This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
1 of 57
More Related Content
Azure data platform overview
2. About Me
Microsoft, Big Data Evangelist
In IT for 30 years, worked on many BI and DW projects
Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM
architect, PDW/APS developer
Been perm employee, contractor, consultant, business owner
Presenter at PASS Business Analytics Conference, PASS Summit, Enterprise Data World conference
Certifications: MCSE: Data Platform, Business Intelligence; MS: Architecting Microsoft Azure
Solutions, Design and Implement Big Data Analytics Solutions, Design and Implement Cloud Data
Platform Solutions
Blog at JamesSerra.com
Former SQL Server MVP
Author of book “Reporting with Microsoft SQL Server 2012”
3. I tried to understand the Microsoft data platform on my own…
And felt like I was body slammed by Randy
Savage:
Let’s prevent that from happening…
4. Data platform continuum
Hybrid Cloud
On premises
Shared
Lower cost
Dedicated
Higher cost
Higher administration Lower administration
Off premises
9. Microsoft Big Data Portfolio
SQL Server Stretch
Business intelligence
Machine learning analytics
Insights
SQL Server 2017
SQL Server 2016 Fast Track
Azure SQL DW
Databricks
Cosmos DB
HDInsight
Hadoop
Analytics Platform System
Sequential Scale Out + AcrossScale Up
Key
Relational Non-relational
On-premisesCloud
Microsoft has solutions covering
and connecting all four
quadrants – that’s why SQL
Server is one of the most utilized
databases in the world
Azure SQL Database
SQL Server in Azure VM
10. VM hosted on Microsoft Azure Infrastructure (“IaaS”)
• From Microsoft images (gallery) or your own images (custom)
SQL 2008R2 / 2012 / 2014 / 2016 / 2017 Web / Standard / Enterprise
Images refreshed with latest version, SP, CU
• Windows Server 2008 R2 / 2012 R2 / 2016, Linux RHEL / Ubuntu
• Fast provisioning (~10 minutes).
• Accessible via RDP and Powershell
• Full compatibility with SQL Server “Box” software
Pay per use
• Per minute (only when running)
• Cost depends on size and licensing
• EA customers can use existing SQL licenses (BYOL)
• Network: only outgoing (not incoming)
• Storage: only used (not allocated)
Elasticity
• 1 core / 2 GB mem / 1 TB 128 cores / 3.5 TB mem / 256 TB
11. Azure SQL Database
A relational database-as-a-service (“PaaS”), fully managed by Microsoft.
For cloud-designed apps when near-zero administration and enterprise-grade capabilities are key.
Perfect for organizations looking to dramatically increase the DB:IT ratio.
12. Azure SQL Database Managed Instance
Managed Instance
Instance scoped programming model with
high compatibility to on-premises databases
Single
Standalone managed database best for
predictable and stable workloads
Elastic pool
Shared resource model best for greater
efficiency through multi-tenancy
Best for modernization at
scale with low cost and effort
13. Supports compatibility modes (SQL Server 2005+), Instance sizes up to 8TB
Security
• TDE
• SQL Audit
• Row level security
• Always Encrypted
14. Scalable High performanceReliable and available
Adapts on-demand to your workload's needs, auto-scaling up to 100TB per database.
100
TB
16. More choices and full integration into Azure’s ecosystem and services
Managed community
MySQL, PostgreSQL,
and MariaDB
Scale in seconds with
built-in high availability
Secure and compliantLanguages and
frameworks of your choice
Industry-leading
global reach
AZURE DATABASE SERVICES FOR
MYSQL, POSTGRESQL, AND MARIADB
Easy Lift and Shift Enterprise Ready
My
17. SMP vs MPP
• Uses many separate CPUs running in parallel to execute a single program
• Shared Nothing: Each CPU has its own memory and disk (scale-out)
• Segments communicate using high-speed network between nodes
MPP - Massively
Parallel Processing
• Multiple CPUs used to complete individual processes simultaneously
• All CPUs share the same memory, disks, and network controllers (scale-up)
• SQL Server implementations traditionally have been SMP
• Mostly, the solution is housed on a shared storage
SMP - Symmetric
Multiprocessing
19. Azure SQL Data Warehouse
A relational data warehouse-as-a-service, fully managed by Microsoft.
