With over 90% of today’s data generated in the last two years, the rate of data growth is showing no sign of slowing down. In this session, we step through the challenges and best practices for capturing data, understanding what data you own, driving insights, and predicting the future using AWS services. We frame the session and demonstrations around common pitfalls of building data lakes and how to successfully drive analytics and insights from data. We also discuss the architecture patterns brought together key AWS services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Kinesis, and Amazon Machine Learning. Discover the real-world application of data lakes for roles including data scientists and business users.
Stephen Moon, Sr. Solutions Architect, Amazon Web Services
James Juniper, Solution Architect for the Geo-Community Cloud, Natural Resources Canada
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
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.
DataEd Webinar: Reference & Master Data Management - Unlocking Business ValueDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
SAP Analytics Cloud combines BI, planning, predictive, and augmented analytics capabilities into one simple cloud environment. Powered by AI technologies and an in-memory database, it is one of the most advanced analytics solutions available today.
by Robbie Wright, HEad of Amazon S3 & Amazon Glacier Product Marketing, AWS
Learn from AWS on how we've designed S3 and Glacier to be durable, available, and massively scalable. Hear how customers are using these services to enhance the accessibility and usability of their data. We will also dive into the benefits of object storage, its applications, and some best practices to follow.
The document provides an overview of enterprise architecture foundations, including stakeholders, views and viewpoints, the enterprise continuum, and architecture repositories. It explains that the enterprise continuum classifies architecture and solution artifacts and consists of the architecture, solution, and enterprise continua. The architecture continuum shows the relationships between foundational, common, industry, and enterprise architectures, aiming to discover commonality and eliminate redundancy. It also establishes an architecture repository structure and tool standardization.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
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.
DataEd Webinar: Reference & Master Data Management - Unlocking Business ValueDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
SAP Analytics Cloud combines BI, planning, predictive, and augmented analytics capabilities into one simple cloud environment. Powered by AI technologies and an in-memory database, it is one of the most advanced analytics solutions available today.
by Robbie Wright, HEad of Amazon S3 & Amazon Glacier Product Marketing, AWS
Learn from AWS on how we've designed S3 and Glacier to be durable, available, and massively scalable. Hear how customers are using these services to enhance the accessibility and usability of their data. We will also dive into the benefits of object storage, its applications, and some best practices to follow.
The document provides an overview of enterprise architecture foundations, including stakeholders, views and viewpoints, the enterprise continuum, and architecture repositories. It explains that the enterprise continuum classifies architecture and solution artifacts and consists of the architecture, solution, and enterprise continua. The architecture continuum shows the relationships between foundational, common, industry, and enterprise architectures, aiming to discover commonality and eliminate redundancy. It also establishes an architecture repository structure and tool standardization.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Data saturday Oslo Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
The document discusses elastic data warehousing using Snowflake's cloud-based data warehouse as a service. Traditional data warehousing and NoSQL solutions are costly and complex to manage. Snowflake provides a fully managed elastic cloud data warehouse that can scale instantly. It allows consolidating all data in one place and enables fast analytics on diverse data sources at massive scale, without the infrastructure complexity or management overhead of other solutions. Customers have realized significantly faster analytics, lower costs, and the ability to easily add new workloads compared to their previous data platforms.
SAP S/4HANA Enterprise Management provides the lean digital core that serves as a foundation for business innovation and optimisation, enabling the enterprise to start the digital journey in line with its individual benefits/risk profile.
Cloud Migration, Application Modernization and Security for PartnersAmazon Web Services
As AWS continues to expand, enterprise customers are increasingly looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud.
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
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.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
The document discusses building data lakes with AWS. It recommends using Amazon S3 as the storage layer for the data lake due to its scalability, durability and integration with other AWS analytics services. It also recommends using AWS Glue to catalog and ingest data into the data lake through automated crawlers. This allows for easy discovery, querying and analysis of data in the lake.
Analyze key aspects to be considered before embarking on your cloud journey. The presentation outlines the strategies, approach, and choices that need to be made, to ensure a smooth transition to the cloud.
