Azure SQL Database (SQL DB) is a database-as-a-service (DBaaS) that provides nearly full T-SQL compatibility so you can gain tons of benefits for new databases or by moving your existing databases to the cloud. Those benefits include provisioning in minutes, built-in high availability and disaster recovery, predictable performance levels, instant scaling, and reduced overhead. And gone will be the days of getting a call at 3am because of a hardware failure. If you want to make your life easier, this is the presentation for you.
With the recent release of SQL Server 2016 SP1 providing a consistent programming surface area has generated quite a buzz in the SQL Server community. SQL Server 2016 SP1 allows businesses of all sizes to leverage full feature set such as In-Memory technologies on all editions of SQL Server to get enterprise grade performance. This presentation focuses on the new improvements, new limits on the lower editions, differentiating factors and key scenarios enabled by SQL Server 2016 SP1 which makes SQL Server 2016 SP1 an obvious choice for the customers. This session was delivered to PASS VC DBA fundamentals chapter for everyone to learn about these exciting new improvements announced with SQL Server 2016 SP1 to ensure they are leveraging them to maximize performance and throughput of your SQL Server environment.
Sql vs NO-SQL database differences explainedSatya Pal
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
Stretch Database allows migrating historical transactional data from an on-premises SQL Server database transparently to Microsoft Azure cloud storage. It enables seamless queries of data regardless of its location. Some limitations include inability to enforce uniqueness on stretched tables and limitations on allowed actions. Performance can degrade due to the additional overhead of query translation and data movement between on-premises and cloud locations. Remote data files provide an alternative method of archiving to cloud storage without changes to table structures but only overhead is additional latency.
The document provides details about an SQL expert's background and certifications. It summarizes the expert's career starting in 1982 working with computers and 1988 starting in the computer industry. In 1996, they started working with SQL Server 6.0 and have since earned multiple Microsoft certifications. The expert now provides training and consultation services, and created an online school called SQL School Greece to teach SQL Server.
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.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
This document provides an overview of Azure SQL Data Warehouse (SQL DWH), a cloud data warehouse service. It discusses SQL DWH's massively parallel processing (MPP) architecture that allows independent scaling of compute and storage. The document demonstrates how to create a SQL DWH, load data using PolyBase, and use common tools. It is intended to help users understand what SQL DWH is, how it works, and common scenarios it can be used for, such as processing large volumes of data without needing to purchase and manage hardware.
This document provides an introduction and overview of Azure DocumentDB. It discusses how DocumentDB is a fully managed NoSQL database service that provides fast and predictable performance for JSON data through SQL querying capabilities. It also describes how DocumentDB offers features like elastic scaling, high availability, global distribution and ease of development. The document then provides information on starting with DocumentDB, writing queries, and programming capabilities within DocumentDB like stored procedures and triggers.
The document discusses technologies within the Microsoft SQL family and Azure SQL that can help organizations address requirements of the General Data Protection Regulation (GDPR). It covers features for discovering and classifying personal data, managing access and controlling how data is used, and protecting data through encryption, auditing and other security controls. Built-in technologies like dynamic data masking, row-level security, authentication options, and transparent data encryption are described as ways SQL Server and Azure SQL Database can help organizations comply with GDPR.
This document provides a summary of Antonios Chatzipavlis's background and experience working with SQL Server. It details his career starting with SQL Server 6.0 in 1996 and earning his first Microsoft certification. It lists the various Microsoft certifications and roles he has held, including becoming an MVP for SQL Server. It also introduces his creation of SQL School Greece in 2012 to share his knowledge.
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
This document discusses Microsoft Azure, a cloud computing platform. It provides an overview of Azure's capabilities including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It highlights key Azure services such as virtual machines, SQL database, web apps, machine learning, and more. The document also discusses how Azure enables businesses to rapidly setup environments, scale infrastructure, and increase efficiency at a lower cost compared to on-premises solutions.
SQL Server 2016 introduces new capabilities to help improve performance, security, and analytics:
- Operational analytics allows running analytics queries concurrently with OLTP workloads using the same schema. This provides minimal impact on OLTP and best performance.
- In-Memory OLTP enhancements include greater Transact-SQL coverage, improved scaling, and tooling improvements.
- The new Query Store feature acts as a "flight data recorder" for databases, enabling quick performance issue identification and resolution.
Introduction to Windows Azure Data ServicesRobert Greiner
This document provides an overview of using Azure for data management. It discusses using PartitionKey and RowKey to organize data into partitions in Azure table storage. It also recommends using the Azure Storage Client library for .NET applications and describes retry policies for handling errors. Links are provided for additional documentation on Azure table storage and messaging between Azure services.
Experience SQL Server 2017: The Modern Data PlatformBob Ward
This is an overview of SQL Server 2017 and its features and capabilities. You can get the recording at https://youtu.be/qgSEwpaRul0
The document provides an overview of SQL Azure, a relational database service available on the Microsoft Azure platform. Key points include:
- SQL Azure allows users to build applications that use a relational database in the cloud without having to manage infrastructure.