Industries first elastic cloud data warehouse with enterprise-grade capabilities.
Support your smallest to your largest data storage needs while handling queries up to 100x faster.
20. AZURE RELATIONAL DATABASE PLATFORM
PowerBI,AppServices,DataFactory,
Analytics,ML,Cognitive,Bot…
Global Azure with 54 Regions
Azure Compute
SQL Data
Warehouse
Azure Storage
SQL Database MariaDBPostgreSQL
Flexible: On-demand scaling, Resource governance
Trusted: HA/DR, Backup/Restore, Security, Audit, Isolation
Intelligent: Advisors, Tuning, Monitoring
Database
Services
Platform
MySQL
21. Azure Database Migration Service (DMS)
A seamless, end-to-end solution for moving on-premises SQL Server, Oracle, and other relational
databases to the cloud.
Azure Database Migration Guide
https://datamigration.microsoft.com/
22. Relational and non-relational defined
Relational databases (RDBMS, SQL Databases)
• Example: Microsoft SQL Server, Oracle Database, IBM DB2
• Mostly used in large enterprise scenarios
• Analytical RDBMS (OLAP, MPP) solutions are SQL DW, Redshift, Teradata, Netezza
Non-relational databases (NoSQL databases)
• Example: Azure Cosmos DB, MongoDB, Cassandra
• Four categories: Key-value stores, Wide-column stores, Document stores and Graph stores
Hadoop: Made up of Hadoop Distributed File System (HDFS), YARN and MapReduce (Ideal for data lake)
OLTP vs OLAP/DW
SMP vs MPP
23. A globally distributed, massively scalable, multi-model database service
Column-family
Document
Graph
Turnkey global distribution
Elastic scale out
of storage & throughput
Guaranteed low latency at the 99th percentile
Comprehensive SLAs
Five well-defined consistency models
Table API
Key-value
Azure Cosmos DB
MongoDB API
Cassandra API
24. Advanced Analytics
Social
LOB
Graph
IoT
Image
CRM
INGEST STORE PREP MODEL & SERVE
(& store)
Data orchestration
and monitoring
Big data store Transform & Clean Data warehouse
AI
BI + Reporting
Azure Data Factory
SSIS
Azure Data Lake
Storage Gen2
Blob Storage
Azure Data Lake
Storage Gen1
SQL Server 2019 Big
Data Cluster
Azure Databricks
Azure HDInsight
PolyBase & Stored
Procedures
Power BI Dataflow
Azure Data Lake Analytics
Azure SQL Data Warehouse
Azure Analysis Services
SQL Database (Single, MI,
HyperScale)
SQL Server in a VM
Cosmos DB
Power BI Aggregations
25. Blob Storage Data Lake Store
Azure Data Lake Storage Gen2
Large partner ecosystem
Global scale – All 50 regions
Durability options
Tiered - Hot/Cool/Archive
Cost Efficient
Built for Hadoop
Hierarchical namespace
ACLs, AAD and RBAC
Performance tuned for big data
Very high scale capacity and throughput
Large partner ecosystem
Global scale – All 50 regions
Durability options
Tiered - Hot/Cool/Archive
Cost Efficient
Built for Hadoop
Hierarchical namespace
ACLs, AAD and RBAC
Performance tuned for big data
Very high scale capacity and throughput
26. LRS
Multiple replicas across
a datacenter
Protect against disk,
node, rack failures
Write is ack’d when all
replicas are committed
Superior to dual-parity
RAID
11 9s of durability
SLA: 99.9%
GRS
Multiple replicas across each
of 2 regions
Protects against major
regional disasters
Asynchronous to secondary
16 9s of durability
SLA: 99.9%
RA-GRS
GRS + Read access to secondary
Separate secondary endpoint
RPO delay to secondary can be
queried
SLA: 99.99% (read), 99.9% (write)
Zone 1
ZRS
Replicas across 3 Zones
Protect against disk, node, rack and
zone failures
Synchronous writes to all 3 zones
12 9s of durability
Available in 8 regions
SLA: 99.9%
Zone 2 Zone 3
27. Caching Layer (Avere tech)
Extra Hot Tier - Premium (SSD + NVME)
Hot Tier (HDD)
Cool Tier (HDD)
Cooler Tier
Archive Tier
Deep Storage Tier (Glass, DNA, etc.)