Data Migration Strategies PowerPoint Presentation SlidesSlideTeam
Data migration is a key consideration of any system implementation. Discuss the data transfer plans with this content ready Data Migration Strategies PowerPoint Presentation Slides. Data transformation plan PowerPoint complete deck is a systematic presentation which includes PPT slides such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Besides this, data transfer plan PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Showcase the process of selecting, preparing, extracting and transforming data using this professionally designed information migration plan presentation design.
Accelerate Cloud Migration to AWS Cloud with Cognizant Cloud StepsAmazon Web Services
The document discusses strategies for accelerating cloud migration. It recommends building a Cloud Center of Excellence to develop in-house skills in cloud technologies. This includes adopting an agile development approach. The document also recommends performing portfolio discovery and analysis to prioritize applications for migration. Key decisions involve determining the best migration patterns for applications based on refactoring, rehosting, replatforming etc. An iterative approach is suggested to continuously learn, improve and optimize the migration process through establishing a "migration factory".
Discover how the Well-Architected Framework can help you build secure, resilient, and efficient infrastructures for your applications.
Gain the skills you need to evaluate the AWS platform and leave the day with knowledge on how to implement your AWS footprint and guidance on best practice design.
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...Amazon Web Services
Whether you are moving a small application or entire datacenters, migrating to the cloud can be a complex process. In this session, we will share some of the common challenges that our customers face on their journey to the cloud and discuss how these challenges can be overcome. We will outline the patterns of success that we have observed from partnering with hundreds of customers on their large-scale migrations as well as highlight the mechanisms we have created to help our customers migrate faster.
About the Event:
AWS Transformation Day is designed for enterprise organizations migrating to the cloud to become more responsive, agile and innovative, while staying secure and compliant. Join us for this one-day event and we’ll share our experiences of helping enterprise customers accelerate the pace of migration and adoption of strategic services.
Who should attend?
This event is recommended for IT and business leaders who are looking to create sustainable benefits and a competitive advantage by using the AWS Cloud. CIOs, CTOs, CISOs, CDOs, CFOs, IT leaders and IT professionals, enterprise developers, business decision makers, and finance executives.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Introducing Snowflake, an elastic data warehouse delivered as a service in the cloud. It aims to simplify data warehousing by removing the need for customers to manage infrastructure, scaling, and tuning. Snowflake uses a multi-cluster architecture to provide elastic scaling of storage, compute, and concurrency. It can bring together structured and semi-structured data for analysis without requiring data transformation. Customers have seen significant improvements in performance, cost savings, and the ability to add new workloads compared to traditional on-premises data warehousing solutions.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document discusses strategies for executing a large-scale migration to AWS. It outlines establishing a cloud enablement team and AWS landing zone to provide a secure, scalable multi-account environment. Application migration strategies discussed include discovery, determining the migration path, rehosting/lift and shift, and replatforming/lift and reshape. Specific migration tools and services mentioned include AWS Application Discovery Service, VMware HCX, AWS Server Migration Service, and AWS Database Migration Service.
BIAN Applied to Open Banking - Thoughts on Architecture and ImplementationBiao Hao
At the BIAN Open Day in NYC November 12, 2019, we shared our thoughts on how BIAN Value Chain business areas, Channels, Customers, Products and Operations, provide a context for addressing Open Banking capabilities in a more systematic way, and the implications the decoupled Value Chain have on business models and reference architecture. Sample use cases such as account information and account aggregation, their mapping to related BIAN service domains, and implementation using microservices and pattern for performance are also discussed.
The document discusses building data lakes and analytics on AWS. It provides an overview of challenges with big data like increasing data variety and growth. It then describes how AWS services like S3, Glue, Athena, EMR, and Redshift can be used to address these challenges by enabling quick ingestion of diverse data types, metadata management, and running analytics tools on curated datasets. The document emphasizes storing raw data immutable and using tiered storage for cost optimization. It outlines using the right AWS service based on user roles and discusses how data lakes and data warehouses are complementary.
The document discusses building data lakes and analytics on AWS. It provides an overview of challenges posed by big data including volume, velocity, variety and veracity of data. It then describes how AWS services like S3, Glue and Athena can help address these challenges by allowing quick ingestion and storage of raw data in its original format. The document also discusses best practices for preparing and analyzing data in the lake using services like EMR, Redshift and SageMaker to derive insights and drive machine learning models.
