The document discusses Azure Synapse Analytics, a limitless analytics service that delivers insights from all data sources with unmatched speed. It provides a unified experience with Azure Synapse Studio for SQL, Apache Spark, pipelines, and BI/AI integration. Key capabilities include cloud-scale analytics, a modern data warehouse with SQL and Spark runtimes, and an integrated platform for AI/BI/continuous intelligence. Synapse Studio is the main interface with hubs for overview, data exploration, development, orchestration, and management.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
These slides - based on the webinar - shed light on how business stakeholders make the most of information from their big data environments and the requirements those stakeholders have to turn big data into business impact.
Using recent big data end-user research from leading IT analyst firm Enterprise Management (EMA), data from Vertica’s recent benchmarks on SQL on Hadoop, and firsthand customer experiences, viewers will learn:
- Use cases where end users around the world are using big data in their organizations
- How maturity with big data strategies impact why and how business stakeholders use information from their big data environments
- How Vertica empowers the use of information from big data environments
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Making the Case for Hadoop in a Large Enterprise-British AirwaysDataWorks Summit
Making the Case for Hadoop in a Large Enterprise
British Airways
Alan Spanos
Data Exploitation Manager
British Airways
Jay Aubby
Architect
British Airways
Extended Data Warehouse - A New Data Architecture for Modern BI with Claudia ...Denodo
This presentation has been extracted from a full webinar organized by Denodo. To learn more click here: http://bit.ly/1FOMD90
Big Data, Internet of Things, Data Lakes, Streaming Analytics, Machine Learning… these are just a few of the buzzwords being thrown around in the world of data management today. They provide us with new sources of data, new forms of analytics, and new ways of storing, managing and utilizing our data. The reality however, is that traditional Data Warehouse architectures are no longer able to handle many of these new technologies and a new data architecture is required.
So what does the new architecture look like? Does the enterprise data warehouse still have a role? Where do these new technologies fit in? How can business users easily and quickly access the various sources of data and analytic results at the right time to make the right decisions in this new world order?
Dr. Claudia Imhoff addresses these questions and presents the Extended Data Warehouse architecture (XDW), demonstrating the need for each component and how an enterprise combines these into appropriate workflows for proper decision support.
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional BI systems have limitations in handling big data as they are not designed for unstructured data and have data latency issues. A business data lake provides a new approach by storing all raw structured and unstructured data in a single environment at low cost. This allows for near real-time analysis on any data from any source to gain insights.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
These slides - based on the webinar - shed light on how business stakeholders make the most of information from their big data environments and the requirements those stakeholders have to turn big data into business impact.
Using recent big data end-user research from leading IT analyst firm Enterprise Management (EMA), data from Vertica’s recent benchmarks on SQL on Hadoop, and firsthand customer experiences, viewers will learn:
- Use cases where end users around the world are using big data in their organizations
- How maturity with big data strategies impact why and how business stakeholders use information from their big data environments
- How Vertica empowers the use of information from big data environments
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Making the Case for Hadoop in a Large Enterprise-British AirwaysDataWorks Summit
Making the Case for Hadoop in a Large Enterprise
British Airways
Alan Spanos
Data Exploitation Manager
British Airways
Jay Aubby
Architect
British Airways
Extended Data Warehouse - A New Data Architecture for Modern BI with Claudia ...Denodo
This presentation has been extracted from a full webinar organized by Denodo. To learn more click here: http://bit.ly/1FOMD90
Big Data, Internet of Things, Data Lakes, Streaming Analytics, Machine Learning… these are just a few of the buzzwords being thrown around in the world of data management today. They provide us with new sources of data, new forms of analytics, and new ways of storing, managing and utilizing our data. The reality however, is that traditional Data Warehouse architectures are no longer able to handle many of these new technologies and a new data architecture is required.
So what does the new architecture look like? Does the enterprise data warehouse still have a role? Where do these new technologies fit in? How can business users easily and quickly access the various sources of data and analytic results at the right time to make the right decisions in this new world order?
Dr. Claudia Imhoff addresses these questions and presents the Extended Data Warehouse architecture (XDW), demonstrating the need for each component and how an enterprise combines these into appropriate workflows for proper decision support.
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional BI systems have limitations in handling big data as they are not designed for unstructured data and have data latency issues. A business data lake provides a new approach by storing all raw structured and unstructured data in a single environment at low cost. This allows for near real-time analysis on any data from any source to gain insights.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
This document summarizes a presentation given by Jim Vogt, President and CEO of Zettaset, on making Hadoop work in business units. It outlines how customer focus is shifting to higher layers of the big data stack like analytics and applications. While Hadoop's value proposition has expanded, enterprises face issues with security, reliability, integration and reliance on professional services. The document discusses use cases in financial services, healthcare and retail payments and how meeting requirements like data security, availability and multi-tenancy is key to Hadoop adoption. It concludes that focus needs to be on business applications over database mechanics with comprehensive security and simplified integration into existing systems and processes.
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Data Services and the Modern Data Ecosystem (Middle East)Denodo
Watch full webinar here: https://bit.ly/3xdSTIU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management. Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled a growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo.