Analytics Engines
(Hadoop, Spark, SCOPE …)
High Performance
Compute
AI / ML
Current Future
Edge
Azure File
Sync
Azure Backup
Data
Box
Data
Box
Edge
Azure
Stack
REST HDFS NFS SMB …
Automatic
Lifecycle
Management
Avere
FXT
28. File Sync
• Windows Srv <-> Azure
• Local caching
• With offline (Databox) can
'sync' remainder
Fuse
• Mount blobs as local FS
• Commit on write
• Linux
Site Replication
• On premise & cloud
• Windows, Linux
• Physical, virtual
• Hyper-V, VMWare
Network Acceleration
• Aspera
• Signiant
AZCopy
• Throughput +30%
• S3 to Azure Blobs
• Sync to cloud
• Hi Latency 10-100%
NetApp
• CloudSync
• SnapMirror
• SnapVault
Data Factory
• On premise & cloud sources
• Structured & unstructured
• Over 60 connectors
• UI design data flow
Partners
• Peer Global File Service
• Talon FAST
• Zerto
• …
Offline
• Data Box
• Data Box Heavy
• Data Box Disk
• Disk Import / Export
Fast Data Transfer
microsoft.com/en-us/garage/profiles/fast-data-transfer/
29. Data Box Heavy PREVIEW
• Capacity: 1 PB
• Weight 500+ lbs
• Secure, ruggedized
appliance
• Preview September 2018
• Same service as Data Box,
but targeted to petabyte-
sized datasets.
Data Box Gateway PREVIEW
• Virtual device provisioned in
your hypervisor
• Supports storage gateway,
SMB, NFS, Azure blob, files
• Preview: September 2018
• Virtual network transfer
appliance (VM), runs on your
choice of hardware.
Data Box Edge PREVIEW
• Local Cache Capacity: ~12 TB
• Includes Data Box Gateway
and Azure IoT Edge.
• Preview: September 2018
• Data Box Edge manages
uploads to Azure and can
pre-process data prior to
upload.
Data Box
• Capacity: 100 TB
• Weight: ~50 lbs
• Secure, ruggedized
appliance
• GA September 2018
• Data Box enables bulk
migration to Azure when
network isn’t an option.
Data Box DiskPREVIEW
• Capacity: 8TB ea.; 40TB/order
• Secure, ruggedized USB
drives orderable in packs of
5 (up to 40TB).
• Currently in Preview
• Perfect for projects that
require a smaller form factor,
e.g., autonomous vehicles.
Order Fill UploadSend Return Cloud to Edge Edge to Cloud Pre-processing ML Inferencing
Network Data Transfer Edge Compute
Offline Data Transfer Online Data Transfer
30. Exactly what is a data lake?
A storage repository, usually Hadoop, that holds a vast amount of raw data in its native
format until it is needed.
• Inexpensively store unlimited data
• Collect all data “just in case”
• Store data with no modeling – “Schema on read”
• Complements EDW
• Frees up expensive EDW resources
• Quick user access to data
• ETL Hadoop tools
• Easily scalable
• Place to move older data (archive)
• Place to backup data to
32. Objectives
Plan the structure based on optimal data retrieval
Avoid a chaotic, unorganized data swamp
Data Retention Policy
Temporary data
Permanent data
Applicable period (ex: project lifetime)
etc…
Business Impact / Criticality
High (HBI)
Medium (MBI)
Low (LBI)
etc…
Confidential Classification
Public information
Internal use only
Supplier/partner confidential
Personally identifiable information (PII)
Sensitive – financial
Sensitive – intellectual property
etc…
Probability of Data Access
Recent/current data
Historical data
etc…
Owner / Steward / SME
Subject Area
Security Boundaries
Department
Business unit
etc…
Time Partitioning
Year/Month/Day/Hour/Minute
Downstream App/Purpose
Common ways to organize the data:
33. Data Warehouse
Serving, Security & Compliance
• Business people
• Low latency
• Complex joins
• Interactive ad-hoc query
• High number of users
• Additional security
• Large support for tools
• Dashboards
• Easily create reports (Self-service BI)