- It is based on SQL Server and provides a familiar programming model, but is designed for the cloud with high availability and scalability.
- The service has limitations on database size and does not provide built-in sharding capabilities, so applications need to implement custom partitioning logic for large datasets.
- Future improvements may address limitations and open up new scenarios and opportunities through integration with other Azure services. SQL Azure is part of Microsoft's broader strategy around cloud-
Moving to the cloud; PaaS, IaaS or Managed InstanceThomas Sykes
In this session we'll look at the cloud choices available in Azure for SQL Server. Whether it's PaaS, IaaS or Managed Instance we'll look into the features provided, the major differences and the Pros and Cons of each solution and how to choose the best option available.
This document provides an overview of Apache Cassandra, including:
- Cassandra is an open source distributed database designed to handle large amounts of data across commodity servers.
- It was originally created at Facebook and is influenced by Amazon Dynamo and Google Bigtable.
- Cassandra uses a peer-to-peer distributed architecture with no single point of failure and supports replication across multiple data centers.
- It uses a column-oriented data model with tunable consistency levels and supports the Cassandra Query Language (CQL) which is similar to SQL.
- Major companies that use Cassandra include Facebook, Netflix, Twitter, IBM and more for its scalability, availability and flexibility.
Cassandra Community Webinar: From Mongo to Cassandra, Architectural LessonsDataStax
We'll be covering some aspects of our architecture, highlighting differences between MongoDB and Cassandra. We'll go in depth to explain why Cassandra is a better choice for our general purpose Application Platform (SHIFT) as well as our Media Buying Analytics tool (the SHIFT Media Manager). We'll be going over common design patterns people might be familiar with coming from a background with MongoDB and highlight how Cassandra would be used as a better alternative. We'll also touch more on cqlengine which is nearing feature completeness as the Cassandra object mapper for Python.
Sql server hybrid what every sql professional should knowBob Ward
This document discusses Microsoft's SQL Server and its capabilities for developing and deploying across on-premises and cloud environments with a single consistent data platform. It highlights tools for backup, availability, encryption, and querying external storage in Microsoft Azure. SQL Server Stretch Database is described as a hybrid solution that securely migrates cold data to Azure while allowing remote query processing with applications continuing to use the on-premises database. The Database Migration Assistant is also mentioned as a tool.
SQL Server 2017 includes several new features such as support for Linux, graph database tables, resumable online indexes, machine learning services using R and Python, and automatic query optimization. It also includes enhancements to memory-optimized tables and new functions. SQL Server on Linux is now available for Red Hat, Suse, and Ubuntu but does not support all features yet, requiring tools like SSMS or VS Code for management. Graph database tables allow easy creation of node and edge tables to model many-to-many relationships. Resumable online indexes allow pausing and resuming long-running index rebuilds. Machine learning services were renamed and can run Python stored procedures. Automatic query optimization continuously monitors performance and forces better plans.
PASS VC: SQL Server Performance Monitoring and BaseliningPARIKSHIT SAVJANI
When managing large scale deployment of SQL Server instances, it is important for DBAs to setup proactive monitoring & establishing performance baselines which helps in performance tuning, capacity planning & identifying workload patterns. Attend this session to learn what data should a DBAs collect & how, to monitor & establish performance baseline in SQL Server.
SQL Server 2016 introduces several new features for In-Memory OLTP including support for up to 2 TB of user data in memory, system-versioned tables, row-level security, and Transparent Data Encryption. The in-memory processing has also been updated to support more T-SQL functionality such as foreign keys, LOB data types, outer joins, and subqueries. The garbage collection process for removing unused memory has also been improved.
SQL Server 2017 Enhancements You Need To KnowQuest
In this session, database experts Pini Dibask and Jason Hall reveal the lesser-known features that’ll help you improve database performance in record time.
Keep your environment always on with sql server 2016 sql bits 2017Bob Ward
This document provides a summary of SQL Server Always On Availability Groups features including enhancements to performance and manageability, read-only secondary replicas, load balancing, and DTC support. It also discusses diagnostic tools like Extended Events and DMVs for monitoring Availability Groups and automatic seeding between replicas.
La Experiencia del Gobierno de Guadalajara - Mario Ramón SilvaFagner Glinski
El documento presenta el nuevo modelo de movilidad sustentable implementado en Guadalajara, México. Este modelo busca contrarrestar los efectos negativos del modelo basado en el transporte privado, como la congestión y la segregación urbana, a través de políticas como la creación de infraestructura peatonal y ciclista segura, la educación vial, y el fomento del desarrollo urbano orientado al transporte público. El modelo también incluye la regulación del estacionamiento y la participación ciudadana para mejorar la movilidad de man
This document provides an introduction and overview of Azure DocumentDB. It discusses how DocumentDB is a fully managed NoSQL database service that provides fast and predictable performance for JSON data through SQL querying capabilities. It also describes how DocumentDB offers features like elastic scaling, high availability, global distribution and ease of development. The document then provides information on starting with DocumentDB, writing queries, and programming capabilities within DocumentDB like stored procedures and triggers.
