data so they can explore it the way they want and discover new insights that drive business value. Learn how Data Server helps IT become a stronger business enabler with governed data access.
Enterprise and multi-tier Power BI deployments with Azure DevOps.Marc Lelijveld
In Power BI we are used to create reports and dashboards really quickly, but in most cases we forget to think about governance, development and maintenance at an enterprise wide scale.
During this session I share some best practices about applying DTAP (Development, Production, Acceptance and Production), or better known as multi-tier deployment.
By using Azure DevOps for deployment we bring back the structure and use a self-service tool in an enterprise environment. Beside deployment there is also version control and enterprise roll-out of your content in a managed structure.
In this session:
- Azure DevOps
- PowerShell
- Power BI REST API
Afternoons with Azure - Power BI and Azure Analysis ServicesCCG
See how Microsoft Power BI and Azure Analysis Services are influencing the BI and analytics market. Journey through data structures and fundamentals for setting up your next dashboard initiative.
Interested in learning more? Click ccganalytics.com/resources for more or call (813) 265-3239.
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Data Visualization Trends - Next Steps for TableauArunima Gupta
Want answers to:
- What is data visualization?
- Why is it deemed disruptive in the field of analytics?
- What is Tableau?
Come view the slide deck!
Concludes with:
- Digital strategy recommendations for Tableau to become the winner in a winner-take-all-market
With the new Power BI Preview Microsoft brings more Self-service BI functionality to the users. In this session we will look from a different perspective to the offering: What about Governance, Application LifeCycle, Enterprise Integration? We will review what is currently possible in the preview for sharong querys, integrating the cloud offering with your enterprise data sources, monitoring datasources and gateways and what is possible to use it on Windows Mobile devices.
An overview of the different sets of functionality of Tableau solution suite, and how it can address the many facets of a comprehensive data mining solution.
Enable the business and make Artificial Intelligence accessible for everyone! Marc Lelijveld
Microsoft is doing a great job in enabling every user to apply Artificial intelligence in his or her daily business by implementing AI functionality in Power BI, Microsoft's end-user BI and analytics tool. Finding insights from the data can be challenging with the massive volumes of data generated today. This is where AI can help to automatically find patterns, help users understand what the data means, and predict future outcomes. But most important of all, enabling the business to make data driven decisions!
In this session I will tell you all about the AI capabilities which Microsoft offers and made available for each and every user within the organization. I'll show you how business users will be able to work with this, without writing a line of code. A session with an overview of AI and a bunch of live demos on how you can implement AI to your daily business.
In this session:
- Azure Cognitive Services
- Auto ML (Machine Learning)
- Power BI Dataflows
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Enabling Governed Data Access with Tableau Data Server Tableau Software
Data Server is one of the most powerful tools within Tableau Server to promote security, governance, data exploration, and collaboration—all while hiding the complexity of your data architecture from business users. It allows you to centrally manage live connections or extracted data sets as well as database drivers. At the same time, Data Server enables business users to have trust and confidence that they are using the right data so they can explore it the way they want and discover new insights that drive business value. Learn how Data Server helps IT become a stronger business enabler with governed data access.
Enterprise and multi-tier Power BI deployments with Azure DevOps.Marc Lelijveld
In Power BI we are used to create reports and dashboards really quickly, but in most cases we forget to think about governance, development and maintenance at an enterprise wide scale.
During this session I share some best practices about applying DTAP (Development, Production, Acceptance and Production), or better known as multi-tier deployment.
By using Azure DevOps for deployment we bring back the structure and use a self-service tool in an enterprise environment. Beside deployment there is also version control and enterprise roll-out of your content in a managed structure.
In this session:
- Azure DevOps
- PowerShell
- Power BI REST API
Afternoons with Azure - Power BI and Azure Analysis ServicesCCG
See how Microsoft Power BI and Azure Analysis Services are influencing the BI and analytics market. Journey through data structures and fundamentals for setting up your next dashboard initiative.
Interested in learning more? Click ccganalytics.com/resources for more or call (813) 265-3239.
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Data Visualization Trends - Next Steps for TableauArunima Gupta
Want answers to:
- What is data visualization?
