In this presentation, we will do assess the on-premises environment and determining what workloads and databases are ready to make the move and what can you do to improve their Azure readiness while reducing downtime during the migration. Planning and assessment plays a critical role in moving to the cloud. We would see wide range of resources and tools to get an assessment completed with ease while identifying workload dependencies with practical tips and tricks focusing on sizing and costs. And finally, we’ll assess the SQL instances and identify their readiness for Azure as well.
Migrating on premises workload to azure sql databasePARIKSHIT SAVJANI
Azure SQL Database is a fully managed cloud database service with built-in intelligence, elastic scale, performance, reliability, and data protection that enables enterprises and ISVs to reduce their total cost of ownership and operational cost and overheads. In this session, I will share real-world experience of successfully migrated existing SaaS application and on-premises workload for some our tier 1 customers and ISV partners to Azure SQL Database service. The session walks through planning, assessment, migration tools and best practices from the proven experiences and practices of migrating real world applications to Azure SQL Database service.
Customer migration to azure sql database from on-premises SQL, for a SaaS app...George Walters
Why would someone take a working on-premises SaaS infrastructure, and migrate it to Azure? We review the technology decisions behind this conversion, and business choices behind migrating to Azure. The SQL 2012 infrastructure and application was migrated to PaaS Services. Finally, how would we do this architecture in 2019.
Microsoft Azure platform provides a database as a service offering that allows developers to use SQL in the same way as they would in an on-premises location.
Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance). Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc. So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.
A Tour of Azure SQL Databases (NOVA SQL UG 2020)Timothy McAliley
A Tour of Azure SQL Databases (NOVA SQL UG 2020) - overview of the different deployment options for Azure SQL Database.
More info: www.meetup.com/novasql
In this presentation, we will do assess the on-premises environment and determining what workloads and databases are ready to make the move and what can you do to improve their Azure readiness while reducing downtime during the migration. Planning and assessment plays a critical role in moving to the cloud. We would see wide range of resources and tools to get an assessment completed with ease while identifying workload dependencies with practical tips and tricks focusing on sizing and costs. And finally, we’ll assess the SQL instances and identify their readiness for Azure as well.
Migrating on premises workload to azure sql databasePARIKSHIT SAVJANI
Azure SQL Database is a fully managed cloud database service with built-in intelligence, elastic scale, performance, reliability, and data protection that enables enterprises and ISVs to reduce their total cost of ownership and operational cost and overheads. In this session, I will share real-world experience of successfully migrated existing SaaS application and on-premises workload for some our tier 1 customers and ISV partners to Azure SQL Database service. The session walks through planning, assessment, migration tools and best practices from the proven experiences and practices of migrating real world applications to Azure SQL Database service.
Customer migration to azure sql database from on-premises SQL, for a SaaS app...George Walters
Why would someone take a working on-premises SaaS infrastructure, and migrate it to Azure? We review the technology decisions behind this conversion, and business choices behind migrating to Azure. The SQL 2012 infrastructure and application was migrated to PaaS Services. Finally, how would we do this architecture in 2019.
Microsoft Azure platform provides a database as a service offering that allows developers to use SQL in the same way as they would in an on-premises location.
Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance). Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc. So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.
A Tour of Azure SQL Databases (NOVA SQL UG 2020)Timothy McAliley
A Tour of Azure SQL Databases (NOVA SQL UG 2020) - overview of the different deployment options for Azure SQL Database.
More info: www.meetup.com/novasql
Azure SQL Database now has a Managed Instance, for near 100% compatibility for lifting-and-shifting applications running on Microsoft SQL Server to Azure. Contact me for more information.
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.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
McGraw-Hill Optimizes Analytics Workloads with DatabricksAmazon Web Services
Using Databricks, McGraw-Hill securely transformed itself from a collection of data silos with limited access to data and minimal collaboration to an organization with democratized access to data and machine learning. This ultimately enables its data teams to rapidly identify usage patterns predicting student performance, so they can make timely enhancements to the software that proactively guide at-risk students through the course material.
Join our webinar to learn:
- How a cloud-based unified analytics platform can help your company perform analytics faster, at lower cost.
- How to mitigate challenges presented by data silos so data science teams can collaborate effectively.