The document discusses technologies within the Microsoft SQL family and Azure SQL that can help organizations address requirements of the General Data Protection Regulation (GDPR). It covers features for discovering and classifying personal data, managing access and controlling how data is used, and protecting data through encryption, auditing and other security controls. Built-in technologies like dynamic data masking, row-level security, authentication options, and transparent data encryption are described as ways SQL Server and Azure SQL Database can help organizations comply with GDPR.
This document provides a summary of Antonios Chatzipavlis's background and experience working with SQL Server. It details his career starting with SQL Server 6.0 in 1996 and earning his first Microsoft certification. It lists the various Microsoft certifications and roles he has held, including becoming an MVP for SQL Server. It also introduces his creation of SQL School Greece in 2012 to share his knowledge.
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
This document discusses Microsoft Azure, a cloud computing platform. It provides an overview of Azure's capabilities including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It highlights key Azure services such as virtual machines, SQL database, web apps, machine learning, and more. The document also discusses how Azure enables businesses to rapidly setup environments, scale infrastructure, and increase efficiency at a lower cost compared to on-premises solutions.
SQL Server 2016 introduces new capabilities to help improve performance, security, and analytics:
- Operational analytics allows running analytics queries concurrently with OLTP workloads using the same schema. This provides minimal impact on OLTP and best performance.
- In-Memory OLTP enhancements include greater Transact-SQL coverage, improved scaling, and tooling improvements.
- The new Query Store feature acts as a "flight data recorder" for databases, enabling quick performance issue identification and resolution.
Introduction to Windows Azure Data ServicesRobert Greiner
This document provides an overview of using Azure for data management. It discusses using PartitionKey and RowKey to organize data into partitions in Azure table storage. It also recommends using the Azure Storage Client library for .NET applications and describes retry policies for handling errors. Links are provided for additional documentation on Azure table storage and messaging between Azure services.
Experience SQL Server 2017: The Modern Data PlatformBob Ward
This is an overview of SQL Server 2017 and its features and capabilities. You can get the recording at https://youtu.be/qgSEwpaRul0
The document provides an overview of SQL Azure, a relational database service available on the Microsoft Azure platform. Key points include:
- SQL Azure allows users to build applications that use a relational database in the cloud without having to manage infrastructure.
- It is based on SQL Server and provides a familiar programming model, but is designed for the cloud with high availability and scalability.
- The service has limitations on database size and does not provide built-in sharding capabilities, so applications need to implement custom partitioning logic for large datasets.
- Future improvements may address limitations and open up new scenarios and opportunities through integration with other Azure services. SQL Azure is part of Microsoft's broader strategy around cloud-
Moving to the cloud; PaaS, IaaS or Managed InstanceThomas Sykes
In this session we'll look at the cloud choices available in Azure for SQL Server. Whether it's PaaS, IaaS or Managed Instance we'll look into the features provided, the major differences and the Pros and Cons of each solution and how to choose the best option available.
This document provides an overview of Apache Cassandra, including:
- Cassandra is an open source distributed database designed to handle large amounts of data across commodity servers.
- It was originally created at Facebook and is influenced by Amazon Dynamo and Google Bigtable.
- Cassandra uses a peer-to-peer distributed architecture with no single point of failure and supports replication across multiple data centers.
- It uses a column-oriented data model with tunable consistency levels and supports the Cassandra Query Language (CQL) which is similar to SQL.
- Major companies that use Cassandra include Facebook, Netflix, Twitter, IBM and more for its scalability, availability and flexibility.
Cassandra Community Webinar: From Mongo to Cassandra, Architectural LessonsDataStax
We'll be covering some aspects of our architecture, highlighting differences between MongoDB and Cassandra. We'll go in depth to explain why Cassandra is a better choice for our general purpose Application Platform (SHIFT) as well as our Media Buying Analytics tool (the SHIFT Media Manager). We'll be going over common design patterns people might be familiar with coming from a background with MongoDB and highlight how Cassandra would be used as a better alternative. We'll also touch more on cqlengine which is nearing feature completeness as the Cassandra object mapper for Python.
Sql server hybrid what every sql professional should knowBob Ward
This document discusses Microsoft's SQL Server and its capabilities for developing and deploying across on-premises and cloud environments with a single consistent data platform. It highlights tools for backup, availability, encryption, and querying external storage in Microsoft Azure. SQL Server Stretch Database is described as a hybrid solution that securely migrates cold data to Azure while allowing remote query processing with applications continuing to use the on-premises database. The Database Migration Assistant is also mentioned as a tool.