- Why is it deemed disruptive in the field of analytics?
- What is Tableau?
Come view the slide deck!
Concludes with:
- Digital strategy recommendations for Tableau to become the winner in a winner-take-all-market
In 2016, cloud technologies went mainstream. But with maturity came the realization that moving to the cloud doesn’t happen overnight. CIOs are prioritizing hosted computing and cloud data storage. But they’re approaching the shift as a gradual, multi-year journey.
Many startups and small businesses will continue to go all-in on cloud. But enterprises will find success in a slow but steady move from on-prem. Hybrid ecosystems—of data, software, and infrastructure—will be the reality for most established organizations.
As this shift to cloud progresses where are things are headed? This paper highlights the top cloud trends for 2017.
Tableau Drive, A new methodology for scaling your analytic cultureTableau Software
Tableau Drive is a methodology for scaling out self-service analytics. Drive is based on best practices from successful enterprise deployments. The methodology relies on iterative, agile methods that are faster and more effective than traditional long-cycle deployment. A cornerstone of the approach is a new model of a partnership between business and IT.
The Drive Methodology is available for free. Some organizations will choose to execute Drive themselves; others will look to Tableau Services or Tableau Partners for expert help.
An Analytics Culture Drives Performance in Asia Pacific Organizations Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this presentation, see what IDC Research expert and Amaysim BI Manager have to say about:
1. Why meeting the analytical needs of business users matter to organizational performance
2. What’s driving leaders in APAC enterprises towards a self-service paradigm?
3. How to encourage adoption of analytical tools in your organization
4. How leading APAC enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
Want to learn more? Check out the full webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
Business intelligence norms are evolving across the retail industry, and leading retailers are prioritizing analytics initiatives as a result. While the trend toward retail analytics isn’t new, maturing technologies and techniques are. Here are the trends that will shape retail analytics in 2017.
Cloud computing is becoming the norm. People are no longer asking why they should go to the cloud. Instead, we hear customers asking insight on what’s working and what they should be thinking about.
Bigger, faster, and cloudier: that’s where big data is headed in 2016. More people are doing more things faster with their data, but the details of how continue to evolve. Get up to speed on the latest trends in big data.
Tableau Software - Business Analytics and Data Visualizationlesterathayde
Tableau boasts drag-and-drop features that allow users to visualize information from any structured format. Tableau is the only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards making it the fastest growing tool on the planet for Business Intelligence. Gartner has recently named us in the magic Quadrant among the Top 27 vendors for BI tool. We are no 1 in ease of use, no 1 in reporting and dashboard creation, interactive visualization, etc.
. Feel free to download the product, see the sample reports & dashboards for other industries from
http://www.tableausoftware.com
Please use the below link to download a 15 Day trial version of Tableau Desktop and Server Versions.
http://www.tableausoftware.com/products/trial
You can also do a self-training by going through the Videos in the below link.
http://www.tableausoftware.com/learn/training.
How a Data-Driven Culture Improves Organizational Performance Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this slideshare, learn from IDC research and Amaysim BI Manager about:
Why meeting the analytical needs of business users matter to organizational performance
What’s driving leaders in APAC enterprises towards a self-service paradigm?
How to encourage adoption of analytical tools in your organization
How leading Asia Pacific enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
This slideshare came from a full webinar delivered by Tableau. You can the full length webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
The last few years have seen a sea of changes in business intelligence (BI). The proliferation of data and advances in technologies are pushing the pace of innovation. Here are 10 trends to watch for in 2012.
Next generation analytics isn’t on its way… it’s already arrived. Most businesses are in the process of developing their new data platforms on the cloud, or moving their existing analytics infrastructure to the cloud. Attend this webinar to learn model architectures and best practices for analytics on AWS. You’ll also learn how you can leverage cloud to spread insight throughout your organization.
Join us to learn:
• What cloud data infrastructure should look like
• How to optimize your analytics deployment on the cloud
• Using Tableau to find and share new insights with everyone in your organization
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
BCG's years of experience distilled into the twelve necessary imperatives for success. For more information, please visit: https://www.bcgperspectives.com/postmerger_integration
These slides present results from The Boston Consulting Group’s 2015 Big Data and Trust Consumer Survey of more than 8,000 consumers in the US and the top five European economies (France, Germany, Italy, Spain, and the UK) and the results of BCG’s 2015 Big Data and Trust Company Survey of the data stewardship practices of 140 companies in eight industries.
