This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.
Modernizing to a Cloud Data ArchitectureDatabricks
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.
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Modernizing to a Cloud Data ArchitectureDatabricks
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.
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
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
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
This presentation is for those of you who are interested in moving your on-prem SQL Server databases and servers to Azure virtual machines (VM’s) in the cloud so you can take advantage of all the benefits of being in the cloud. This is commonly referred to as a “lift and shift” as part of an Infrastructure-as-a-service (IaaS) solution. I will discuss the various Azure VM sizes and options, migration strategies, storage options, high availability (HA) and disaster recovery (DR) solutions, and best practices.
The cloud is all the rage. Does it live up to its hype? What are the benefits of the cloud? Join me as I discuss the reasons so many companies are moving to the cloud and demo how to get up and running with a VM (IaaS) and a database (PaaS) in Azure. See why the ability to scale easily, the quickness that you can create a VM, and the built-in redundancy are just some of the reasons that moving to the cloud a “no brainer”. And if you have an on-prem datacenter, learn how to get out of the air-conditioning business!
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
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
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
This presentation is for those of you who are interested in moving your on-prem SQL Server databases and servers to Azure virtual machines (VM’s) in the cloud so you can take advantage of all the benefits of being in the cloud. This is commonly referred to as a “lift and shift” as part of an Infrastructure-as-a-service (IaaS) solution. I will discuss the various Azure VM sizes and options, migration strategies, storage options, high availability (HA) and disaster recovery (DR) solutions, and best practices.
The cloud is all the rage. Does it live up to its hype? What are the benefits of the cloud? Join me as I discuss the reasons so many companies are moving to the cloud and demo how to get up and running with a VM (IaaS) and a database (PaaS) in Azure. See why the ability to scale easily, the quickness that you can create a VM, and the built-in redundancy are just some of the reasons that moving to the cloud a “no brainer”. And if you have an on-prem datacenter, learn how to get out of the air-conditioning business!
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
In this presentation we introduce the basic concepts around SQL Server Azure: the database in the cloud.
Regards,
Ing. Eduardo Castro, PhD
http://ecastrom.blogspot.com
http://comunidadwindows.org
Join us for a deep dive into Windows Azure. We’ll start with a developer-focused overview of this brave new platform and the cloud computing services that can be used either together or independently to build amazing applications. As the day unfolds, we’ll explore data storage, SQL Azure™, and the basics of deployment with Windows Azure. Register today for these free, live sessions in your local area.
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
At our March Data Analytics Meetup, Dan Rodriguez and Cherian Mathew demonstrated the variations in Microsoft Azure programs and how they are impacting digital transformation.
We live in a world of unprecedented change. To be successful in this world of change, you will need to develop a data culture, creating an environment where every team and every individual is empowered to do great things because of the data at their fingertips. In this event you will learn how to create a culture of data and how the Microsoft Modern BI platform and tools can help you to can harness the power of data once only reserved for data scientists. Learn about how to tap into the power of natural language, self-service business insights and visualization capabilities – and make insights available to anyone, anywhere, at any time.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Introduces the Microsoft’s Data Platform for on premise and cloud. Challenges businesses are facing with data and sources of data. Understand about Evolution of Database Systems in the modern world and what business are doing with their data and what their new needs are with respect to changing industry landscapes.
Dive into the Opportunities available for businesses and industry verticals: the ones which are identified already and the ones which are not explored yet.
Understand the Microsoft’s Cloud vision and what is Microsoft’s Azure platform is offering, for Infrastructure as a Service or Platform as a Service for you to build your own offerings.
Introduce and demo some of the Real World Scenarios/Case Studies where Businesses have used the Cloud/Azure for creating New and Innovative solutions to unlock these potentials.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
22. Data platform continuum
Shared
lower
cost
Dedicated
higher
cost
Higher administration Lower administration
Physical
SQL Server
Physical Machine (raw iron)
IaaS
SQL Server in Azure VM
Virtualized Machines
Virtual
SQL Server Private Cloud
Virtualized Machine + Appliance
PaaS & SaaS
Azure SQL Database
Virtualized Database
23. SQL Server 2019 big data, analytics, and AI
Managed SQL Server, Spark,
and data lake
Store high volume data in a data lake and access
it easily using either SQL or Spark
Management services, admin portal, and
integrated security make it all easy to manage
SQL
Server
Data virtualization
Combine data from many sources without
moving or replicating it
Scale out compute and caching to boost
performance
T-SQL
Analytics Apps
Open
database
connectivity
NoSQL Relational
databases
HDFS
Complete AI platform
Easily feed integrated data from many sources to
your model training
Ingest and prep data and then train, store, and
operationalize your models all in one system
SQL Server External Tables
Compute pools and data pools
Spark
Scalable, shared storage (HDFS)
External
data sources
Admin portal and management services
Integrated AD-based security
SQL Server
ML Services
Spark &
Spark ML
HDFS
REST API containers
for models
24. Azure SQL Database deployment option
Azure SQL Database
Database-scoped deployment option with
predictable workload performance
Shared resource model optimized for greater
efficiency of multi-tenant applications
Best for apps that require resource
guarantee at database level
Best for SaaS apps with multiple databases that can share
resources at database level, achieving better cost efficiency
Best for modernization at scale with
low friction and effort
Elastic Pool
Single Managed Instance
Instance-scoped deployment option with high
compatibility with SQL Server and full PaaS benefits
Service
Tiers
26. Azure Data Factory
• Hybrid data integration service for enabling code-free ETL
• Industr
y
leading
data
ingesti
on
• Visual
• No
Code
• Hybrid • Pay
only
for
what
you
use
• Managed SSIS
Productive & trusted hybrid data integration service
that simplifies ETL with any data, from any source, at scale.
28. Azure
Integration Runtime
Command and Control
L E G E N D
Data
Orchestration @ Scale
Trigger Pipeline
Activity Activity
Activity Activity
Activity
Self-hosted
Integration Runtime
Linked
Service
31. Integrated data platform for BI, AI 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 ON-DEMAND
Form Factors
SQL
Languages
Python .NET Java Scala R
Experience Azure Synapse Analytics Studio
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
Azure Synapse Analytics
39. The Modern Data Problem
• How to derive value from data:
• What happened historically?
• What is happening now?
• What is going to happen?
Each dimension of data is
constantly expanding
62. Azure Cognitive Services
Azure Bot Service
Azure Search Azure Databricks
Azure Machine Learning
Azure AI Infrastructure
Knowledge mining
AI apps and agents Machine learning
Azure AI
Productive Built for enterprises Trusted
Solution Areas
63. Language
Speech
Search
Cognitive
Services Lab
Knowledge
Vision
Custom
Vision
Speech API
Bing Spell
Check
Speaker Recognition
Bing Entity Search
Video Indexer
Emotion
Translator Text
Text to
speech
QnA Maker
Bing Search
Bing Speech
Custom Speech
Custom Decision
Translator
Speech
Computer
Vision
Content
Moderator
Bing Autosuggest
Text Analytics
Face
Language
Understanding (LUIS)
Cognitive Services APIs
Bing Statistics
add-in
Bing Visual Search
Bing Custom Search
Unified Speech
66. MongoDB
Table API
Turnkey global
distribution
Elastic scale out
of storage & throughput
Guaranteed low latency
at the 99th percentile
Comprehensive
SLAs
Five well-defined
consistency models
Azure Cosmos DB
Document
Column-family
Key-value Graph
Core (SQL) API