SQL Server 2017 includes several new features such as support for Linux, graph database tables, resumable online indexes, machine learning services using R and Python, and automatic query optimization. It also includes enhancements to memory-optimized tables and new functions. SQL Server on Linux is now available for Red Hat, Suse, and Ubuntu but does not support all features yet, requiring tools like SSMS or VS Code for management. Graph database tables allow easy creation of node and edge tables to model many-to-many relationships. Resumable online indexes allow pausing and resuming long-running index rebuilds. Machine learning services were renamed and can run Python stored procedures. Automatic query optimization continuously monitors performance and forces better plans.
PASS VC: SQL Server Performance Monitoring and BaseliningPARIKSHIT SAVJANI
When managing large scale deployment of SQL Server instances, it is important for DBAs to setup proactive monitoring & establishing performance baselines which helps in performance tuning, capacity planning & identifying workload patterns. Attend this session to learn what data should a DBAs collect & how, to monitor & establish performance baseline in SQL Server.
SQL Server 2016 introduces several new features for In-Memory OLTP including support for up to 2 TB of user data in memory, system-versioned tables, row-level security, and Transparent Data Encryption. The in-memory processing has also been updated to support more T-SQL functionality such as foreign keys, LOB data types, outer joins, and subqueries. The garbage collection process for removing unused memory has also been improved.
SQL Server 2017 Enhancements You Need To KnowQuest
In this session, database experts Pini Dibask and Jason Hall reveal the lesser-known features that’ll help you improve database performance in record time.
Keep your environment always on with sql server 2016 sql bits 2017Bob Ward
This document provides a summary of SQL Server Always On Availability Groups features including enhancements to performance and manageability, read-only secondary replicas, load balancing, and DTC support. It also discusses diagnostic tools like Extended Events and DMVs for monitoring Availability Groups and automatic seeding between replicas.
La Experiencia del Gobierno de Guadalajara - Mario Ramón SilvaFagner Glinski
El documento presenta el nuevo modelo de movilidad sustentable implementado en Guadalajara, México. Este modelo busca contrarrestar los efectos negativos del modelo basado en el transporte privado, como la congestión y la segregación urbana, a través de políticas como la creación de infraestructura peatonal y ciclista segura, la educación vial, y el fomento del desarrollo urbano orientado al transporte público. El modelo también incluye la regulación del estacionamiento y la participación ciudadana para mejorar la movilidad de man
Lakhbinder Singh has over 11 years of experience in telecommunications network planning and optimization. He currently leads a team of 21 transmission planners at ZTE India, where he is responsible for designing microwave radio networks for various mobile operators. Prior to this, he held transmission planning roles at other telecom companies and has extensive expertise in 2G, 3G, and IP-based network design and implementation.
La meteorización es el proceso de descomposición y desintegración de las rocas y minerales por la acción de agentes externos como el agua, el aire, los organismos vivos y las variaciones de temperatura. Se divide en meteorización física, química y biológica. La meteorización física produce desintegración a través de procesos como la termoclastia, la corrosión y la gelifracción. La meteorización química incluye reacciones como la hidrólisis, la oxidación, la carbonata
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This document provides 10 tips for preventing cyber-bullying such as educating yourself on cyber safety, protecting your passwords, keeping photos appropriate, not opening unknown messages, logging out of accounts, pausing before posting, raising awareness of cyber-bullying, setting privacy controls, checking your online presence, and avoiding cyber-bullying others.
This document provides details on Andri Yulianto's personal and professional background, including his education in industrial engineering from Wr. Supratman University in Surabaya, Indonesia from 1997-2002. It outlines his extensive work experience and training in HSE roles over the past 15 years in shipyard fabrication, onshore services, mining operations and oil and gas construction projects in Indonesia. His experience includes positions at SIEMENS, Pandan Bahari Shipyard, Trimegah Bangun Persada, Uni Marine Pacific, and Britoil Offshore Indonesia where he was responsible for advising on and implementing HSE standards and programs.
Truss is a framework, typically consisting of rafters, posts, and struts, supporting a roof, bridge, or other structure.
a truss is a structure that "consists of two-force members only, where the members are organized so that the assemblage as a whole behaves as a single object"
Azure SQL Database is a relational database-as-a-service hosted in the Azure cloud that reduces costs by eliminating the need to manage virtual machines, operating systems, or database software. It provides automatic backups, high availability through geo-replication, and the ability to scale performance by changing service tiers. Azure Cosmos DB is a globally distributed, multi-model database that supports automatic indexing, multiple data models via different APIs, and configurable consistency levels with strong performance guarantees. Azure Redis Cache uses the open-source Redis data structure store with managed caching instances in Azure for improved application performance.
Azure provides several data related services for storing, processing, and analyzing data in the cloud at scale. Key services include Azure SQL Database for relational data, Azure DocumentDB for NoSQL data, Azure Data Warehouse for analytics, Azure Data Lake Store for big data storage, and Azure Storage for binary data. These services provide scalability, high availability, and manageability. Azure SQL Database provides fully managed SQL databases with options for single databases, elastic pools, and geo-replication. Azure Data Warehouse enables petabyte-scale analytics with massively parallel processing.