2 years ago if someone had claimed they could stand up a petabyte scale data warehouse in under an hour and then have a non-technical business user querying it live 30 minutes later without knowing any SQL or coding language, they would have been laughed out of the room. These days, that’s called taking advantage of disruptive technology. Amazon Web Services and Tableau Software have shifted the entire paradigm by which organizations not only store and access their data, but ultimately how they innovate with it. The fast, scalable, and inexpensive services that AWS provides for housing data combined with Tableau’s unbelievably flexible and user friendly visual analytic solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts without expensive overhead or lengthy ramp-up time. Attend this webinar to learn how Amazon Web Services and Tableau Software are leveraged together everyday to: • Empower visual ad-hoc data discovery against big data • Revolutionize corporate reporting and dashboards • Promote data driven decision making at every level The presentation will include: • A live demonstration of AWS and Tableau working together • A real customer case study focused on fraud detection and online video metrics • Live Q&A and an opportunity to trial both solutions
From raw data to business insights. A modern data lakejavier ramirez
In this talk I spoke about the pitfalls when you try to build a data lake, and how you can solve the problem either with unmanaged open source, or with the managed and/or native solutions at AWS. Delivered at the Madrid Data Engineering meetup in May 2019
Building a modern data platform in the cloud. AWS DevDay Nordicsjavier ramirez
This presentation introduces the problems of data engineering and the AWS services you can use to make your life easier. It featured a live demo in Stockholm and Oslo, which is shown as screenshots. The URL for doing the demo yourself is included in the slides
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
The Presentation Talks about how Cloud Computing is Big Data's Best Friend and How AWS Cloud Components Fit in to complete your Big Data Life Cycle.
Agenda:
- How Big is Big Data Actually growing?
- How Cloud has the potential to become Big Data's Best Friend
- A tour on The Big Data Life Cycle
- How AWS Cloud Components Fit in to this Life Cycle
- A Case Study of Our Log Analytics Tool Cloudlytics, using Big Data Implementation
on AWS Cloud.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Scaling your Analytics with Amazon Elastic MapReduce (BDT301) | AWS re:Invent...Amazon Web Services
Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. Join the General Manager of Amazon EMR, Peter Sirota, to learn how to scale your analytics, use Hadoop with Amazon EMR, write queries with Hive, develop real world data flows with Pig, and understand the operational needs of a production data platform.
The Modern Tech Stack: Data Analytics in the Cloud for Developers and FoundersAggregage
You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In which format? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Can business people create reports via drag and drop? Can operations monitor what’s going on? Will the data lake scale when you have twice as much data? Is your data secure? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently.
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
SendGrid Improves Email Delivery with Hybrid Data WarehousingAmazon Web Services
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
SendGrid was looking to evolve its data warehouse architecture in order to improve decision making and optimize customer experience. They needed a scalable and reliable architecture that would allow them to move nimbly and efficiently with a relatively small IT organization, while supporting the needs of both business and technical users at SendGrid.
SendGrid’s Director of Enterprise Data Operations will be joining architects from Amazon Web Services (AWS) and Informatica to discuss SendGrid’s journey to a hybrid cloud architecture and how a hybrid data warehousing solution is optimized to support SendGrid’s analytics initiative. Speakers will also review common technologies and use cases being deployed in hybrid cloud today, common data management challenges in hybrid cloud and best practices for addressing these challenges.
Join us to learn:
• How to evolve to a hybrid data warehouse with Amazon Redshift for scalability, agility and cost efficiency with minimal IT resources
• Hybrid cloud data management use cases
• Best practices for addressing hybrid cloud data management challenges
MSC203_How Citrix Uses AWS Marketplace Solutions To Accelerate Analytic Workl...Amazon Web Services
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
How Citrix Uses AWS Marketplace Solutions to Accelerate Analytic Workloads on...Amazon Web Services
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuickSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
Increasingly, valuable customer data sources are dispersed among on-premises data centers, SaaS providers, partners, third-party data providers, and public datasets. Building a data lake on AWS offers a foundation for storing on-premises, third-party, and public datasets cost effectively with high performance. This workshop introduces AWS tools and technologies you can use to analyze and extract value from petabyte-scale datasets, including Amazon Athena and Amazon Redshift Spectrum.