• Know questions
34. What is Azure Databricks?
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
Best of Databricks Best of Microsoft
Designed in collaboration with the founders of Apache Spark
One-click set up; streamlined workflows
Interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
Native integration with Azure services (Power BI, SQL DW, Cosmos DB, Blob Storage)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise -grade SLAs)
35. Azure
HDInsight
Hadoop and Spark
as a Service on Azure
Fully-managed Hadoop and Spark
for the cloud
100% Open Source Hortonworks
data platform
Clusters up and running in minutes
Managed, monitored and supported
by Microsoft with the industry’s best SLA
Familiar BI tools for analysis, or open source
notebooks for interactive data science
63% lower TCO than deploy your own
Hadoop on-premises*
*IDC study “The Business Value and TCO Advantage of Apache Hadoop in the Cloud with Microsoft Azure HDInsight”
36. Hortonworks Data Platform (HDP) 3.0
Simply put, Hortonworks ties all the open source products together (20)
(under the covers of HDInsight 4.0 – public preview)
39. Compute pool
SQL Compute
Node
SQL Compute
Node
SQL Compute
Node
…
Compute pool
SQL Compute
Node
IoT data
Directly
read from
HDFS
Persistent storage
…
Storage pool
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
Kubernetes pod
Analytics
Custom
apps BI
SQL Server
master instance
Node Node Node Node Node Node Node
SQL
Data mart
SQL Data
Node
SQL Data
Node
Compute pool
SQL Compute
Node
Storage Storage
46. Q & A ?
James Serra, Big Data Evangelist
Email me at: jamesserra3@gmail.com
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (where this slide deck is posted via the “Presentations” link on the top menu)
49. INGEST STORE PREP & TRAIN MODEL & SERVE
C L O U D D A T A W A R E H O U S E
Azure Data Lake Store Gen2
Logs (unstructured)
Azure Data Factory
Microsoft Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the above architecture to meet their unique needs.
Media (unstructured)
Files (unstructured)
PolyBase
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
50. INGEST STORE PREP & TRAIN MODEL & SERVE
M O D E R N D A T A W A R E H O U S E
Azure Data Lake Store Gen2
Logs (unstructured)
Azure Data Factory
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the above architecture to meet their unique needs.
Media (unstructured)
Files (unstructured)
PolyBase
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
51. A D V A N C E D A N A L Y T I C S O N B I G D A T A
INGEST STORE PREP & TRAIN MODEL & SERVE
Cosmos DB
Business/custom apps
(structured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
PolyBase
SparkR
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet
their unique needs.
Real-time apps
52. INGEST STORE PREP & TRAIN MODEL & SERVE
R E A L T I M E A N A L Y T I C S
Sensors and IoT
(unstructured)
Apache Kafka for
HDInsight
Cosmos DB
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory
Azure Databricks
Real-time apps
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
Microsoft Azure also supports other Big Data services like Azure IoT Hub, Azure Event Hubs, Azure Machine Learning to allow customers to
tailor the above architecture to meet their unique needs.
PolyBase
53. INGEST STORE MODEL & SERVE
D A T A M A R T C O N S O L I D A T I O N
Azure Data Lake Store Gen2 Azure SQL
Data Warehouse
Azure Data Factory Azure Analysis
Services
Power BI
RDBMS data marts
Hadoop
Microsoft Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the architecture to meet their unique needs.
PolyBase
54. INGEST STORE PREP & TRAIN MODEL & SERVE
H U B & S P O K E A R C H I T E C T U R E F O R B I
Azure SQL
Data Warehouse
PolyBase
Business/custom apps
(structured)
Power BI
Microsoft Azure supports other services like Azure HDInsight to allow customers a truly customized solution.
Multiple Azure Analysis
Services instances
SQL
Multiple Azure SQL
Database instances
Data Marts
Data Cubes
Azure Databricks
Logs (unstructured)
Media (unstructured)
Files (unstructured)
Azure Data Lake Store Gen2Azure Data Factory
55. INGEST STORE PREP & TRAIN MODEL & SERVE
A U T O S C A L I N G D A T A W A R E H O U S E
Microsoft Azure supports other services like Azure HDInsight to allow customers a truly customized solution.