Azure SQL Database is a cloud-based relational database service built on Microsoft SQL Server that provides predictable performance, scalability, high availability with no downtime, and near-zero administration. It offers instant scalability, database replication across regions for backup, and has Microsoft handle common management operations. Developers can access data using ADO.NET, Java, PHP, Node.js, Python, Ruby and JSON. It provides features like stored procedures, triggers, views, encryption, temporal tables, performance monitoring, row-level security, and dynamic data masking.
Azure SQL Database is a cloud-based relational database service built on the Microsoft SQL Server engine. It provides predictable performance and scalability with minimal downtime and administration. Key features include elastic pools for cost-effective scaling, built-in backups and disaster recovery, security features like encryption and auditing, and tools for management and monitoring performance. The document provides an overview of Azure SQL Database capabilities and service tiers for databases and elastic pools.
Azure Identity (AD,ADFS 2.0,AAD,ADB2C,OAuth,OpenID,PingID,AD Custom Policies) ,
Azure PaaS (Azure Functions, Serverless computing, Azure Comsos DB, Webhooks, API Apps, Logic Apps, Kudu, Azure Websites), Azure Functions, Lamda Function, Event Functions, Serverless architecture, Implementing azure functions on GIT HUB comment feature, Why Azure Functions, Azure Virtual Machines, Azure Cloud Services, Azure Web Apps & WebJobs, Service Fabric, Consumption Plans, Billing Model, Benefits of Azure Functions, What is serverless, Implementing bigger solutions into smaller azure functions, Microservices, Use cases, Function App, Implementation storing unstructured data using Azure functions into Cosmos DB, Cosmos DB, Custom Azure functions, Azure Cosmos DB, IOTS, Document DB, Doc DB, How to setup a Jenkins build server and automatically trigger code from Visual studio online,Azure App Service, App service Environment, Azure Stack, Managing Azure App services, Azure Powershell, Azure CLI, REST APIS, Azure Portal, Templates, Kudu Console access, Run GIT Commands on Kudu Console, Locking Azure Resources, Configuring Custom Domains, Adding Extensions to Azure Web App/Websites, App service Deployment options, Data Services in Azure , Azure SQL, Azure SQL server, Azure SQL database vs SQL server in a Azure VM, SQL Tiers, DTU, Data Transactional Unit, Planning & provisioning azure SQL databases,Migrating SQL Databases, Azure SQL Server, SQL server transactional replication, Deploy database to Microsoft Azure Database Wizard, DAC package, DAC, SQL compatibility issues, Migrating SQL with downtime, DMA, Data Migration Assistant, Database Snapshot, Migrating SQL without downtime, DTU, Data Transactional Unit, Recommendations for best performance during SQL Import Process, Transactional Replication, T-SQL, Task to implement what ever you learnt till now,
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
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.
Ralph Kemperdick – IT-Tage 2015 – Microsoft Azure als DatenplattformInformatik Aktuell
In dieser Session möchten wir eine Orientierung geben, welche Daten-Services auf Azure die geeignete Plattform für eine App bzw. eine Anwendung sein können. Die Session konzentriert sich auf die Platform as a Service (PaaS) mit einem SQL Interface. Es wird Azure SQL Server, Azure SQL DW, DocumentDB, Stream Analytics, Spark/Scala/Hive und Data Lake Analytics betrachtet und Unterschiede herausgearbeitet. Live Demos begleiten die einzelnen Themen in der Session. Ferner werden Argumente für und gegen Cloud basierte Services diskutiert.
Azure SQL Database is a managed cloud database service that makes building and maintaining applications easier. It provides continuous learning of app patterns to optimize performance, reliability, and data protection. The service takes care of scalability, backup, and high availability. It provides recommendations to optimize database performance and fix issues. Azure SQL Database offers pricing tiers for different performance levels and capabilities for security, monitoring, and compliance. It can be used for a variety of workloads including web, mobile, and multi-tenant apps.
Autonomous Database is Oracle's fully managed cloud database service. It provides automated operations including provisioning, tuning, patching and backup. Autonomous Database can be deployed in serverless or dedicated modes. Serverless provides elastic scaling on shared infrastructure, while dedicated runs on a customer's dedicated Exadata cloud service. Both support Autonomous Data Warehouse and Autonomous Transaction Processing databases. Autonomous Database handles all database administration tasks, allowing users to focus on running SQL queries without managing infrastructure.
This document summarizes key components of Microsoft Azure's data platform, including SQL Database, NoSQL options like Azure Tables, Blob Storage, and Azure Files. It provides an overview of each service, how they work, common use cases, and demos of creating resources and accessing data. The document is aimed at helping readers understand Azure's database and data storage options for building cloud applications.
