Data Vault Vs Data Lake

Building an Effective Data Warehouse Architecture

Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.

Data Lakehouse, Data Mesh, and Data Fabric (r1)

Five Things to Consider About Data Mesh and Data Governance

Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about: Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles? Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.

Time to Talk about Data Mesh

LibbySchulze

This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.

Introduction to metadata management

Open Data Support

Agile Data Engineering - Intro to Data Vault Modeling (2016)

The document provides an introduction to Data Vault data modeling and discusses how it enables agile data warehousing. It describes the core structures of a Data Vault model including hubs, links, and satellites. It explains how the Data Vault approach provides benefits such as model agility, productivity, and extensibility. The document also summarizes the key changes in the Data Vault 2.0 methodology.

Data mesh

ManojKumarR41

A Work of Zhamak Dehghani Principal consultant ThoughtWorks https://martinfowler.com/articles/data-monolith-to-mesh.html https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.

Building Lakehouses on Delta Lake with SQL Analytics Primer

Data Warehousing Trends, Best Practices, and Future Outlook

Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn: - Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart - Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon - Step by step approach to building an effective data warehouse architecture - Common reasons for the failure of data warehouse implementations and how to avoid them

Data Vault Overview

Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology. If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me). Thank-you kindly, Daniel Linstedt

Data platform architecture

Sudheer Kondla

The document discusses data architecture solutions for solving real-time, high-volume data problems with low latency response times. It recommends a data platform capable of capturing, ingesting, streaming, and optionally storing data for batch analytics. The solution should provide fast data ingestion, real-time analytics, fast action, and quick time to value. Multiple data sources like logs, social media, and internal systems would be ingested using Apache Flume and Kafka and analyzed with Spark/Storm streaming. The processed data would be stored in HDFS, Cassandra, S3, or Hive. Kafka, Spark, and Cassandra are identified as key technologies for real-time data pipelines, stream analytics, and high availability persistent storage.

Wallchart - Data Warehouse Documentation Roadmap

David Walker

This document outlines the key components and processes involved in planning, designing, building, implementing and managing a data warehouse architecture. It includes sections on business requirements, data requirements, technical architecture, data modeling, ETL processes, testing, implementation, project management and documentation. The document provides a roadmap to guide an organization through each stage of developing an enterprise data warehouse.

Introduction To Data Vault - DAMA Oregon 2012

DAMA, Oregon Chapter, 2012 presentation - an introduction to Data Vault modeling. I will be covering parts of the methodology, comparison and contrast of issues in general for the EDW space. Followed by a brief technical introduction of the Data Vault modeling method. After the presentation i I will be providing a demonstration of the ETL loading layers, LIVE! You can find more on-line training at: http://LearnDataVault.com/training

Intro to Data Vault 2.0 on Snowflake

This document provides an introduction and overview of implementing Data Vault 2.0 on Snowflake. It begins with an agenda and the presenter's background. It then discusses why customers are asking for Data Vault and provides an overview of the Data Vault methodology including its core components of hubs, links, and satellites. The document applies Snowflake features like separation of workloads and agile warehouse scaling to support Data Vault implementations. It also addresses modeling semi-structured data and building virtual information marts using views.

Introduction to Data Warehousing

Gurpreet Singh Sachdeva

The document provides an introduction to data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-varying, and non-volatile collection of data used for organizational decision making. It describes key characteristics of a data warehouse such as maintaining historical data, facilitating analysis to improve understanding, and enabling better decision making. It also discusses dimensions, facts, ETL processes, and common data warehouse architectures like star schemas.

Building the Data Lake with Azure Data Factory and Data Lake Analytics

Khalid Salama

In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.

Data Engineering Basics

Catherine Kimani

Data modelling 101

Christopher Bradley

How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...

HostedbyConfluent

Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling. In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse. She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product. This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.

Modernizing to a Cloud Data Architecture

Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.

Webinar Data Mesh - Part 3

Jeffrey T. Pollock

The document discusses data mesh vs data fabric architectures. It defines data mesh as a decentralized data processing architecture with microservices and event-driven integration of enterprise data assets across multi-cloud environments. The key aspects of data mesh are that it is decentralized, processes data at the edge, uses immutable event logs and streams for integration, and can move all types of data reliably. The document then provides an overview of how data mesh architectures have evolved from hub-and-spoke models to more distributed designs using techniques like kappa architecture and describes some use cases for event streaming and complex event processing.