Data saturday Oslo Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
The document discusses elastic data warehousing using Snowflake's cloud-based data warehouse as a service. Traditional data warehousing and NoSQL solutions are costly and complex to manage. Snowflake provides a fully managed elastic cloud data warehouse that can scale instantly. It allows consolidating all data in one place and enables fast analytics on diverse data sources at massive scale, without the infrastructure complexity or management overhead of other solutions. Customers have realized significantly faster analytics, lower costs, and the ability to easily add new workloads compared to their previous data platforms.
SAP S/4HANA Enterprise Management provides the lean digital core that serves as a foundation for business innovation and optimisation, enabling the enterprise to start the digital journey in line with its individual benefits/risk profile.
Cloud Migration, Application Modernization and Security for PartnersAmazon Web Services
As AWS continues to expand, enterprise customers are increasingly looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud.
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
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.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
The document discusses building data lakes with AWS. It recommends using Amazon S3 as the storage layer for the data lake due to its scalability, durability and integration with other AWS analytics services. It also recommends using AWS Glue to catalog and ingest data into the data lake through automated crawlers. This allows for easy discovery, querying and analysis of data in the lake.
Analyze key aspects to be considered before embarking on your cloud journey. The presentation outlines the strategies, approach, and choices that need to be made, to ensure a smooth transition to the cloud.
Data Migration Strategies PowerPoint Presentation SlidesSlideTeam
Data migration is a key consideration of any system implementation. Discuss the data transfer plans with this content ready Data Migration Strategies PowerPoint Presentation Slides. Data transformation plan PowerPoint complete deck is a systematic presentation which includes PPT slides such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Besides this, data transfer plan PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Showcase the process of selecting, preparing, extracting and transforming data using this professionally designed information migration plan presentation design.
Accelerate Cloud Migration to AWS Cloud with Cognizant Cloud StepsAmazon Web Services
The document discusses strategies for accelerating cloud migration. It recommends building a Cloud Center of Excellence to develop in-house skills in cloud technologies. This includes adopting an agile development approach. The document also recommends performing portfolio discovery and analysis to prioritize applications for migration. Key decisions involve determining the best migration patterns for applications based on refactoring, rehosting, replatforming etc. An iterative approach is suggested to continuously learn, improve and optimize the migration process through establishing a "migration factory".
Discover how the Well-Architected Framework can help you build secure, resilient, and efficient infrastructures for your applications.
Gain the skills you need to evaluate the AWS platform and leave the day with knowledge on how to implement your AWS footprint and guidance on best practice design.
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...Amazon Web Services
Whether you are moving a small application or entire datacenters, migrating to the cloud can be a complex process. In this session, we will share some of the common challenges that our customers face on their journey to the cloud and discuss how these challenges can be overcome. We will outline the patterns of success that we have observed from partnering with hundreds of customers on their large-scale migrations as well as highlight the mechanisms we have created to help our customers migrate faster.
About the Event:
AWS Transformation Day is designed for enterprise organizations migrating to the cloud to become more responsive, agile and innovative, while staying secure and compliant. Join us for this one-day event and we’ll share our experiences of helping enterprise customers accelerate the pace of migration and adoption of strategic services.
Who should attend?
This event is recommended for IT and business leaders who are looking to create sustainable benefits and a competitive advantage by using the AWS Cloud. CIOs, CTOs, CISOs, CDOs, CFOs, IT leaders and IT professionals, enterprise developers, business decision makers, and finance executives.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Introducing Snowflake, an elastic data warehouse delivered as a service in the cloud. It aims to simplify data warehousing by removing the need for customers to manage infrastructure, scaling, and tuning. Snowflake uses a multi-cluster architecture to provide elastic scaling of storage, compute, and concurrency. It can bring together structured and semi-structured data for analysis without requiring data transformation. Customers have seen significant improvements in performance, cost savings, and the ability to add new workloads compared to traditional on-premises data warehousing solutions.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document discusses strategies for executing a large-scale migration to AWS. It outlines establishing a cloud enablement team and AWS landing zone to provide a secure, scalable multi-account environment. Application migration strategies discussed include discovery, determining the migration path, rehosting/lift and shift, and replatforming/lift and reshape. Specific migration tools and services mentioned include AWS Application Discovery Service, VMware HCX, AWS Server Migration Service, and AWS Database Migration Service.