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
Everything You Need to Know About Big Data: From Architectural Principles to ...Amazon Web Services
In this session, we discuss architectural principles that help simplify big data analytics. We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Migrating Massive Databases and Data Warehouses to the Cloud - ENT327 - re:In...Amazon Web Services
Databases continue to grow to be multiple terabytes in size, but migrating to the cloud doesn't have to take days or create disruption for your business. To perform data migration at petabyte scale with minimal impact to your business, you can now use the new combination of AWS Database Migration Service replication agents and AWS Snowball. In this session, we discuss how to extract large-scale data from an on-premises Oracle database and migrate it to Amazon Aurora. We then outline a step-by-step process for converting your Oracle schema to a PostgreSQL-based schema.
Similar to 5 Reasons to Move Your BI to the Cloud (20)
Every year around this time a group of us at Tableau try to slow down and take a look around. We take some time to talk about what’s happening in the market—what’s new, what’s surprising, what’s meaningful. And what a time to be in the world of data and analytics! Smart new platforms are launched seemingly every month. Organizations are starting to see the benefits of broadly empowering people with data. People are using data in ways that were science fiction just a couple of years ago.
It’s always a great discussion. It’s this discussion that drives our Top 10 Trends in Business Intelligence for 2015.
Tableau Drive, Uma nova metodologia para implantações corporativasTableau Software
O Tableau Drive é uma metodologia para expandir a analítica de autoatendimento. O Drive é baseado em práticas recomendadas de implantações empresariais bem-sucedidas. A metodologia é baseada em métodos iterativos e ágeis que são mais rápidos e mais eficazes que a implantação tradicional em ciclos longos. Um marco da abordagem é um novo modelo de parceria entre o negócio e TI.
A Metodologia do Drive está disponível gratuitamente. Algumas organizações optam por executar o Drive elas mesmas; outras recorrem aos Tableau Services ou Tableau Partners para obter ajuda de especialistas.
Tableau Drive는 셀프 서비스 분석을 확장하기 위한 방법론입니다. Drive는 성공적인 엔터프라이즈 배포 우수 사례를 기반으로 만들어졌습니다. Drive방법론은 주기가 긴 기존 배포에 비해 빠르고 효과적일 뿐만 아니라 반복적이며 대응력이 뛰어난 방법을 사용합니다. 이 접근 방식의 근본에는 비즈니스와 IT 간의 새로운 파트너쉽 모델이 있습니다.
Drive 방법론은 무료로 사용할 수 있습니다. 조직에 따라 직접 Drive를 실행할 수도 있고 Tableau Service 또는 Tableau 파트너에게 문의하여 전문가의 도움을 받을 수 있습니다.
Tableau Drive, Une méthodologie innovante pour les déploiements en entrepriseTableau Software
Tableau Drive est une méthodologie de déploiement de l’analytique en libre-service. Drive est basé sur les meilleures pratiques observées lors de déploiements en entreprise réussis. La méthodologie repose sur des méthodes agiles et itératives, plus rapides et plus efficaces que les déploiements traditionnels à cycle plus long. Un nouveau modèle de partenariat entre utilisateurs métier et services informatiques constitue la pierre angulaire de cette approche.
La méthodologie Drive est disponible gratuitement. Certaines organisations préfèrent exécuter Drive elles-mêmes ; d’autres choisissent de solliciter l’aide de spécialistes via les services Tableau ou les partenaires Tableau.
Tableau Drive, Una nueva metodología para implementaciones empresarialesTableau Software
Tableau Drive es una metodología para ampliar el análisis de autogestión. Drive se basa en prácticas recomendadas de implementaciones empresariales exitosas. La metodología se apoya en métodos iterativos y ágiles que resultan más rápidos y eficaces que la implementación de ciclos largos tradicional. Una piedra angular de este enfoque es un modelo nuevo que proviene de una asociación entre el negocio y la TI.