Introduction to Data Virtualization (session 1 from Packed Lunch Webinar Series)

This document summarizes a 6-session presentation on using data virtualization to solve key data integration challenges. The first session introduces data virtualization, covering how it can make business intelligence more agile, integrate big data, combine service-oriented architecture with data integration, enhance master data management and data warehousing, and create a single view of the customer. The presentation agenda is outlined and includes explanations of data virtualization, how it enhances existing architectures, demonstrations of its capabilities, Q&A, and next steps. Customer case studies show data virtualization delivering cost savings, productivity improvements, and faster access to new data sources and reports.

Owning Your Own (Data) Lake House

Data Con LA

Data Con LA 2020 Description In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake. Speaker Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning

Bridging the Last Mile: Getting Data to the People Who Need It

Watch full webinar here: https://bit.ly/3cUA0Qi Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it. Attend this session to learn: - The challenges organizations face when trying to get data to the business users in a timely manner - How Data Virtualization can accelerate time-to-value for an organization’s data assets - Examples of leading companies that used data virtualization to get the right data to the users at the right time

ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...

Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no. Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit. Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop. This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.

What's hot

Data Lakehouse, Data Mesh, and Data Fabric (r2)

Introduction to metadata management

Open Data Support

Agile Data Engineering - Intro to Data Vault Modeling (2016)

Data mesh

ManojKumarR41

Building Lakehouses on Delta Lake with SQL Analytics Primer

Data Warehousing Trends, Best Practices, and Future Outlook

Data Vault Overview

Data platform architecture

Sudheer Kondla

Wallchart - Data Warehouse Documentation Roadmap

David Walker

Introduction To Data Vault - DAMA Oregon 2012

Intro to Data Vault 2.0 on Snowflake

Introduction to Data Warehousing

Gurpreet Singh Sachdeva

Building the Data Lake with Azure Data Factory and Data Lake Analytics

Khalid Salama

Data Engineering Basics

Catherine Kimani

Data modelling 101

Christopher Bradley

How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...

HostedbyConfluent

Modernizing to a Cloud Data Architecture

Webinar Data Mesh - Part 3

Jeffrey T. Pollock

Introduction to Data Virtualization (session 1 from Packed Lunch Webinar Series)

Owning Your Own (Data) Lake House

Data Con LA

What's hot (20)

Data Lakehouse, Data Mesh, and Data Fabric (r2)

Introduction to metadata management

Agile Data Engineering - Intro to Data Vault Modeling (2016)

Data mesh

Building Lakehouses on Delta Lake with SQL Analytics Primer

Data Warehousing Trends, Best Practices, and Future Outlook

Data Vault Overview

Data platform architecture

Wallchart - Data Warehouse Documentation Roadmap

Introduction To Data Vault - DAMA Oregon 2012

Intro to Data Vault 2.0 on Snowflake

Introduction to Data Warehousing

Building the Data Lake with Azure Data Factory and Data Lake Analytics

Data Engineering Basics

Data modelling 101

How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...

Modernizing to a Cloud Data Architecture

Webinar Data Mesh - Part 3

Introduction to Data Virtualization (session 1 from Packed Lunch Webinar Series)

Owning Your Own (Data) Lake House

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Bridging the Last Mile: Getting Data to the People Who Need It

ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...

ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture

Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020. Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms. Data lakes will be built in cloud object storage. We’ll discuss the options there as well. Get this data point for your data lake journey.

Are You Killing the Benefits of Your Data Lake?

Watch the full webinar on-demand here: https://goo.gl/RL1ZSa Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system. So, what’s the solution? A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment Attend this session to learn: • Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake. • Why a multi-purpose data lake is essential in building a universal data delivery system. • How to build a logical multi-purpose data lake using data virtualization. Do not miss this opportunity to make your data lake project successful and beneficial.

When and How Data Lakes Fit into a Modern Data Architecture

DELWP’s Data Lake: Investing in Asset Wealth for Public/Community Benefit – B...

Amazon Web Services

In the last 10 years, Department of Environment, Land, Water and Planning (DELWP) has procured half a petabyte of aerial photography, satellite imagery, and point cloud data worth more than $45million. In addition, a range of faster products critical to the delivery of services are produced continuously. DELWP’s faster data needs is expected to grow overtime and an enterprise system to manage, publish and enable discovery and analysis of this growing catalogue is essential. Find out how DELWP is implementing pioneering data science strategies to successfully integrate disparate systems and data silos, reduce risks and increase compliance, and support a rapid pace of innovation to improve DELWP’s services to the Victorian public. Project Manager with DELWP’s Digital First program, Alena Moison will share the department’s journey through digital transformation, key successes and learning points, as well as how they worked with Bulletproof to implement solutions on AWS. Speakers: Giles Bill, Bulletproof Networks Sam Mason, Bulletproof Networks Alena Moison, Department of Environment, Land, Water and Planning

The Data Lake and Getting Buisnesses the Big Data Insights They Need

Dunn Solutions Group

Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR

Big Data, NoSQL, NewSQL & The Future of Data Management

Tony Bain

It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward. These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future. Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.