BIAN Applied to Open Banking - Thoughts on Architecture and ImplementationBiao Hao
At the BIAN Open Day in NYC November 12, 2019, we shared our thoughts on how BIAN Value Chain business areas, Channels, Customers, Products and Operations, provide a context for addressing Open Banking capabilities in a more systematic way, and the implications the decoupled Value Chain have on business models and reference architecture. Sample use cases such as account information and account aggregation, their mapping to related BIAN service domains, and implementation using microservices and pattern for performance are also discussed.
The document discusses building data lakes and analytics on AWS. It provides an overview of challenges with big data like increasing data variety and growth. It then describes how AWS services like S3, Glue, Athena, EMR, and Redshift can be used to address these challenges by enabling quick ingestion of diverse data types, metadata management, and running analytics tools on curated datasets. The document emphasizes storing raw data immutable and using tiered storage for cost optimization. It outlines using the right AWS service based on user roles and discusses how data lakes and data warehouses are complementary.
The document discusses building data lakes and analytics on AWS. It provides an overview of challenges posed by big data including volume, velocity, variety and veracity of data. It then describes how AWS services like S3, Glue and Athena can help address these challenges by allowing quick ingestion and storage of raw data in its original format. The document also discusses best practices for preparing and analyzing data in the lake using services like EMR, Redshift and SageMaker to derive insights and drive machine learning models.
The document discusses building data lakes and analytics on AWS. It provides an overview of challenges posed by big data including volume, velocity, variety and veracity of data. It then describes how AWS services like S3, Glue and Athena can help address these challenges by allowing quick ingestion and storage of raw data in its original format. The document also discusses best practices for preparing and analyzing data in the lake using services like EMR, Redshift and SageMaker to derive insights and drive machine learning models.
Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...Amazon Web Services
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
The document discusses data lake architectures on AWS. It defines a data lake as a centralized storage platform capable of storing heterogeneous data sets at virtually limitless scale. It describes how AWS services like S3, Glue, Athena, EMR, Redshift, and Kinesis can be used to build data lakes for storing, cataloging, processing, analyzing and gaining insights from large volumes of diverse data. Examples of using these services for clickstream analytics, real-time analytics, machine learning, and reducing total cost of ownership are also provided.
The document discusses big data analytics and machine learning on AWS. It describes what big data is and the 3Vs of big data - variety, velocity, and volume. It provides examples of AWS services that can be used for big data analytics like S3, Redshift, EMR, Athena, and Kinesis. It also provides examples of customers like Sysco, FINRA, and Nasdaq that are using AWS services to build data lakes and leverage big data analytics.
The document discusses building data lakes and analytics on AWS. It describes how data lakes extend the traditional approach of data warehousing by allowing storage and analysis of structured, semi-structured, and unstructured data at massive scales cost effectively. It provides an overview of various AWS services that can be used for data ingestion, storage, processing, analysis and machine learning with data lakes.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation 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.
Data Lake Implementation: Processing and Querying Data in Place (STG204-R1) -...Amazon Web Services
Flexibility is key when building and scaling a data lake. The analytics solutions you use in the future will almost certainly be different from the ones you use today, and choosing the right storage architecture gives you the agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore best practices for building a data lake in Amazon S3 and Amazon Glacier for leveraging an entire array of AWS, open source, and third-party analytics tools. We explore use cases for traditional analytics tools, including Amazon EMR and AWS Glue, as well as query-in-place tools like Amazon Athena, Amazon Redshift Spectrum, Amazon S3 Select, and Amazon Glacier Select.
Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...Amazon Web Services
This document provides a summary of a presentation on building data lakes and analytics on AWS. It discusses:
- The challenges of big data including volume, velocity, variety and veracity.
- How an AWS data lake can address these challenges by quickly ingesting and storing any type of data while providing insights, security and the ability to run the right analytics tools without data movement.