Azure Analysis
Services
Azure Functions
(Auto-scaling)
Business/custom apps
(structured)
Logs (unstructured)
Media (unstructured)
Files (unstructured)
Azure SQL
Data Warehouse
PolyBase
Power BIAzure Data Lake Store Gen2Azure Data Factory
Azure Databricks
56. D A T A W A R E H O U S E M I G R A T I O N
INGEST STORE PREP & TRAIN MODEL & SERVE
Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the architecture to meet their unique needs.
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Business/custom apps
Azure Data Lake Store Gen2
Logs (unstructured)
Azure Data Factory Azure Databricks
Media (unstructured)
Files (unstructured)
Azure Analysis
Services
Power BI
PolyBase
Editor's Notes
Data Platform Overview
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.
http://www.ispot.tv/ad/7f64/directv-hang-gliding
Mention WWF, then WWE
What is Randy Savage’s nickname (Macho Man)? Who won the final match of the first WrestleMania Hulk Hogan and Mr. T def. Roddy Piper and Paul Orndorff. Who won in the final match for WrestleMania III: Hulk Hogan (c) def André the Giant for the WWF World Heavyweight Championship. What was the name of the show starring "Rowdy" Roddy Piper: Piper’s Pit. What was the nickname for announcer Gene Okerlund: Mean Gene. Who did Hulk Hogan defeat in 1984 to become World Champion? The Iron Sheik Who did Andre the Giant eliminate to win the battle royal at WrestleMania 2? Bret "The Hitman" Hart What was the nickname of Jimmy Snuka? "Superfly"
One of the first things to understand in any discussion of Azure versus on-premises SQL Server databases is that you can use it all. Microsoft’s Data Platform leverages SQL Server technology and makes it available across physical on-premises machines, private cloud environments, third party hosted private cloud environments, and public cloud. This enables you to meet unique and diverse business needs through a combination of on-premises and cloud-hosted deployments, while using the same set of server products, development tools, and expertise across these environments.
As seen in the diagram, each offering can be characterized by the level of administration you have over the infrastructure (on the X axis), and by the degree of cost efficiency achieved by database level consolidation and automation (on the Y axis).
When designing an application, four basic options are available for hosting the SQL Server part of the application:
SQL Server on nonvirtualized physical machines
SQL Server in on-premises virtualized machines (private cloud)
SQL Server in Azure Virtual Machine (public cloud)
Azure SQL Database (public cloud)
Before the advent of the cloud. FInd someone to provision physical hardware, servers on the hardware, and applications on those physical servers. So many! Lots of steps, parts, counter productive.
All good things begin with a story.
So before I talk to you about serverless, I wanted to discuss some history of how apps have been built through the years and how that has led us to the computing options we have today.
Buy your own hardware and software
Run it in your own data-centers
Deal with a multitude of questions
# of servers
Physical security
Hardware failure
Software patching
Backup
Essentially a lot of questions, not all of which might be directly related to running your business. For example, if you are running an insurance company, you want to spend your time growing and innovating in the insurance domain, not worrying about how to handle servers.
Along came the cloud, way faster than provisioning hardware. So much innovation and rapid appilcation building. BUt Still have questions about servers and scaling.
Along came the cloud, and provided some relief from some of those questions.
IaaS in the cloud, allowed you to forget about managing all that physical hardware. Instead of hosting it yourself, you could now utilize servers hosted by someone else. While the worries around managing physical infrastructure are gone, there are a number of questions that still need to thought about:
How many virtual machines do I need?
How many would I need in future when my usage grows?
How to handle updating the servers for the latest security patches
How to think about deploying my code and the various dependencies, such as application runtimes, to the server
Who monitors my apps and so on
This means that you still haven’t reached that ideal state where you could focus purely on your business and its problems.
Trying to remove infrastructure. Don't care about patching OS or manging instance of your VM. All you care about is your web app – still have chores / non-business questions.
Well, the cloud continue to help with that problem and with the advent of PaaS, there was even more simplification. PaaS allows you to just worry about your app, and let the cloud vendor take care of not only the physical hardware, but also the ongoing management of the operating environment like Operating Systems, Virtual Machines or Containers, as well as the various application runtimes, whether it is Java Runtime, .NET, Node.js, and so on.