La metodología de Drive es encuentra disponible de manera gratuita. Algunas organizaciones ejecutarán Drive por cuenta propia; otras consultarán a los servicios de Tableau o a socios de Tableau para obtener ayuda experta.
Tableau Drive, Die neue Methode für Bereitstellungen in UnternehmenTableau Software
Tableau Drive dient als Methode zur Verbreitung einer Self-Service-Analysekultur in Organisationen. Drive basiert auf Best Practices, die bei Unternehmen bereits erfolgreich umgesetzt wurden. Die Methode stützt sich auf iterative, agile Praktiken, die schneller und effektiver sind als herkömmliche Self-Service-Analysen über lange Zeiträume. Grundpfeiler dieses Ansatzes ist ein neues Partnerschaftsmodell zwischen Geschäfts- und IT-Abteilung.
Das Drive-Verfahren ist kostenlos verfügbar. Manche Unternehmen arbeiten eigenständig mit Drive, andere suchen fachliche Hilfe bei Tableau-Services oder wenden sich an einen Tableau-Partner.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
3. 5 Reasons to Move Your BI to the Cloud
• Security without sacrificing accessibility
• Speed when you need it
• Effortlessly scale up and out
• Deliver an agile single version of the truth
• You don’t have to give up on-prem
4. Traditional BI promises security,
and it delivers by making it nearly
impossible to access data. Local servers
are secure, but can be challenging to
configure for failover.
Once your data is in AWS, you’re ready for
High Availability and Failover with a click.
Tableau can make your data more secure
with Informatica and Kerberos, while
simplifying access to shared data that the
whole team needs with Data Server.
Security without sacrificing accessibility
Traditional BI
Promise
Reality Why Cloud?
Security
Data can be difficult
to permission. Hard
to configure for
failover.
Configure
permissions securely
in minutes
leveraging existing
systems. Cloud
solutions
automatically
failover.
Learn more:
Tableau Data Server
High Availability and Failover Support for AWS DB
Informatica for Tableau
5. Speed is a reference point for every existing
BI implementation.
Under optimal conditions… it might actually
be delivered.
The cloud offers flexibility that can supply
fantastic speed by allowing you to match
your capacity to your load on demand. By
using S3 for storage, EMR for compute and
Tableau for analytics, you can separate
every process and analyze big data on the
fly.
Speed when you need it
Traditional BI
Promise
Reality Why Cloud?
Speed
Rapid changes in
load are difficult to
respond to.
Combined storage
and compute can
slow down analysis.
Scale up or scale
down in seconds.
Separate storage
and compute. Pay
for what you need.
Use Hadoop,
relational databases,
warehouses as
needed and
preferred.
Learn more:
Using Amazon EMR and S3 for Ad Hoc Access to Massive Data
Netflix and Presto
Tableau’s Amazon Aurora Connector
6. For global organizations, deploying a
scalable, performant and easy to use
BI implementation can be a challenge.
With AWS and Tableau Online, your data
can be replicated (or accessed)
in multiple regional data centers.
If you need more capacity, click to
add it. The cloud can often be more
affordable than existing solutions.
Effortlessly scale up and out
Traditional BI
Promise
Reality Why Cloud?
Scale
Regional teams
hard to support with
local data centers.
Servers are
expensive…
Utilize multiple
regional datacenters
to support different
teams. Scale up or
out on the fly.
Leverage AWS scale
for affordability.
Learn more:
Tableau Online EMEA Data Center
Cross-Region Replication in AWS
Cloud economics
7. With large and complex datasources,
it only makes sense that organizations
would define common calculations, names
and measures to standardize. The problem
is, traditional BI defines those standards in
code and inflexible ETL processes.
Tableau can enable you to define your
single version of the truth visually,
and instantly share that connection with
anyone through Data Server. Leverage
aliasing, joins, unions, calculations,
grouping, sets, and a performant direct
connection to any Amazon database to give
your team data they can rely on.
Deliver an agile single version of the truth
Traditional BI
Promise
Reality Why Cloud?