- How to implement data analytics infrastructure to put models into production quickly
a talk about azure synapse aimed to help people who are not data experts understand what synapse is and how you can integrate it with other technologies
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
Cortana Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. It is comprised of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. This presentation will cover all the products involved, how they work together, and use cases.
Azure SQL Database now has a Managed Instance, for near 100% compatibility for lifting-and-shifting applications running on Microsoft SQL Server to Azure. Contact me for more information.
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.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
McGraw-Hill Optimizes Analytics Workloads with DatabricksAmazon Web Services
Using Databricks, McGraw-Hill securely transformed itself from a collection of data silos with limited access to data and minimal collaboration to an organization with democratized access to data and machine learning. This ultimately enables its data teams to rapidly identify usage patterns predicting student performance, so they can make timely enhancements to the software that proactively guide at-risk students through the course material.
Join our webinar to learn:
- How a cloud-based unified analytics platform can help your company perform analytics faster, at lower cost.
- How to mitigate challenges presented by data silos so data science teams can collaborate effectively.
- How to implement data analytics infrastructure to put models into production quickly
a talk about azure synapse aimed to help people who are not data experts understand what synapse is and how you can integrate it with other technologies
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
Cortana Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. It is comprised of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. This presentation will cover all the products involved, how they work together, and use cases.
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
The Impact of Cloud Computing on Predictive Analytics 7-29-09 v5Robert Grossman
This is a talk I gave in San Diego on July 29, 2009 explaining some of the impact and some of the opportunities of cloud computing on predictive analytics.
Pat Helland's "book review" of the Above the Clouds: a Berkeley View of Cloud Computing paper.
As Pat says "If you are interested in cloud computing, you want to understand these ideas"
Lecture #6 - ET-3010
Cloud Computing - Overview and Examples
Connected Services and Cloud Computing
School of Electrical Engineering and Informatics SEEI / STEI
Institut Teknologi Bandung ITB
Update April 2017
Similar to An Introduction to Cloud Computing by Robert Grossman 08-06-09 (v19) (20)
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I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
This a talk that I gave at BioIT World West on March 12, 2019. The talk was called: A Gen3 Perspective of Disparate Data:From Pipelines in Data Commons to AI in Data Ecosystems.
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This is an overview of the Data Biosphere Project, its goals, its architecture, and the three core projects that form its foundation. We also discuss data commons.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
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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.
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
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Cyber risk predictions
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An Introduction to Cloud Computing by Robert Grossman 08-06-09 (v19)
1. An Overview of Cloud Computing:
My Other Computer is a Data Center
Robert Grossman
Open Data Group &
University of Illinois at Chicago
IEEE New Technologies Conference
August 6, 2009
6. Are There Other Types of Clouds?
6
Large Data Cloud Services
ad targeting
7. One Definition
Clouds provide on-demand resources or
services over a network, often the Internet,
with the scale and reliability of a data center.
No standard definition.
Cloud architectures are not new.
What is new:
– Scale
– Ease of use
– Pricing model.
7
9. Elastic, Usage Based Pricing Is New
9
1 computer in a rack
for 120 hours
120 computers in three
racks for 1 hour
costs the same as
Elastic, usage based pricing turns capex into opex.
Clouds can be used to manage surges in computing needs.
10. Simplicity Offered By the Cloud is New
10
+ .. and you have a computer
ready to work.
A new programmer can develop a
program to process a container full of
data with less than day of training
using MapReduce.
12. Varieties of Clouds
Architectural Model
– On-demand computing instances
vs large data cloud services
Payment Model
– Elastic, usage based pricing,
lease/own, …
Management Model
– Private vs Public; Single vs
Multiple Tenant; …
Programming Model
– Queue Service, MPI,
MapReduce, Distributed UDF
12
Computing instances
vs large data cloud
services
Private internal vs
public external
Elastic, usage-
based pricing or not
All combinations
occur.
13. Architectural Models:
How Do You Fill a Data Center?
Cloud Storage Services
Cloud Compute Services
(MapReduce & Generalizations)
Cloud Data Services
(BigTable, etc.)