You are not completely free from thinking about servers however, since you still have to worry about how to scale your app and accordingly specify the number and size of the various compute resources your apps would need at various times.
The server exists, it's not yours. NO questions about the server.
The latest in this series of evolution is Serverless – which further abstracts the underlying infrastructure from you, and allows you to focus only on a single question – how are you going to build your app to achieve the best results for your business, or whatever it is that you are trying to do with your apps.
Whether it is the code to delight your customers through a great experience, or the logic to communicate securely with a business partner, that is where most organization want to spend their creative energies on.
And serverless allows you to do just that, focus on your code and forget about the infrastructure.
This is why we believe it is the platform for next generation of apps.
Now before we jump into explaining the benefits of serverless, let’s call out what it’s not. It’s not the total lack of servers, that would a bit difficult. Instead, it’s a system where you don’t have to think about Servers at all, so for you, they are invisible.
Our portfolio of products provides customers with the power to deploy the solution that suits their business needs.
Your choice of platform, whether on-premises, hybrid or private or public cloud, doesn’t limit you now or in the future. Migrating or expanding becomes an easy process and doesn’t require excessive downtime or introduce potential threats to your business success.
With Microsoft, you can seamlessly scale up to larger processing and storage capabilities, or scale out by adding additional servers in parallel arrangement.
T: SQL Server is a trusted market leader, and it’s the cornerstone of our data warehouse offering.
With digital transformation in mind, let’s focus on how SQL Database provides the low-cost, low-friction option to migrating your SQL Server data at scale to SQL Database – without having to re-architect your apps.
Introducing Azure SQL Database Managed Instance
SQL Database Managed Instance is an expansion of the existing SQL Database service designed to enable database lift-and-shift to a fully-managed PaaS, without re-designing the application. SQL Database Managed Instance provides high compatibility with the on-premises SQL Server programming model and out-of-box support for the large majority of SQL Server features and accompanying tools and services.
It’s important to note that Managed Instance isn’t a new service – it is a third deployment option within Azure SQL Database, sitting alongside single databases and elastic pools. As part of Azure SQL Database, Microsoft’s fully managed cloud database service, it inherits all its built-in PaaS features.
The Hyperscale service tier is a highly scalable performance tier that leverages the Azure architecture to scale out resources for SQL Database substantially beyond the limits available for the General Purpose and Business Critical service tiers.
Hyperscale provides the following additional capabilities:
Reliable and available
Because it’s part of SQL Database, it has multiple levels of redundancy and no single points of failure. It also provides a 99.99% availability SLA.
Scalable
Support for up to 100 TB of database size
Compute and storage scale independently
Nearly instantaneous database backups (based on file snapshots stored in Azure Blob storage) regardless of size with no IO impact on Compute
High performance
Fast database restores (based on file snapshots) in minutes rather than hours or days (not a size of data operation)
Higher overall performance due to higher log throughput and faster transaction commit times regardless of data volumes
Rapid Scale up - you can, in constant time, scale up your compute resources to accommodate heavy workloads when needed, and then scale the resources back down when not needed.
Rapid scale out - you can provision one or more read-only nodes for offloading your read workload and for use as hot-standbys
Azure Database for MySQL provides fully managed enterprise ready community MySQL database as a service for service for app development and deployment. Being community MySQL allows you to easily lift and shift to the cloud and use languages and frameworks for your choice. On top of that you get built-in high availability and capability to scale in seconds, helping you easily adjust to changes in customer demands. Additionally, you benefit from the unparalleled security and compliance, including Azure IP advantage, as well as Azure’s industry leading reach with more datacenters than any other cloud provider. All this with a flexible pricing model so you can choose resources for your workload with no hidden cost.
Languages and Frameworks of your choice
Being based on the community-editions of MySQL, PostgreSQL and MariaDB mean you can use the existing development languages frameworks and tools you already use for your apps. In addition, Azure Database Services are deeply integrated with Azure Web Apps to provide a streamlined provisioning and management experience for common frameworks (like WordPress, Drupal, Joomla) and languages (PHP, Node.js, Ruby) to provide a best-in-class PaaS experience.