Single version
of the truth
To properly ingest
and combine data,
ETL processes must
be manually
configured by IT.
They are highly
inflexible to change.
Use Tableau Data
Server for easy
access to aliasing,
joins, unions,
calculations,
grouping, sets and
other key tools.
Amazon makes data
ingest and
transformation easy.
Learn more:
Journey to a Single Version of the Truth
AWS Data Pipeline
8. What if you have an investment in on-prem?
That’s great. Tableau makes it easy to use
your on-prem data and local server too.
If it makes sense for you to move to
the cloud, Tableau supports you and your
data wherever it is, however it
is hosted.
A hybrid approach will be the practical way
forward for many organizations. Move a
little, a lot, or all the way to
the cloud.
You don’t have to give up on-prem
Traditional BI
Promise
Reality Why Hybrid?
Everything needs to
be either in the cloud
or on-prem.
Most organizations
have some data in
the cloud and some
on-prem. Others are
experimenting with a
move to the cloud.
No one is going to
move to the cloud in
a day. If you
invested in on-prem,
you can continue to
leverage that
investment while
experimenting with
the cloud and finding
the right solution for
your business.
Learn more:
7 Trends in the Cloud
9. Get started in the cloud today
It doesn’t take much to get started in the Cloud. In
five minutes, you can have a trial
of Tableau Online and a Redshift instance ready to
go.
Read on for more detailed information
on AWS and Tableau architecture.
Getting Started with Redshift
Tableau Online Trial
10. Appendix: Cloud Data and BI Architecture
Flat files
Application data
Server logs
Internet APIs
Tableau Desktop
Server or Online
S3
Hive
EMR
Spark
AML
RDS
Redshift
Collect/Store Store/Analyze Data warehouse AnalysisRaw data
Machine Learning
11. Appendix: Cloud Data and BI Architecture
Collect data from multiple sources and store in
its native format
Flat files
Application data
Server logs
Internet APIs
Tableau Desktop
Server or Online
S3
Hive
EMR
Spark
AML
RDS
Redshift
Collect/Store Store/Analyze Data warehouse AnalysisRaw data
Machine Learning
12. Flat files
Application data
Server logs
Internet APIs
Appendix: Cloud Data and BI Architecture
Separate storage and consumption. Eliminate
capacity constraints.
Pay for what you use.
Tableau Desktop
Server or Online
S3
Hive
EMR
Spark
AML
RDS
Redshift
Collect/Store Store/Analyze Data warehouse AnalysisRaw data
Machine Learning
13. Appendix: Cloud Data and BI Architecture
Support different use cases within the same
platform. Easy to access, easy to secure.
Flat files
Application data
Server logs
Internet APIs
Tableau Desktop
Server or Online
S3
Hive
EMR
Spark
AML
RDS
Redshift
Collect/Store Store/Analyze Data warehouse AnalysisRaw data
Machine Learning
14. Appendix: Cloud Data and BI Architecture
Connect and analyze in minutes.
Share insight with anyone securely.
Flat files
Application data
Server logs
Internet APIs
Tableau Desktop
Server or Online
S3
Hive
EMR
Spark
AML
RDS
Redshift
Collect/Store Store/Analyze Data warehouse AnalysisRaw data
Machine Learning
15. 1. Redshift
2. Aurora
3. EMR
4. RDS (MySql)
Appendix: Connecting Amazon and Tableau
Desktop Server + Online
Direct Connection or
Extract (In-Memory)
Connection from published
datasource or workbook
Workbook, connection or extract
EMR RDSRedshift
Aurora
16. 1. Tableau Server on EC2
2. Marketplace BYOL
3. Marketplace
Appendix: Hosting Tableau Server on AWS
Workbook, connection or extract
Desktop
Server
17. 1. How Tableau and Amazon Work Together
2. Connectivity to Amazon Databases
I. Amazon Redshift connector
II. Tuning your Redshift connection for performance
III. Explore Big Data Analytics with Amazon Redshift
3. Tableau Server on AWS
I. Deployment Guidelines and Best Practices
II. Running Tableau Server on Amazon AWS
More resources