This webinar by Volodymyr Trishyn (Senior Software Engineer, Consultant, GlobalLogic) was delivered at On Air webinar #15 on July 31, 2020.
Webinar agenda:
- SQL Database
- Azure SQL Data Warehouse
- Azure SQL Elastic Database Pool
- Geo-replication
- Distributed Transactions
- Transaction Isolation Level
- Table Partitioning
- Materialized View Pattern
More details and presentation: https://www.globallogic.com/ua/about/events/webinar-azure-sql/
Cloud architectural patterns and Microsoft Azure toolsPushkar Chivate
This document discusses various cloud architectural patterns and Microsoft Azure services. It provides an overview of data management, resiliency, and messaging patterns. It then demonstrates the Materialized View pattern and how it can improve query performance. Finally, it shows examples of Azure Tables, DocumentDB, and Azure Service Bus queues for messaging between loosely coupled applications.
What is in a modern BI architecture? In this presentation, we explore PaaS, Azure Active Directory and Storage options including SQL Database and SQL Datawarehouse.
Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It provides the freedom to query data at scale using either serverless or dedicated options. Azure HDInsight allows the use of open source frameworks like Hadoop, Spark, Hive, and Kafka for processing large volumes of data. Azure Databricks offers environments for SQL, data science/engineering, and machine learning. The Azure IoT Hub enables scalable IoT solutions by allowing bidirectional communication between IoT applications and connected devices.
A Tour of Azure SQL Databases (NOVA SQL UG 2020)Timothy McAliley
This document provides information about upcoming webinars on Azure SQL and AI/ML hosted by various user groups. It lists the experience of the person running the user groups and provides an agenda for upcoming webinars in May and June 2020 that will cover various Azure database and analytics services. It also includes references and links for further learning about Azure SQL Database, Azure SQL Managed Instance, high availability and disaster recovery options.
Modern ETL: Azure Data Factory, Data Lake, and SQL DatabaseEric Bragas
This document discusses modern Extract, Transform, Load (ETL) tools in Azure, including Azure Data Factory, Azure Data Lake, and Azure SQL Database. It provides an overview of each tool and how they can be used together in a data warehouse architecture with Azure Data Lake acting as the data hub and Azure SQL Database being used for analytics and reporting through the creation of data marts. It also includes two demonstrations, one on Azure Data Factory and another showing Azure Data Lake Store and Analytics.
J1 T1 3 - Azure Data Lake store & analytics 101 - Kenneth M. NielsenMS Cloud Summit
This document provides an overview and demonstration of Azure Data Lake Store and Azure Data Lake Analytics. The presenter discusses how Azure Data Lake can store and analyze large amounts of data in its native format. Key capabilities of Azure Data Lake Store like unlimited storage, security features, and support for any data type are highlighted. Azure Data Lake Analytics is presented as an elastic analytics service built on Apache YARN that can process large amounts of data. The U-SQL language for big data analytics is demonstrated, along with using Visual Studio and PowerShell for interacting with Azure Data Lake. The presentation concludes with a question and answer section.
This document provides an introduction to Microsoft Azure, including key concepts like cloud computing, virtualization, cloud service models, and Azure components. It covers Azure storage services like blobs, tables, and SQL Azure. It also discusses the developer experience on Azure, using familiar tools like Visual Studio. Traffic Manager is introduced as a way to control traffic distribution for high availability. The document demonstrates deploying a web app to Azure and provides an overview of StudioRG infrastructure with an example.
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
"Rebranding for Growth", Anna VelykoivanenkoFwdays
Since there is no single formula for rebranding, this presentation will explore best practices for aligning business strategy and communication to achieve business goals.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Hands On: Create a Lightning Aura Component with force:RecordDataLynda Kane
Slide Deck from the 3/26/2020 virtual meeting of the Cleveland Developer Group presentation on creating a Lightning Aura Component using force:RecordData.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
Rock, Paper, Scissors: An Apex Map Learning JourneyLynda Kane
Slide Deck from Presentations to WITDevs (April 2021) and Cleveland Developer Group (6/28/2023) on using Rock, Paper, Scissors to learn the Map construct in Salesforce Apex development.
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
5. Microsoft Azure SQL Database
• SQL Database is a relational database service in the cloud based on
the market-leading Microsoft SQL Server engine, with mission-critical
capabilities.
SQL Database delivers predictable performance, scalability, business
continuity, data protection, and near-zero administration to cloud
developers and solution architects.
You can focus on rapid app development and accelerating your time
to market, rather than managing virtual machines and infrastructure.
6. Microsoft Azure SQL Database
• Because it’s based on the SQL Server engine,
Azure SQL Database supports existing SQL Server tools, libraries and
APIs, which makes it easier for you to move and extend to the cloud.
( Using the same TDS interface as regular SQL database)
7. Microsoft Azure SQL Database
• Based on SQL server technology. (A subset of the product)
• Fully managed service (Microsoft manage it completely!)