- Key components of a data lake on AWS including storage, data catalog, analytics, machine learning capabilities, and tools for real-time and traditional data movement.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
The document discusses data lakes on AWS. It describes how data lakes allow organizations to capture and analyze large amounts of structured and unstructured data at low costs. Key services for building data lakes on AWS include Amazon S3 for storage, AWS Glue for data cataloging and ETL, Amazon Athena for interactive querying, and Amazon QuickSight for visualization and analytics. The document outlines how these services provide scalable, secure, cost-effective solutions for data lakes that help organizations drive business value from their data.
The AWS Big Data services are inherently built to run at @scale. In this session, you will learn how to develop an enterprise scale big data application using AWS services such as Amazon EMR, Amazon Redshift & Redshift Spectrum, Amazon Athena, Amazon Elasticsearch Service, Amazon Kinesis, Amazon QuickSight and AWS Glue. This session will also cover different architectural patterns and customer use cases.
Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...Amazon Web Services
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes, and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
This document discusses implementing a data lake on AWS to securely store, categorize, and analyze all types of data in a centralized repository. It describes key attributes of a data lake like decoupled storage and compute, rapid ingestion and transformation, and schema on read. It then outlines various AWS services that can be used to build a data lake like S3, Athena, EMR, Redshift, Glue, and Kinesis. It provides examples of streaming IoT data into a data lake and running queries and analytics on the data.
Similar to Build Data Lakes and Analytics on AWS: Patterns & Best Practices (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
40. Federal Geospatial Platform
The Leader in Geospatial for the Government of Canada
Easy access to GC “AAA” Geospatial Data
Standards-based formats
RESTful web services
ISO Metadata
OGC
Simple workflow to assess, visualize and publish
Re-usable viewer on GitHub
Collaborative Mapping Environment on Esri’s ArcGIS Online
FGP Geo-Community Cloud Platform as a Service (PaaS) on AWS
A GC standards compliant Geospatial Platform On-Demand
41. …OBJECTIVES 2018-20
Make Government of Canada Earth Observation information more easily
available to Canadians
Access, Visualization and Analysis functionality for EO and Spatial
Information using the Federal Geospatial Platform (GC Tool)
Enhanced imagery visualization options (past/present time-series)
On-the-fly imagery processing (projection, class renderings, dynamic mosaics)
Geoanalytics against near real time GC imagery on-demand
46. FGP Geo-Community Cloud
2017-18 Proof of Concept on AWS (complete)
2018-19 Foundation Laid – SSC Brokered Cloud
FGP “Core Solution Stack”
2019-20
On Demand Processing Capabilities via API Gateway
Geospatial Managed Storage - host your own geospatial data
Support multiple “portals” from a common GC ecosystem
Concurrently…
Innovation Zone
Sandbox Enviros for broad-based Geospatial R&D P/T/A
AI and Machine Learning against Geospatial + EO integrated with FGP Platform as a
Service
47. Geo-Community Cloud – AWS Services
ca-canada-1a
Public Subnet
Private Subnet
Private Subnet
App Tier
Web Tier
DB Tier
Amazon Route 53
WAF Web Application
Firewall
Internet Gateway
Classic Load Balancer
EC2 Instances
Application Load Balancer
Elastic Block Storage
NAT Gateway
Database
S3 Bucket
Glacier Storage
NAT Gateway
NAT Gateway
Auto Scaling
Accessible
Authoritative
800 datasets and growing
Think enterprise, not silos
Build once, use many times, for the benefit of all, including common approaches and solutions
Think horizontal not vertical
Cloud First
Use existing GC standards and tools
Context
Climate Change
Cumulative Effects
Current launch window is Feb 18-24 2019
Qty 3 satellites all on same SpaceX Falcon 9 launch vehicle from Vandenberg Air Force base in California.
The 3 satellites will be released 3 minutes apart and then later once ‘fully woken up’ they will be moved into final position which is evenly spaced. (120 degrees apart)
1.4 TBytes/day, just under 1 PByte/year
RCM data policy is TBD but even free data for the public requires individual accounts due to Remote Sensing Space Systems Act (RSSSA). They will most likely be valuable added products that could be open (FGP/OpenMaps), but qty is unknown.
Question is this… How will it be made useful?
We decided to tackle the technical challenge of processing EO data our way – to create web services, which make it possible for everyone to get data in their favorite GIS applications using standard WMS and WCS services.
Also, processing on demand with Esri’s Image Server and GeoAnalytics Server.