Quasi-relational
Data Services
App App App App App
App App
App App
large data cloud
services
App App App
…
on-demand
computing instances
14. Payment Models
Buying racks, containers and data centers
Leasing racks containers and data centers
Utility based computing (pay as you go)
– Moves cap ex to op ex
– Handle surge requirements (use 1000 servers for 1
hour vs 1 server for 1000 hours)
14
15. Management Models
Public, private and hybrid models
Single tenant vs multiple tenant (shared vs
non-shared hardware)
Owned vs leased
Manage yourself vs outsource management
All combinations are possible
15
16. Programming Models
Amazon’s Simple
Queue Service
MPI, sockets, FIFO
16
MapReduce
Distributed UDF
on-demand
computing
instances
large data
cloud services
DryadLINQ
Azure services
17. Part 3. Cloud Computing Industry
“Cloud computing has become the center of
investment and innovation.”
Nicholas Carr, 2009 IDC Directions
17
Cloud computing is
approaching the top of
the Gartner hype cycle.
18. IaaS, PaaS and SaaS Point of View
SaaS
PaaS
IaaS
Infrastructure as a Service
PRODUCT: Compute power, storage
and networking infrastructure over the
internet, provided as a virtual machine
image
USERS: Developers
Platform as a Service
PRODUCT: storage, compute and
other services to simplify application
development, especially of web
applications.
USERS: Application Developers
Software as a Service
PRODUCT: Finished
application available on
demand to end user
USERS: Software consumer
19. Building Data Centers
Sun’s Modular
Data Center (MD)
Formerly Project
Blackbox
Containers used by
Google, Microsoft
& others
Data center
consists of 10-60+
containers.
19
20. Data Center Operating Systems
Data center services include: VM management
services, business continuity services, security
services, power management services, etc.
20
workstatio
n
VM 1 VM 5
…
VM 1 VM 50,000
…
Data Center Operating System
21. Berkeley View of Cloud Computing
21
Providers of Cloud Services
Consumers of Cloud Services
Providers of Software as a Service
Consumers of Software as a Service
Berkeley Report on cloud computing divides industry
into these layers & concentrates on public clouds.
Data Centers
22. Transition Taking Place
A hand full of players are building multiple data
centers a year and improving with each one.
This includes Google, Microsoft, Yahoo, …
A data center today costs $200 M – $400+ M
Berkeley RAD Report points out analogy with
semiconductor industry as companies stopped
building their own Fabs and starting leasing
Fabs from others as Fabs approached $1B
22
23. Mindmeister Map of Cloud Computing
Dupont’s Mindmeister Map divides the industry:
– IaaS, PaaS, Management, Community
http://www.mindmeister.com/maps/show_public/15936058
23
25. Virtualization
Virtualization separates logical infrastructure
from the underlying physical resources to
decrease time to make changes, improve
flexibility, improve utilization and reduce costs
Example - server virtualization. Use one
physical server to support multiple logical
virtual machines (VMs), which are sometimes
called logical partitions.
Technology pioneered by IBM in 1960s to
better utilize mainframes
25
26. Idea Dates Back to the 1960s
26
IBM Mainframe
IBM VM/370
CMS
App
Native (Full) Virtualization
Examples: Vmware ESX
MVS
App
CMS
App
27. Two Types of Virtualization
Using the hypervisor, each guest OS sees its own
independent copy of the CPU, memory, IO, etc.
27
Physical Hardware
Hyperviser
Unmodified
Guest OS 1
Unmodified
Guest OS 2
Native (Full) Virtualization
Examples: Vmware ESX
Apps
Physical Hardware
Hyperviser
Modified
Guest OS 1
Modified
Guest OS 2
Para Virtualization
Examples: Xen
Apps
28. Four Key Properties
1. Partitioning: run multiple VMs on one
physical server; one VM doesn’t know about
the others
2. Isolation: security isolation is at the hardware
level.