Scale in Seconds with built-in HA
Azure Database Services are built upon the same service fabric framework that has been powering SQL Database for years. Unlike an VM-based PaaS offering like AWS RDS, Azure Database Services do not have the overhead a full VM stack has (e.g.; Linux OS + DB). Running on in a secured container implementation (SQLPAL, a very light-weight SQL OS), Azure Database Services can provision a new server in seconds in the event that a primary server hangs or crashes whereas in a traditional VM-based implementation the entire Linux (or Windows) OS stack has to bootstrap before the DB service loads. This means the entire experience of a failover can happen in as little as 30-45 seconds – and most importantly WITHOUT the need for a replica. AWS RDS requires deployment in Multi-AZ in order to achieve 99.95% SLA, which doubles your costs as you have 2 DB servers running at all times. With Azure Database Services, no replicas are needed which means no additional cost, or maintenance, by the customer.
Additionally, this HA infrastructure enables the ability for Azure Database Services to scale performance on the fly. When a customer needs to scale-up for workload spikes, by simply changing a slider in the portal, a new server is provisioned at a higher performance level and the previous server’s DNS name and storage is connected to the new instance. Scaling can take a little time as 20 seconds meaning customers can scale performance, up or down, with little/no downtime to the application.
Secure and Compliant
“Secure by default” is the standard for any Azure service, meaning elements such as SSL encryption between the database and application are turned on by default. Additionally, all data at-rest is encrypted by default in Azure storage using AES 256 bit encryption. And since Azure Database Services are using OSS database engines, the Azure IP Advantage means that customers do not have to worry about litigation using an OSS product in Azure. Microsoft provides indemnification for any OSS first party workload in Azure.
Industry-leading global reach
With more regions across the globe than any other public cloud provider, Azure offers the ability to have the most globally distributed MySQL, PostgreSQL or MariaDB-based application in the world.
SMP is one server where each CPU in the server shares the same memory, disk, and network controllers (scale-up). MPP means data is distributed among many independent servers running in parallel and is a shared-nothing architecture, where each server operates self-sufficiently and controls its own memory and disk (scale-out).
SMP is one server where each CPU in the server shares the same memory, disk, and network controllers (scale-up). MPP means data is distributed among many independent servers running in parallel and is a shared-nothing architecture, where each server operates self-sufficiently and controls its own memory and disk (scale-out).
Azure Cosmos DB offers the first globally distributed, multi-model database service for building planet scale apps. It’s been powering Microsoft’s internet-scale services for years, and now it’s ready to launch yours.
Only Azure Cosmos DB makes global distribution turn-key.
You can add Azure locations to your database anywhere across the world, at any time, with a single click. Cosmos DB will seamlessly replicate your data and make it highly available.
Cosmos DB allows you to scale throughput and storage elastically, and globally! You only pay for the throughput and storage you need – anywhere in the world, at any time.
Transfer options:
https://www.microsoft.com/en-us/garage/profiles/fast-data-transfer/
Azure data box
Also called bit bucket, staging area, landing zone or enterprise data hub (Cloudera)
http://www.jamesserra.com/archive/2014/05/hadoop-and-data-warehouses/
http://www.jamesserra.com/archive/2014/12/the-modern-data-warehouse/
http://adtmag.com/articles/2014/07/28/gartner-warns-on-data-lakes.aspx
http://intellyx.com/2015/01/30/make-sure-your-data-lake-is-both-just-in-case-and-just-in-time/
http://www.blue-granite.com/blog/bid/402596/Top-Five-Differences-between-Data-Lakes-and-Data-Warehouses
http://www.martinsights.com/?p=1088
http://data-informed.com/hadoop-vs-data-warehouse-comparing-apples-oranges/
http://www.martinsights.com/?p=1082
http://www.martinsights.com/?p=1094
http://www.martinsights.com/?p=1102
https://www.sqlchick.com/entries/2017/12/30/zones-in-a-data-lake
https://www.sqlchick.com/entries/2016/7/31/data-lake-use-cases-and-planning
Question: Do you see many companies building data lakes?