Join us for our upcoming Middle East Webinar series episode, “Data Services and the Modern Data Ecosystem,” presented by Chief Evangelist MEA, Alexey Sidorov. Tune-in as we explore how a business can easily support and manage a Data Service ecosystem, providing a more flexible approach for information sharing supporting an ever diverse community of consumers.
Watch on-demand this webinar to learn:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary.
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
This document discusses IBM's industry data models and how they can be used with IBM's data lake architecture. It provides an overview of the data lake components and how the models integrate by being deployed to the data lake catalog and repositories. The models include predefined business vocabularies, data warehouse designs, and other reference materials that can accelerate analytics projects and provide governance.
Data Virtualization - Enabling Next Generation AnalyticsDenodo
Watch full webinar here: https://goo.gl/3gNMXX
Webinar featuring guest speaker Boris Evelson, Vice President, Principal Analyst at Forrester Research and Lakshmi Randall, Director of Product Marketing, Denodo.
Majority of enterprises today are data-aware. Being data-aware, or even data-driven, however, is not enough. Are your data-driven applications providing contextual and actionable insight? Are your analytics applications driving tangible business outcomes? Are you deriving insights from all the enterprise data? Enter Systems Of Insight (SOI), Forrester's latest analytical framework for insights-driven businesses.
In this webinar you will learn about the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. Specifically the webinar will explore roles data virtualization (aka Data Fabric) plays in modern SOI architectures such as:
• A single virtual catalog / view on all enterprise data sources including data lakes.
• A more agile and flexible virtual enterprise data warehouse.
• A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric).
Hadoop 2015: what we larned -Think Big, A Teradata CompanyDataWorks Summit
Think Big is expanding its open source consulting internationally by opening an office in London to serve as its international hub. It is aggressively hiring to support this expansion into areas like data engineering, data science, and sales. Rick Farnell, co-founder and SVP of Think Big, will lead the new international practice. The first phase of expansion will include offices in Dublin, Munich, and Mumbai to serve the European and Indian markets.
This presentation will discuss the stories of 3 companies that span different industries; what challenges they faced and how cloud analytics solved for them; what technologies were implemented to solve the challenges; and how they were able to benefit from their new cloud analytics environments.
The objectives of this session include:
• Detail and explain the key benefits and advantages of moving BI and analytics workloads to the cloud, and why companies shouldn’t wait any longer to make their move.
• Compare the different analytics cloud options companies have, and the pros and cons of each.
• Describe some of the challenges companies may face when moving their analytics to the cloud, and what they need to prepare for.
• Provide the case studies of three companies, what issues they were solving for, what technologies they implemented and why, and how they benefited from their new solutions.
• Learn what to look for one considering a partner and trusted advisor to assist with an analytics cloud migration.
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Emergence of MongoDB as an Enterprise Data HubMongoDB
Emergence of MongoDB as an Enterprise Data Hub, presented by Dylan Tong, Sr. Solutions Architect, MongoDB at MongoDB Evenings Seattle at the Seattle Public Library on October 6, 2015.
Get Started with Cloudera’s Cyber SolutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Join Cloudera, StreamSets, and Arcadia Data as we show you first hand how we have made it easier to get your first use case up and running. During this session you will learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
3 things to learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
Solution Centric Architectural Presentation - Implementing a Logical Data War...Denodo
Watch full webinar here: https://bit.ly/3H5AYZf
Implementing a logical data fabric as an architecture makes absolute sense when you have data spread across various sources in the cloud, including data warehouses, data lakes and even realtime data. In this session our customer will discuss the ways in which they implemented Denodo as a logical data fabric and how it helped them reduce risk and speed up time to access data.
How to create a successful data archiving strategy for your Salesforce Org.DataArchiva
Data archiving has been proved to be one of the most effective approaches when it comes to managing Salesforce data growth and storage space. You can seamlessly archive your Salesforce data using Big Objects and save significant data storage costs.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Logical Data Fabric: Maturing Implementation from Small to Big (APAC)Denodo
Watch full webinar here: https://bit.ly/3w1E1Nx
This presentation featuring guest speaker Deb Mukherji, Practice Head – Data Analytics & AI from our partner firm Tech Mahindra provides practical tips on how to start and later expand a logical data fabric implementation. Implementing a logical data fabric is not a one-shot deal. It is a journey. How do you start small, demonstrate ROI, and then expand to additional use cases? This presentation provides practical tips on how to start and later expand a logical data fabric implementation.
Don't miss out, register for this complimentary webinar now to learn:
- The enterprise data management challenges.
- Advantages of a logical data fabric over a physical data warehouse.
- How to architect a logical data fabric using data virtualization.
Applying Big Data Superpowers to HealthcarePaul Boal
When I see a data analyst quickly transform and drill through a new pile of data to uncover a keen insight, I feel like I'm watching a new movie from the Marvel universe. If you haven't explored and learned to apply cloud, big data, streaming data, and rapid analytics techniques, then you haven't uncovered your superpowers, yet. Here's how you can get started.
Analyst Webinar: Best Practices In Enabling Data-Driven Decision MakingDenodo
Watch full webinar here: https://bit.ly/37YkgN4
This presentation looks at the trends that are emerging from companies on their journeys to becoming data-driven enterprises.
These trends are taken from a survey of 500 companies and highlight critical success factors, what companies are doing, their progress so far and their plans going forward. It also looks at the role that data virtualization has within the data driven enterprise.