• We Don't to think and worry about:
VMs
Resources (IO, CPU, Memory)
Installations, upgrades, patches
Services
Files placement
Transaction log
Availability solutions
8. Microsoft Azure SQL Database – Server
• The basic unit in Azure that holds DBs is a “server”
• A server is a logical entity used for logical grouping of DBs and security
bounding.
It looks and smell like a server but it's basically only a TDS proxy endpoint.
(For the application it looks like a server (instance))
• When creating it, you are required to provide a unique server name-
(xxxx.database.windows.net).
• The server Initially contains only a master database.
9. Microsoft Azure SQL Database –DB
Microsoft Azure SQL Database has the following properties:
• Name – of the DB
• Service Tier (Performance Level)
• Max Size
• Collation – for all the tables are columns.
• “Server” – in which group to place the DB
10. Microsoft Azure SQL Database – service tiers
Azure SQL Database provides multiple service tiers to handle different types of
workloads.
• Performance level – measured by “DTU” (a combination of CPU, IO and log usage)
• Max size
• Point-in-time restore retention
• Parallel connections
• Size – does not include the log file size
You can change service tiers at any time with zero downtime to your application.
(Scale up)
11. Microsoft Azure SQL Database – service tiers
Basic, Standard, and Premium service tiers all have an uptime SLA of 99.99%
• Basic: Best suited for a small size database, supporting typically one single active
operation at a given time.
(predictable performance hour over hour)
• Standard: The go-to option for most cloud applications, supporting multiple
concurrent queries.
(predictable performance minute over minute)
• Premium: Designed for high transactional volume, supporting a large number of
concurrent users and requiring the highest level of business continuity
capabilities.
(predictable performance second over second)
12. Microsoft Azure SQL Database – service tiers
• The Database Transaction Unit (DTU) is the unit of measure in SQL
Database that represents the relative power of databases based on a
real-world measure: the database transaction. We took a set of
operations that are typical for an online transaction processing (OLTP)
request, and then measured how many transactions could be
completed per second under fully loaded conditions (that’s the short
version, you can read the gory details in the Benchmark overview).
• A Basic database has 5 DTUs, which means it can complete 5
transactions per second, while a Premium P11 database has 1750
DTUs.
15. Microsoft Azure SQL Database – Connection
• The Microsoft Azure SQL Database service is only available with TCP
port 1433. (ensure your firewall allows outgoing TCP communication
on TCP port 1433)
• The protocol is TDS (Tabular Data Stream) protocol over TCP/IP.
• All connections are encrypted - SSL is required - if encryption is not
defined in the connection string, connection will fail.
• Use firewall rules to connect from outside Microsoft data center
16. Microsoft Azure SQL Database – Connection
• Connect using the server name : xxxx.database.windows.net
• Connection Security is managed by SQL Database Firewall, an IP
Address-based access control for SQL Database
Rules can be defined at the server level
No IP authorized by default
Option to disable/enable access from
applications hosted in Windows Azure:
18. Microsoft Azure SQL Database – Business continuity
• Let’s start from the bottom line:
• SLA is built- in 99.9%.
In opposed to traditional Always-On and failover cluster which
requires a lot of work and money, this solution is cheap and out-of-
the-box.
19. Microsoft Azure SQL Database – Business continuity
• Business continuity can be affected by one or more of the following
three major categories of issues:
Failure of individual servers, devices or network connectivity
(the disk that my data is on died)
Corruption, unwanted modification or deletion of data
(someone dropped a table or deleted a row)
Widespread loss of datacenter facilities
(Amsterdam is gone)
20. Microsoft Azure SQL Database – Business continuity
• Protection from Failure of Individual Servers and Devices - High
Availability through Infrastructure Redundancy:
Maintaining multiple (3) copies of all data in different physical nodes
located across fully independent physical sub-systems such as server
racks and network routers.
21. Microsoft Azure SQL Database – Business continuity
Point-in-time restore, Programmatic “oops recovery” of data deletion
or alteration:
• Auto backups, transactional logs every 5 min
• Backups in Azure Storage and geo-replicated
• Backups retention policy:
Basic, up to 7 days
Standard, up to 14 days
Premium, up to 35 days
23. Microsoft Azure SQL Database – Elastic tools
• Elastic Database features enables you to use the virtually unlimited
database resources of Azure SQL Database to create solutions for
transactional workloads, and especially Software as a Service (SaaS)
applications.
• Enabling this functionality is facilitated by features such as the elastic
database client library and split-merge tool, known together as
elastic database tools, which simplify building applications that rely
on sharding.
24. Microsoft Azure SQL Database – Elastic tools
• Elastic Database tools:
simplify development and management of sharded database
solutions. The tools are: the Elastic Database client library and the
Elastic Database split-merge tool.
25. Microsoft Azure SQL Database – Elastic tools
• Elastic Database pools (preview): A pool is a collection of databases to
which you can add or remove databases at any time. The databases in
the pool share a fixed amount of resources (DTUs).