Raw: Raw events are stored for historical reference. Also called staging layer or landing area
Cleansed: Raw events are transformed (cleaned and mastered) into directly consumable data sets. Aim is to uniform the way files are stored in terms of encoding, format, data types and content (i.e. strings). Also called conformed layer
Application: Business logic is applied to the cleansed data to produce data ready to be consumed by applications (i.e. DW application, advanced analysis process, etc). This is also called by a lot of other names: workspace, trusted, gold, secure, production ready, governed, presentation
Sandbox: Optional layer to be used to “play” in. Also called exploration layer or data science workspace
Azure Databricks features –
Enhance your teams’ productivity
Get started quickly by launching your new Spark environment with one click.
Share your insights in powerful ways through rich integration with PowerBI.
Improve collaboration amongst your analytics team through a unified workspace.
Innovate faster with native integration with rest of Azure platform.
Build on the most compliant and trusted cloud
Simplify security and identity control with built-in integration with Active Directory.
Regulate access with fine-grained user permissions to Azure Databricks’ notebooks, clusters, jobs and data.
Build with confidence on the trusted cloud backed by unmatched support, compliance and SLAs.
Scale without limits
Operate at massive scale without limits globally.
Accelerate data processing with the fastest Spark engine.
Key points: Summarize key benefits for Azure Analysis Services
Azure Analysis Services helps you transform complex data from different data sources into a BI semantic model, so users in your organization can easily gain insights by connecting to the data models using tools like Excel, Power BI, and others to create reports and perform ad hoc data analysis
Talk Track
Transform Complex Data into rich BI semantic models: Azure Analysis Services Analysis Services helps you transform complex data into a single business user friendly data model making it easy for business users to understand and analyze data across different data sources.
Gain instant insights with in-memory cache using your preferred visualization tools : Not only can business users get insights from data easily using their preferred data visualization tool, whether it is Power BI, Excel or other major data visualization tools, but with the in-memory cache capabilities of Azure Analysis Services, users can gain insights over billions of rows of data at the speed of thought
Proven Technology: Azure Analysis Services is based on the proven analytics engine in SQL Server 2016 Analysis Services, that has helped organizations turn complex data into a trusted, single source of truth for years. This means that BI professionals who are familiar with SQL Server Analysis Services, tabular models can get started quickly and do not need to learn new tools or skills.
Analytics engine as-a-service (provision fast, scale faster): The same proven enterprise grade BI platform is now available as a fully managed service in Azure. With the power of the trusted Microsoft Cloud, you do not need to manage infrastructure on-premises and can benefit from the scalability of the cloud. Additionally you can use Azure Resource Manager to create and deploy an Azure Analysis Services instance within seconds, and use backup restore to quickly move your existing models to Azure Analysis Services and take advantage of the scale, flexibility and management benefits of the cloud. Scale up, scale down, or pause the service and pay only for what you use.
Azure Analysis Services is built for Hybrid BI - Organizations store data in the cloud and on-premises. Azure Analysis Services is built for hybrid data. Data can be access in the cloud, on-premises or a combination of both, enabling a hybrid solution. So - you do not have to move on-premises data to the cloud.
To summarize, Azure Analysis Services is simple to use – it is easy to get started, you can use your existing skills to create BI semantic models, and your favorite data visualizations tools to analyze your data.
Increase analytics and apps performance with scale out data marts
3/17/2019
Microsoft Azure supports other services like Azure HDInsight, Azure Data Lake, Azure IoT Hub, Azure Events Hub in various layers of the architecture above to allow customers a truly customized solution.
1) Copy source data into the Azure Data Lake Store (twitter data example)2) Massage/filter the data using Hadoop (or skip using Hadoop and use stored procedures in SQL DW/DB to massage data after step #5)3) Pass data into Azure ML to build models using Hive query (or pass in directly from Azure Data Lake Store)4) Azure ML feeds prediction results into the data warehouse5) Non-relational data in Azure Data Lake Store copied to data warehouse in relational format (optionally use PolyBase with external tables to avoid copying data)6) Power BI pulls data from data warehouse to build dashboards and reports7) Azure Data Catalog captures metadata from Azure Data Lake Store and SQL DW/DB8) Power BI and Excel can pull data from the Azure Data Lake Store via HDInsight9) To support high concurrency if using SQL DW, or for easier end-user data layer, create an SSAS cube