During the session we'll address:
- What is a data-driven enterprise?
- What are the critical success factors?
- What are companies doing to create a data-driven enterprise and why?
- What progress are they making?
- What are the plans on people, process and technologies?
- Why is data virtualization central to provisioning and accessing data in a data-driven enterprise?
- How should you get started?
This document discusses the changing landscape of data management as the volume of data grows exponentially. It introduces the concept of "Total Data" which advocates a flexible approach to data management that processes all applicable data across operational databases, data warehouses, Hadoop, and archives. The trends driving more data include greater understanding of data's value, improved processing capabilities, and the rise of machine-generated data. New approaches are needed to virtually access and analyze large datasets at lower costs. RainStor provides a specialized database that can reduce, retain, and retrieve large volumes of historical structured data at 10x lower costs than alternatives.
Agile Data Management with Enterprise Data Fabric (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3juxqaw
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
- and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.
The document discusses the challenges of maintaining separate data lake and data warehouse systems. It notes that businesses need to integrate these areas to overcome issues like managing diverse workloads, providing consistent security and user management across uses cases, and enabling data sharing between data science and business analytics teams. An integrated system is needed that can support both structured analytics and big data/semi-structured workloads from a single platform.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
This document summarizes a presentation given by Jim Vogt, President and CEO of Zettaset, on making Hadoop work in business units. It outlines how customer focus is shifting to higher layers of the big data stack like analytics and applications. While Hadoop's value proposition has expanded, enterprises face issues with security, reliability, integration and reliance on professional services. The document discusses use cases in financial services, healthcare and retail payments and how meeting requirements like data security, availability and multi-tenancy is key to Hadoop adoption. It concludes that focus needs to be on business applications over database mechanics with comprehensive security and simplified integration into existing systems and processes.
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Data Services and the Modern Data Ecosystem (Middle East)Denodo
Watch full webinar here: https://bit.ly/3xdSTIU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management. Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled a growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo.
Join us for our upcoming Middle East Webinar series episode, “Data Services and the Modern Data Ecosystem,” presented by Chief Evangelist MEA, Alexey Sidorov. Tune-in as we explore how a business can easily support and manage a Data Service ecosystem, providing a more flexible approach for information sharing supporting an ever diverse community of consumers.
Watch on-demand this webinar to learn:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary.
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
This document discusses IBM's industry data models and how they can be used with IBM's data lake architecture. It provides an overview of the data lake components and how the models integrate by being deployed to the data lake catalog and repositories. The models include predefined business vocabularies, data warehouse designs, and other reference materials that can accelerate analytics projects and provide governance.
Data Virtualization - Enabling Next Generation AnalyticsDenodo
Watch full webinar here: https://goo.gl/3gNMXX
Webinar featuring guest speaker Boris Evelson, Vice President, Principal Analyst at Forrester Research and Lakshmi Randall, Director of Product Marketing, Denodo.
Majority of enterprises today are data-aware. Being data-aware, or even data-driven, however, is not enough. Are your data-driven applications providing contextual and actionable insight? Are your analytics applications driving tangible business outcomes? Are you deriving insights from all the enterprise data? Enter Systems Of Insight (SOI), Forrester's latest analytical framework for insights-driven businesses.
In this webinar you will learn about the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. Specifically the webinar will explore roles data virtualization (aka Data Fabric) plays in modern SOI architectures such as:
• A single virtual catalog / view on all enterprise data sources including data lakes.
• A more agile and flexible virtual enterprise data warehouse.
• A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric).
Hadoop 2015: what we larned -Think Big, A Teradata CompanyDataWorks Summit
Think Big is expanding its open source consulting internationally by opening an office in London to serve as its international hub. It is aggressively hiring to support this expansion into areas like data engineering, data science, and sales. Rick Farnell, co-founder and SVP of Think Big, will lead the new international practice. The first phase of expansion will include offices in Dublin, Munich, and Mumbai to serve the European and Indian markets.
This presentation will discuss the stories of 3 companies that span different industries; what challenges they faced and how cloud analytics solved for them; what technologies were implemented to solve the challenges; and how they were able to benefit from their new cloud analytics environments.
The objectives of this session include:
• Detail and explain the key benefits and advantages of moving BI and analytics workloads to the cloud, and why companies shouldn’t wait any longer to make their move.
• Compare the different analytics cloud options companies have, and the pros and cons of each.
• Describe some of the challenges companies may face when moving their analytics to the cloud, and what they need to prepare for.
• Provide the case studies of three companies, what issues they were solving for, what technologies they implemented and why, and how they benefited from their new solutions.
• Learn what to look for one considering a partner and trusted advisor to assist with an analytics cloud migration.
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Emergence of MongoDB as an Enterprise Data HubMongoDB
Emergence of MongoDB as an Enterprise Data Hub, presented by Dylan Tong, Sr. Solutions Architect, MongoDB at MongoDB Evenings Seattle at the Seattle Public Library on October 6, 2015.