• Elastic Database jobs (preview): Use jobs to manage large numbers of
Azure SQL databases. Easily perform administrative operations such
as schema changes, credentials management, reference data updates
and more
• Elastic Database query (preview): Enables you to run a Transact-SQL
query that spans multiple databases. This enables connection to
reporting tools such as Excel, PowerBI, Tableau, etc.
26. Microsoft Azure SQL database – sharding
“A database shard is a horizontal partition of data in a database.”
• Sharding is a technique to distribute large amounts of identically-
structured data across a number of independent databases. It is
especially popular with cloud developers who are creating Software
as a Service (SAAS) offerings for end customers or businesses. These
end customers are often referred to as “Tenants”.
• Sharding works best when every transaction in an application can be
restricted to a single value of a sharding key. That ensures that all
transactions will be local to a specific database.
28. Elastic Database client library
• The Elastic Database client library helps you easily develop sharded
applications using hundreds—or even thousands—of Azure SQL
databases hosted on Microsoft Azure
• Elastic Database client library is now available as open source
software on GitHub.
• Elastic database client library supports:
Shard management
Data-dependent routing
Multi-shard queries (MSQ)
29. Elastic Database library – Shard Map Management
• To manage a collection of shards, a special database called the "shard map
manager" is created.
It maintains information allowing an application to connect to the correct
database based upon the value of the sharding key.
• Shard map management is the ability for an application to manage various
metadata about its shards.
• Developers can use this functionality to register databases as shards,
describe mappings of individual sharding keys or key ranges to those
databases.
• Without the elastic database client library, you would need to spend a lot
of time writing the management code when implementing sharding.
30. Elastic Database library – Shard Map Management
• use the ShardMapManager class, found in the Elastic Database client
library to manage shard maps.
• Elastic Scale support the following .Net Framework types as sharding
keys:
integer
long
guid
byte[]
datetime
timespan
datetimeoffset
31. Elastic Database library – Shard Map Management
• Shard maps can be constructed using lists of individual sharding key
values, or they can be constructed using ranges of sharding key
values.
• The data managed by a ShardMapManager instance is kept in three
places:
Global Shard Map (GSM): You specify a database to serve as the repository
for all of its shard maps and mappings.
Local Shard Map (LSM): Every database that you specify to be a shard is
modified to contain shard map information.
Application cache
32. Elastic Database library – Data dependent routing
• Data dependent routing is the ability to use the data in a query to
route the request to an appropriate database. This is a fundamental
pattern when working with sharded databases.
• Each specific query or transaction in an application using data
dependent routing is restricted to accessing a single database per
request.
• the Shard Map Manager opens connections to the correct databases
when needed, based on the data in the shard map and the value of
the sharding key that is the target of the application’s request.
33. Elastic Database library – Data dependent routing
• The ShardMap.OpenConnectionForKey method returns an ADO.Net
connection ready for issuing commands to the appropriate database
based on the value of the key parameter.
• The OpenConnectionForKey method returns a new already-open
connection to the correct database. Connections utilized in this way
still take full advantage of ADO.Net connection pooling.
34. Elastic Database library – Multi-shard querying
• Multi-shard querying is used for tasks such as data
collection/reporting that require running a query that stretches
across several shards.
• The main entry point into multi-shard querying is the
MultiShardConnection class.
• myShardMap.GetShards() method retrieves all shards from the shard
map and provides an easy way to run a query across all relevant
databases.
• The collection of shards for a multi-shard query can be refined further
by performing a LINQ query over the collection returned from the call
to myShardMap.GetShards()
#3: Been around for more than 10 years…. Bla bla bla
#8: It gets features a lot before the boxed product receives them.
#9: It's basically routes your connection to the physical location of the primary replica.
#15: Create a DB,create in already existing server (Selaopenhouse),choose a low pricing tier,create a sample DB.Change the firewall setting of the already existing AdventureWorks DB (on Selaopenhouse server).connect using visual studio and using management studio.
#18: Create a DB,create in already existing server (Selaopenhouse),choose a low pricing tier,create a sample DB.Change the firewall setting of the already existing AdventureWorks DB (on Selaopenhouse server).connect using visual studio and using management studio.
#21: Azure SQL Database has a built-in high availability subsystem that protects your database from failures of individual servers and devices
mitigates outages due to failures of individual server components, such as hard drives, network interface adapters, or even entire servers. Data durability and fault tolerance is enhanced by
#27: multi-tenant sharding, the rows in the database tables are all designed to carry a key identifying the tenant ID or sharding key. Again, the application tier is responsible for routing a tenant’s request to the appropriate database, and this can be supported by the elastic database client library. In addition, row-level security can be used to filter which rows each tenant can access
#36: Create a pool through the portal, show shards management table (script) and concepts, and some client code example: <Explain the story behind the demo>
Insert data in a specific shard query,
Show the data in the DB,
Show a multi shard query to retrieve the customers.