Data Mesh using Microsoft Fabric

Nathan Bijnens

The document discusses Microsoft's approach to implementing a data mesh architecture using their Azure Data Fabric. It describes how the Fabric can provide a unified foundation for data governance, security, and compliance while also enabling business units to independently manage their own domain-specific data products and analytics using automated data services. The Fabric aims to overcome issues with centralized data architectures by empowering lines of business and reducing dependencies on central teams. It also discusses how domains, workspaces, and "shortcuts" can help virtualize and share data across business units and data platforms while maintaining appropriate access controls and governance.

ADV Slides: Building and Growing Organizational Analytics with Data Lakes

Data Lakes: A Logical Approach for Faster Unified Insights

Watch full webinar here: https://bit.ly/3Cpn2bj Data lakes and data warehouses offer organizations centralized data delivery platforms. The recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI we discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and that 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important. In the recent report Logical Data Fabric to the Rescue Integrating Data Warehouses, Data Lakes, and Data Hubs by Rick van der Lans, we also discovered the importance of “time to insight and speed”. During this webinar we will discuss how a logical data fabric not only helps organizations have a holistic view of their data across multiple data lakes, data warehouses and data sources, but how it improves time to value. Attend & Learn: - How a Logical Data Fabric is the right approach to assist organizations to unify their data. - The advanced features of a Logical Data Fabric that assist with optimizing your queries irrespective of data source, whether the data is in a data lake, data warehouse or other source. - How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self service.

Future of Making Things

JC Davis

The document discusses how data accessibility is driving innovation in manufacturing through cloud and vault data management systems. It outlines how data has become more disruptive as information needs to be accessed in real-time across sites and stakeholders. Those not leveraging their data may fall behind. The presentation will demonstrate how a cloud-based vault provides real-time accessibility, analytics, and concurrent engineering across organizations and on mobile devices. Key benefits include data centricity, productivity, flexibility, and reduced costs compared to on-premise systems. Attendees will understand how partners can help leverage data for business optimization through these solutions.

Data Lake Acceleration vs. Data Virtualization - What’s the difference?

Watch full webinar here: https://bit.ly/3hgOSwm Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.

Data Virtualization enabled Data Fabric: Operationalize the Data Lake (APAC)

Watch full webinar here: https://bit.ly/3aIofv9 The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users. While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users. Attend this session to learn how big data fabric enabled by data virtualization: - Provides lightning fast self-service data access to business users - Centralizes data security, governance and data privacy - Fulfills the promise of data lakes to provide actionable insights

Options for Data Prep - A Survey of the Current Market

Dremio Corporation

Data comes in many shapes and sizes, and every company struggles to find ways to transform, validate, and enrich data for multiple purposes. The problem has been around as long as data, and the market has an overwhelming number of options. In this presentation we look at the problem and key options from vendors in the market today. Dremio is a new approach that eliminates the need for stand alone data prep tools.

Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization

Watch full webinar here: https://bit.ly/2XXAzU3 So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy? Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo. With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment. You might still not be convinced. Give us an opportunity and join us as we try to bust this myth! Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.

Data Lakes: A Logical Approach for Faster Unified Insights (ASEAN)

Watch full webinar here: https://bit.ly/3JBpwGm Data lakes and data warehouses offer organizations a centralized data delivery platform. From the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and that 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important. In the recent report Logical Data Fabric to the Rescue Integrating Data Warehouses, Data Lakes, and Data Hubs by Rick van der Lans, we also discovered the importance of “time to insight and speed”. During this webinar, we will discuss how a logical data fabric not only helps organizations have a holistic view of their data across multiple data lakes, data warehouses, and data sources but how it improves time to value. Catch this on-demand session & learn: - How a Logical Data Fabric is the right approach to assist organizations to unify their data. - The advanced features of a Logical Data Fabric that assist with optimizing your queries irrespective of data source, whether the data is in a data lake, data warehouse, or other sources. - How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.

The Great Lakes: How to Approach a Big Data Implementation

Inside Analysis

- Rick Stellwagen from Think Big, A Teradata Company, discussed best practices for implementing a data lake including establishing standards for data ingestion and metadata capture, developing a security plan, and planning for data discovery and reporting. - Analyst Robin Bloor asked questions about metadata management, data governance, and security for data lakes. Bloor noted that while data lakes are a new concept, best practices are needed as organizations move analytics and BI capabilities to this model. - Upcoming Briefing Room topics in 2015 will focus on big data, cloud computing, and innovators in technology.

Bridging the Last Mile: Getting Data to the People Who Need It (APAC)