Get Started with Cloudera’s Cyber SolutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Join Cloudera, StreamSets, and Arcadia Data as we show you first hand how we have made it easier to get your first use case up and running. During this session you will learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
3 things to learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
Solution Centric Architectural Presentation - Implementing a Logical Data War...Denodo
Watch full webinar here: https://bit.ly/3H5AYZf
Implementing a logical data fabric as an architecture makes absolute sense when you have data spread across various sources in the cloud, including data warehouses, data lakes and even realtime data. In this session our customer will discuss the ways in which they implemented Denodo as a logical data fabric and how it helped them reduce risk and speed up time to access data.
How to create a successful data archiving strategy for your Salesforce Org.DataArchiva
Data archiving has been proved to be one of the most effective approaches when it comes to managing Salesforce data growth and storage space. You can seamlessly archive your Salesforce data using Big Objects and save significant data storage costs.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Logical Data Fabric: Maturing Implementation from Small to Big (APAC)Denodo
Watch full webinar here: https://bit.ly/3w1E1Nx
This presentation featuring guest speaker Deb Mukherji, Practice Head – Data Analytics & AI from our partner firm Tech Mahindra provides practical tips on how to start and later expand a logical data fabric implementation. Implementing a logical data fabric is not a one-shot deal. It is a journey. How do you start small, demonstrate ROI, and then expand to additional use cases? This presentation provides practical tips on how to start and later expand a logical data fabric implementation.
Don't miss out, register for this complimentary webinar now to learn:
- The enterprise data management challenges.
- Advantages of a logical data fabric over a physical data warehouse.
- How to architect a logical data fabric using data virtualization.
Applying Big Data Superpowers to HealthcarePaul Boal
When I see a data analyst quickly transform and drill through a new pile of data to uncover a keen insight, I feel like I'm watching a new movie from the Marvel universe. If you haven't explored and learned to apply cloud, big data, streaming data, and rapid analytics techniques, then you haven't uncovered your superpowers, yet. Here's how you can get started.
Analyst Webinar: Best Practices In Enabling Data-Driven Decision MakingDenodo
Watch full webinar here: https://bit.ly/37YkgN4
This presentation looks at the trends that are emerging from companies on their journeys to becoming data-driven enterprises.
These trends are taken from a survey of 500 companies and highlight critical success factors, what companies are doing, their progress so far and their plans going forward. It also looks at the role that data virtualization has within the data driven enterprise.
During the session we'll address:
- What is a data-driven enterprise?
- What are the critical success factors?
- What are companies doing to create a data-driven enterprise and why?
- What progress are they making?
- What are the plans on people, process and technologies?
- Why is data virtualization central to provisioning and accessing data in a data-driven enterprise?
- How should you get started?
This document discusses the changing landscape of data management as the volume of data grows exponentially. It introduces the concept of "Total Data" which advocates a flexible approach to data management that processes all applicable data across operational databases, data warehouses, Hadoop, and archives. The trends driving more data include greater understanding of data's value, improved processing capabilities, and the rise of machine-generated data. New approaches are needed to virtually access and analyze large datasets at lower costs. RainStor provides a specialized database that can reduce, retain, and retrieve large volumes of historical structured data at 10x lower costs than alternatives.
Agile Data Management with Enterprise Data Fabric (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3juxqaw
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
- and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.
The document discusses the challenges of maintaining separate data lake and data warehouse systems. It notes that businesses need to integrate these areas to overcome issues like managing diverse workloads, providing consistent security and user management across uses cases, and enabling data sharing between data science and business analytics teams. An integrated system is needed that can support both structured analytics and big data/semi-structured workloads from a single platform.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
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.
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
The document provides an overview of modern data architectures, including data lakes, data warehouses, data lakehouses, and data meshes. It discusses the challenges of big and diverse data, as well as empowering teams through decentralized approaches. The key considerations in determining a data strategy are understanding your use cases and data types, empowering both technology and people, and removing barriers to insights. Starting points may be strategic, focusing on goals, or tactical, focusing on immediate needs.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
IBM Cloud Pak for Data is a unified platform that simplifies data collection, organization, and analysis through an integrated cloud-native architecture. It allows enterprises to turn data into insights by unifying various data sources and providing a catalog of microservices for additional functionality. The platform addresses challenges organizations face in leveraging data due to legacy systems, regulatory constraints, and time spent preparing data. It provides a single interface for data teams to collaborate and access over 45 integrated services to more efficiently gain insights from data.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
In the digital world, semi-structured data is as important as transactional, structured data. Both need to be analyzed to create a competitive advantage. Unfortunately, neither the data lake nor the data warehouse are adequate to handle the analysis of both data types.
These slides—based on the webinar from EMA Research and Vertica—delve into the push toward the innovative unified analytics warehouse (UAW), a merging of the data lake and data warehouse.
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloads
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
This document discusses moving from a centralized data architecture to a distributed data mesh architecture. It describes how a data mesh shifts data management responsibilities to individual business domains, with each domain acting as both a provider and consumer of data products. Key aspects of the data mesh approach discussed include domain-driven design, domain zones to organize domains, treating data as products, and using this approach to enable analytics at enterprise scale on platforms like Azure.