3. Encapsulation: entire state of the machine
can be copied to files and moved around
4. Hardware abstraction: provision and migrate
VM to another server
28
31. The Google Data Stack
The Google File System (2003)
MapReduce: Simplified Data Processing… (2004)
BigTable: A Distributed Storage System… (2006)
31
32. Map-Reduce Example
Input is file with one document per record
User specifies map function
– key = document URL
– Value = terms that document contains
(“doc cdickens”,
“it was the best of times”)
“it”, 1
“was”, 1
“the”, 1
“best”, 1
map
33. Example (cont’d)
MapReduce library gathers together all pairs
with the same key value (shuffle/sort phase)
The user-defined reduce function combines all
the values associated with the same key
key = “it”
values = 1, 1
key = “was”
values = 1, 1
key = “best”
values = 1
key = “worst”
values = 1
“it”, 2
“was”, 2
“best”, 1
“worst”, 1reduce
34. Generalization: Apply User Defined
Functions (UDF) to Files in Storage Cloud
34
map/shuffle reduce
UDFUDF
37. Sector’s Layered Cloud Services
Storage Services
Table Services
Compute Services
37
Sector’s Stack
Applications
Sector’s Distributed File
System (SDFS)
Sphere’s UDF
Routing &
Transport Services
UDP-based Data Transport
Protocol (UDT)
38. Hadoop & Sector
Hadoop Sector
Storage Cloud Block-based file
system
File-based
Programming
Model
MapReduce UDF &
MapReduce
Protocol TCP UDP-based
protocol (UDT)
Replication At time of writing Periodically
Security Not yet HIPAA capable
Language Java C++
38
39. MalStone Benchmark
Benchmark developed by Open Cloud
Consortium for clouds supporting data
intensive computing.
Code to generate synthetic data required is
available from code.google.com/p/malgen
Stylized analytic computation that is easy to
implement in MapReduce and its
generalizations.
39
41. MalStone B Benchmark
41
MalStone B
Hadoop v0.18.3 799 min
Hadoop Streaming v0.18.3 142 min
Sector v1.19 44 min
# Nodes 20 nodes
# Records 10 Billion
Size of Dataset 1 TB
42. Trading Functionality for Scalability
Databases Data Clouds
Scalability 100’s TB 100’s PB
Functionalit
y
Full SQL-based queries,
including joins
Optimized access to sorted
tables (tables with single keys)
Optimized Databases are optimized for
safe writes
Clouds optimized for efficient
reads
Consistency
model
ACID (Atomicity, Consistency,
Isolation & Durability) –
database always consist
Eventual consistency – updates
eventually propagate through
system
Parallelism Difficult because of ACID
model; shared nothing is
possible (Graywolf)
Basic design incorporates
parallelism over commodity
components
Scale Racks Data center
42
43. Not Everyone Agrees
David J. DeWitt and Michael Stonebraker,
MapReduce: A Major Step Backwards,
Database Column, Jane 17, 2008
43
44. Part 6. Standards Efforts
44
Change of gauge at Ussuriisk (near
Vladivostok) at the Chinese –Russian border
Train gauge
in China is
1435 mm
Train gauge
in Russia is
1520 mm
How can a
cloud
application
move from
one cloud
storage
service to
another?
45. Standards Efforts for Clouds
Cloud Computing Interoperability Forum (CCIF)
Open Cloud Consortium (OCC)
Open Grid Forum (OGF)
Distributed Management Task Force (DMTF)
Storage Network Industrial Association (SNIA)
Plus several others…
45
46. www.opencloudconsortium.org
1. Supports the development of standards.
2. Supports reference implementations for
cloud computing, preferably open source.
3. Manages a testbed for cloud computing
called the Open Cloud Testbed.
4. Supports the development of benchmarks.
5. Sponsors workshops and other events related
to cloud computing.
46
47. Activities Currently Focused Around
Five Use Cases
1. Moving an existing cloud application from Cloud
1 to Cloud 2 without changing the application.
2. Providing surge capacity for an application on
Cloud 1 using any of the Clouds 2, 3, … (without
changing the application).
Cloud 1 Cloud 2
1. Migrate / port
2. Surge / burst
48. Large Data Cloud Use Cases
3. Moving a large data cloud application from
one large data cloud storage service to
another.
4. Moving a large data cloud application from
one large data cloud compute service to
another.
Large Data Cloud Storage Services
Large Data Cloud Compute Services
App 1 App 2
49. Inter-Cloud Use Case
5. Inter-cloud communication between two
HIPAA compliant clouds.
Cloud 1 Cloud 2
50. OCC Welcomes New Members
Companies and organizations are welcome to
join the Open Cloud Consortium (OCC)
www.opencloudconsortium.org/membership.html
Join one of our working groups
– Large Data Clouds Working Group
– Standard Cloud Performance Measurement
(SCPM) Working Group
– Information Sharing & Security Working Group
51. For More Information
Contact information:
Robert Grossman
rlg@opendatagroup.com
blog.rgrossman.com
Web sites
– www.opendatagroup.com
– www.ncdm.uic.edu
– www.opencloudconsortium.org
51