All business sizes can benefit from better use of their data to gain insights, how the cloud can help overcome common data challenges and accelerate transformation with the cloud technology
https://www.rapyder.com/cloud-data-analytics-services/
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Enable Better Decision Making with Power BI Visualizations & Modern Data EstateCCG
Self-service BI empowers users to reach analytic outputs through data visualizations and reporting tools. Solution Architect and Cloud Solution Specialist, James McAuliffe, will be taking you through a journey of Azure's Modern Data Estate.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...Denodo
Watch full webinar here: https://buff.ly/46pRfV7
This Denodo session explores the power of data virtualization, shedding light on its architecture, customer value, and a diverse range of use cases. Attendees will discover how the Denodo Platform enables seamless connectivity to various data sources while effortlessly combining, cleansing, and delivering data through 5 differentiated use cases.
Architecture: Delve into the core architecture of the Denodo Platform and learn how it empowers organizations to create a unified virtual data layer. Understand how data is accessed, integrated, and delivered in a real-time, agile manner.
Value for the Customer: Explore the tangible benefits that Denodo offers to its customers. From cost savings to improved decision-making, discover how the Denodo Platform helps organizations derive maximum value from their data assets.
Five Different Use Cases: Uncover five real-world use cases where Denodo's data virtualization platform has made a significant impact. From data governance to analytics, Denodo proves its versatility across a variety of domains.
- Logical Data Fabric
- Self Service Analytics
- Data Governance
- 360 degree of Entities
- Hybrid/Multi-Cloud Integration
Watch this illuminating session to gain insights into the transformative capabilities of the Denodo Platform.
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
1. Analytics in a day
Cloud analytics in the age of
self-service and data science
2. The heart of analytics
Section 1
Data businesses need
data warehouses
Section 2
Data warehouses &
data lakes come together
Section 3
BI & DW come together
Section 4
The cloud for modern analytics
Section 5
A new class of analytics
4. Is the data warehouse
still relevant?
What’s changed since 1988?
A 30-year-old architecture, still going strong
Commerce and technology
The data warehouse itself
6. All data businesses need
to be analytic businesses
Without analytics data is a cost center,
not a resource
7. Analytic businesses need
to evolve data science
Every business has opportunities to make
analytics faster, easier, and more insightful
8. Store
Data Ingestion Big Data Data Warehousing
The cloud data warehouse in the data-driven business
9. Data Ingestion Big Data Data Warehousing
Store
The cloud data warehouse in the data-driven business
10. Store
Data Ingestion Big Data Data Warehousing
Cloud data
SaaS data
On-premises data
Devices data
The cloud data warehouse in the data-driven business
11. Store
Data Ingestion Big Data Data Warehousing
Cloud data
SaaS data
On-premises data
Devices data
The cloud data warehouse in the data-driven business
12. Store
Data Ingestion Big Data Data Warehousing
Cloud data
SaaS data
On-premises data
Devices data
The cloud data warehouse in the data-driven business
16. 80%
report struggling to
become mature users
of data*
55%
report data silos and
data management
difficulties as roadblocks*
* Harvard Business Review (2019), Understanding why analytics strategies fall short for some, but not for others
Analytics & AI is the #1 investment for business leaders,
however they struggle to maximize ROI
17. Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Proven security & privacy
Dependable performance
Structured
Business analytics
Data lake Data warehouse
Businesses are forced to maintain
two critical, yet independent analytics systems
28. The new economy
thrives on data literacy
Communicating with data is a critical skill in
the new economy
29. Users and IT must come
together in the new enterprise
Get over the IT / business divide
30. Governance and self-service
enhance decision-making
Governance is not about making the right decisions,
it is about making decisions the right way
31. The importance of data models
BI models Power BI
• Built and maintained by business users or BI developers
• Use enterprise models, departmental data, and external sources
• Focused on a single subject area, but often widely shared
Machine Learning
models
Azure Synapse
Analytics
• Built and maintained by data scientists
• Mostly developed from raw sources in the data lake
• Often experimental, needing a data engineer for production use
Azure Synapse
AnalyticsEnterprise models
• Built and maintained by IT architects
• Consolidated data from many systems
• Centralized as an authoritative source for reporting and analysis
32. Enterprise models in the
self-service environment
If business users
are tech-smart and
data literate, why
do they need
enterprise models?
Consistency
Some business processes can be built once and shared as a
corporate standard
Governance
Certain data sets need complex security and privacy controls
Efficiency
No need to repeat design, preparing, and loading or securing
Line-of-business sources
Data ingestion &
transformation
Enterprise models
Azure Synapse
Analytics
Power BI
33. BI models in the enterprise
environment
If enterprise
models are so
important, why do
users need self-
service BI models?
Flexibility
Some data sets are temporary, external, or ad-hoc don’t need to
be consolidated
Efficiency
Tech-smart business users have fresh and innovative ideas they need to
explore with agility
Ad-hoc, departmental and
external sources
Line-of-business sources
Data ingestion &
transformation
Power BI
Enterprise models
Azure Synapse
Analytics
BI models
34. Section 4
The cloud for modern analytics
Data science models in the
enterprise environment
What is the role of
the data
warehouse with
data science?
Integrating results with enterprise models
Making the results of data science easily available for business functions
Serving enterprise data for data scientists
Helps ensure consistency across diverse analyses
Power BI
Azure Synapse
Analytics
Azure
Databricks
Enterprise models
Azure Synapse
Analytics
Data science results
39. Structured, unstructured, and streaming data
integrated in a single, scalable, environment
A cloud analytics platform
is the hub for all data models
40. BI
Bring together the best of both worlds with the market-
leading BI service and the industry-leading analytics platform
Power BI can analyze and visualize
massive volumes of data
Azure Synapse Analytics provides a
scalable platform to enable real-time BI
Analytics
41. Section 5
A new class of analytics
Power BI can analyze and
visualize massive volumes of data
Azure Synapse Analytics
provides a scalable platform
to enable real-time BI
Azure Machine Learning natively
integrates with Azure Synapse &
Power BI to democratize AI across
your business
BI Analytics Machine learning
Bring together the best of both worlds with the market-
leading BI service and the industry-leading analytics platform
43. Unified experience
Azure Synapse Studio
Integration Management Monitoring Security
Analytics runtimes
SQL
Azure Data Lake Storage
Azure Machine
Learning
On-premises data
Cloud data
SaaS data
Streaming data
Power BI
Azure Synapse lies at the heart of business, AI, and BI
Azure Synapse Analytics
44. Unified experienceAzure Synapse Studio
Integration Management Monitoring SecuritySQL
Azure Data Lake Storage
Azure Machine
Learning
On-premises
data
Cloud
data
SaaS data
Streaming
data
Cloud analytics has taken a leap forward
with a unified, unmatched platform
Azure Synapse Analytics
Power BI
46. Introducing Azure
Synapse Analytics
A limitless analytics service with unmatched
time to insight, that delivers insights from all
your data, across data warehouses and big
data analytics systems, with blazing speed
Simply put, Azure Synapse is Azure SQL Data
Warehouse evolved
We have taken the same industry leading data
warehouse and elevated it to a whole new level of
performance and capabilities
47. Azure Synapse
Analytics
Snowflake
Standard
Amazon
Redshift
Google
BigQuery
per byte
$33
$103
$48
…$564
94% less
TPC-H benchmark comparison
Price-performance | Lower is better
* GigaOm TPC-H benchmark report, January 2019, “GigaOm report: Data Warehouse in the Cloud Benchmark
With the best price-performance
in the business
Up to 14x faster and costs 94%
less than other cloud providers
A breakthrough in the cost of enterprise analytics
48. Data consolidation using
Azure Synapse Analytics
Migration to the cloud for
efficient business operations
Using Azure Synapse Analytics
for predictive analytics
Organizations that fully harness their data outperform
49. At the core of all use cases is…Azure Synapse Analytics
Real-time
analytics
Modern data
warehousing
Advanced
analytics
"We want to analyze
data coming from
multiple sources and
in varied formats"
"We want to leverage
the analytics platform
for advanced fraud
detection"
“We’re trying to get
insights from our
devices in real-time”
Cloud-scale analytics
53. Query and analyze data with
T-SQL using both provisioned
and serverless models
Quickly create notebooks with
your choice of Python, Scala,
SparkSQL, and .NET for
Apache Spark
Build end-to-end workflows
for your data movement and
data processing scenarios
Execute all data tasks with a
simple UI and unified
environment
Azure Synapse Analytics
Synapse SQL
Apache Spark
for Synapse
Synapse Pipelines Synapse Studio
54. Integrated analytics platform for AI, BI, and continuous intelligence
Platform
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
Data lake integrated and Common Data
Model aware
METASTORE
SECURITY
MANAGEMENT
MONITORING
Integrated platform services
for, management, security, monitoring,
and metastore
DATA INTEGRATION
Analytics Runtimes
Integrated analytics runtimes available
provisioned and serverless
Synapse SQL offering T-SQL for batch,
streaming, and interactive processing
Synapse Spark for big data processing
with Python, Scala, R and .NET
PROVISIONED (DW) SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala R
Multiple languages suited to different
analytics workloads
Experience Synapse Studio
SaaS developer experiences for code
free and code first
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
Designed for analytics workloads at any
scale
Azure Synapse Analytics
55. Integrated analytics platform for AI, BI, and continuous intelligence
Platform
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
METASTORE
SECURITY
MANAGEMENT
MONITORING
DATA INTEGRATION
Analytics Runtimes
PROVISIONED (DW) SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala R
Experience Synapse Studio
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
Azure Synapse Analytics
Connected Services
Azure Data Catalog
Azure Data Lake Storage
Azure Data Share
Azure Databricks
Azure HDInsight
Azure Machine Learning
Power BI
3rd Party Integration
58. Synapse Studio is divided into
Activity hubs
Hubs organize the tasks needed for
building analytics solutions
Synapse Studio
Overview Data
Monitor Manage
Quick-access to common
gestures, most-recently used
items, and links to tutorials
and documentation.
Explore structured and
unstructured data
Centralized view of all resource
usage and activities in the
workspace.
Configure the workspace,
pool, access to artifacts
Develop
Write code and the define
business logic of the pipeline
via notebooks, SQL scripts,
Data flows, etc.
Orchestrate
Design pipelines that that
move and transform data.
69. Author SQL Scripts
Execute SQL script on provisioned SQL
Pool or SQL Serverless
Publish individual SQL script or multiple
SQL scripts through Publish all feature
Support for languages and Intellisense
Develop hub -
SQL scripts
70. View results in table or chart form and
export results in several popular formats
Develop hub -
SQL scripts
71. Data flows are a visual way of
specifying how to transform data,
providing a code-free experience
Develop hub -
Data flows
72. Develop hub –
Power BI
Create Power BI reports in the workspace
Provide access to published reports in the
workspace
Update reports in real time from Synapse
workspace and show on Power BI service
Visually explore and analyze data
74. Best-in-class
Price-performance is calculated by GigaOm as the TPC-H metric of cost of ownership divided by composite query.
Results based on GigaOm’s TPC-H results, published in January 2019
Leader in price per performance
76. Price-performance @ 30TB
Lower is Better
Amazon
Redshift
Google BigQuery
Flat Rate
Azure Synapse
Analytics
Google BigQuery
Flat Rate
Snowflake
Standard
$1310
$570
$309
$206
$286
$153
$0
$100
$200
$300
$400
$500
$600
Snowflake
Standard
Best-in-class
Price-performance is calculated by GigaOm as the TPC-H metric of cost of ownership divided by composite query.
Results based on GigaOm’s TPC-H results, published in January 2019
77. --T-SQL syntax for scoring data in SQL DW
SELECT
d.*, p.Score
FROM PREDICT(MODEL = @onnx_model, DATA = dbo.mytable AS
d)
WITH (Score float) AS p;
Upload
models
Machine learning
enabled DW
Native PREDICT-ion
T-SQL based experience
(interactive/batch scoring)
Interoperability with other
models built elsewhere
Scoring executed where the
data lives
T-SQL Language
Data Warehouse
Data
+
Score models
Model Predictions
=
Synapse SQL
Create models
78. Event Hubs
IoT Hub
T-SQL language
Built-in streaming ingestion & analytics
Streaming Ingestion Data Warehouse
Synapse SQL
Heterogenous
data preparation
and ingestion
Native SQL streaming
High throughput ingestion
(up to 200MB/sec)
Delivery latencies in seconds
Ingestion throughput scales with
compute scale
Analytics capabilities
79. Empower more users
per data warehouse
Leverage up to 128 concurrent
slots, simultaneously, on a single
data warehouse
Number of simultaneous workloads
increases with data warehouse capacity
Utilize preset functions to allocate
resources that need them the most
80. Intra cluster workload isolation
(Scale in)
Marketing
CREATE WORKLOAD GROUP Sales
WITH
(
[ MIN_PERCENTAGE_RESOURCE = 60 ]
[ CAP_PERCENTAGE_RESOURCE = 100 ]
[ MAX_CONCURRENCY = 6 ] )
40%
Data
warehouse
Local In-Memory + SSD Cache
Compute
1000c DWU
60%
Sales
60%
100%
Workload aware
query execution
Workload isolation
Multiple workloads share
deployed resources
Reservation or shared resource
configuration
Online changes to workload policies
81. CREATE MATERIALZIED VIEW vw_ProductSales
WITH (DISTRIBUTION = HASH(ProductKey))
AS
SELECT
ProductName
ProductKey,
SUM(Amount) AS TotalSales
FROM
FactSales fs
INNER JOIN DimProduct dp ON fs.prodkey = dp.prodkey
GROUP BY
ProductName,
ProductKey
See more by scaling
to petabytes
82. ProductName ProductKey TotalSales
Product A 5453 784,943.00
Product B 763 48,723.00
… … …
FactSales
Table
10B Records
DimProduct
Table
1,000 Records
Materialized View
(1000 Records)
See more by scaling
to petabytes
FactInventory
Table
mvw_ProductSales
1,000 Records
CREATE MATERIALZIED VIEW
mvw_ProductSales
WITH (DISTRIBUTION = HASH(ProductKey))
AS
SELECT
ProductName
ProductKey,
SUM(Amount) AS TotalSales
FROM
FactSales fs
INNER JOIN DimProduct dp
ON fs.prodkey = dp.prodkey
GROUP BY
ProductName,
ProductKey
SELECT
<COLUMNS>
FROM FactSales fs
INNER JOIN
SELECT
ProductName
ProductKey,
SUM(Amount) AS TotalSales
FROM
FactSales fs
INNER JOIN DimProduct dp
GROUP BY
ProductName,
ProductKey ) ps
INNER JOIN FactInventory
GROUP BY …
83. Execution2
Cache Hit
~.2 seconds
Execution1
Cache Miss
Regular
Execution
SELECT
ProductName
ProductKey,
SUM(Amount) AS TotalSales
FROM
Fact Sales
INNER JOIN DimProduct
GROUP BY
ProductName,
ProductKey
Build confidence in your
data with result set cache
Data
Warehouse
Resultset
Cache
84. Most secure data
warehouse in the cloud
Multiple levels of security between the
user and the data warehouse
...at no additional cost
Threat Protection
Network Security
Authentication
Access Control
Data Protection
Customer Data
85. Comprehensive security
Category Feature
Data protection
Data in transit
Data encryption at rest
Data discovery and classification
Access control
Object level security (tables/views)
Row level security
Column level security
Dynamic data masking
SQL login
Authentication Azure active directory
Multi-factor authentication
Virtual networks
Network security Firewall
Azure ExpressRoute
Threat detection
Threat protection Auditing
Vulnerability assessment
87. Discovery and
exploration
What’s in this file? How many rows are there? What’s the max value?
SQL serverless reduces data lake exploration to the right-click
Data
transformation
How to convert CSVs to Parquet quickly? How to transform the raw data?
Use the full power of T-SQL to transform the data in the data lake
88. Overview
An interactive query service that provides T-SQL
queries over high scale data in Azure Storage.
Benefits
Serverless
No infrastructure
Pay only for query execution
No ETL
Offers security
Data integration with Databricks, HDInsight
T-SQL syntax to query data
Supports data in various formats
(Parquet, CSV, JSON)
Support for BI ecosystem
Azure Storage
SQL
Serverless
Query
Power BI
Azure Data
Studio
SSMS
DW
Read and write
data files
Curate and
transform data
Sync table
definitions
Read and write
data files
Azure Synapse Analytics > SQL > SQL serverless
90. Allows multiple languages in one notebook
%%<Name of language>
Offers use of temporary tables across languages
Support for syntax highlight, syntax error, syntax code
completion, smart indent, and code folding
Export results
Quickly create &
configure notebooks
91. As notebook cells run, the underlying
Apache Spark application status is
shown, providing immediate feedback
and progress tracking.
Quickly create &
configure notebooks
93. Overview
Linked services defines the connection
information needed for pipelines to connect to
external resources
Benefits
Offers 90+ pre-built connectors
Allows easy cross platform data migration
Represents data store or compute resources
94. Prep and transform data
Mapping dataflow
Code free data transformation at scale
Wrangling dataflow
Code free data preparation at scale
95. Handle upserts,
updates, deletes
on sql sinks
Add new partition
methods
Add schema
drift support
Add file handling (move
files after read, write files
to file names described
in rows, etc.)
New inventory of
functions (e.g. Hash
functions for row
comparison)
Commonly used ETL
patterns (Sequence
generator/Lookup
transformation/SCD…)
Data lineage – Capturing
sink column lineage &
impact analysis
(invaluable if this is for
enterprise deployment)
Implement commonly
used ETL patterns as
templates (SCD type1,
type2, data vault)
Data flow
Capabilities
96. Insights for all with
Power BI + Azure
Power up your BI with Azure Synapse
97. Where do you find yourself on the curve?
Hindsight Insight Foresight
Value
Difficulty
What happened?
Descriptive Analysis
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
How can we make it happen?
Prescriptive Analysis
98. Where do you find yourself on the curve?
Hindsight Insight Foresight
Value
Difficulty
What happened?
Descriptive Analysis
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
How can we make it happen?
Prescriptive Analysis
BI
99. BI + Analytics unlock the door to AI, machine learning, and
real-time insights
Hindsight Insight Foresight
Value
Difficulty
What happened?
Descriptive Analysis
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
How can we make it happen?
Prescriptive Analysis
AnalyticsBI
100. BI
Bring together the best of both worlds with the market-
leading BI service and the industry-leading analytics platform
Power BI can analyze and visualize
massive volumes of data
Azure Synapse Analytics provides a
scalable platform to enable real-time BI
Analytics
101. Power BI can analyze and
visualize massive volumes of data
Azure Synapse Analytics
provides a scalable platform
to enable real-time BI
Azure Machine Learning natively
integrates with Azure Synapse &
Power BI to democratize AI across
your business
BI Analytics Machine learning
Bring together the best of both worlds with the market-
leading BI service and the industry-leading analytics platform
102. Accelerate business value with a powerful analytics platform
Business analysts IT professionals Data scientists
Frictionless
collaboration
Unified
analytics platform
Advanced analytics
and AI
Powerful visualization and
reporting
Unmatched
capabilities
Business value
Common Data Model on Azure Data Lake StorageUnified data
Azure Synapse AnalyticsPower BI
Powerful and
integrated
tooling
Azure Machine Learning
103. Visualize and
report
Power BI
Model &
serve
Azure Synapse
Analytics
CDM folders
Azure Data Lake
Storage
Respond instantly
Enable instant response times with
Power BI Aggregations on massive
datasets when querying at the
aggregated level
Get granular with your data
Queries at the granular level are
sent to Azure Synapse Analytics
with DirectQuery leveraging its
industry-leading performance
Save money with industry-
leading performance
Azure Synapse Analytics is up to
14x faster and 94% cheaper than
other cloud providers
View reports with a single pane
of glass
Skip the configuration when
connecting to Power BI with
integrated Power BI-authoring
directly in the Azure Synapse Studio
Accelerate business value with a powerful analytics platform
104. Customers using Azure Synapse & Power BI today
are transforming their business with purpose
27%
Faster time
to insights
271% Average ROI
26%
Lower total cost
of ownership
60%
Increased customer
satisfaction
* Forrester, October 2019, “The Total Economic Impact of Microsoft Azure Analytics with Power BI”
105. Build Power BI dashboards directly
from Azure Synapse
Azure Synapse + Power BI integration
111. Get Started Today
Create a free Azure account and get started with Azure Synapse Analytics:
https://azure.microsoft.com/en-us/free/synapse-analytics/
Get in touch with us:
https://info.microsoft.com/ww-landing-contact-me-azure-analytics.html
Attend a virtual Azure Analytics workshop with Informatica:
https://now.informatica.com/Microsoft_CDW_Workshops.html#fbid=uqwtl_SXNFV
Learn more:
https://aka.ms/synapse