Fast, distributed NoSQL and relational database at any scale. This contains many features including Partition and Indexes,
Data movement, Change Feed
Integration (Azure Functions and Search), Consistency Models, Replication and Multi-write, etc.,
In the PowerPoint presentation about Azure Synapse, we begin by introducing Azure Synapse as an integrated analytics service, emphasizing its role in unifying big data and data warehousing. Key features such as unlimited information processing, querying of both relational and non-relational data, and integration with AI and BI capabilities are highlighted. The presentation delves into the architecture of Azure Synapse, illustrating how it interconnects with Azure Data Lake, Power BI, and Azure Machine Learning. We explore its robust data integration capabilities, including Azure Synapse Pipelines for efficient ETL processes. The discussion then moves to its prowess in analytics and big data processing, supporting various languages like T-SQL, Python, and Scala. The integration of Azure Synapse with AI and machine learning is underscored, showcasing its application in predictive analytics. Security features form a crucial part of the talk, emphasizing data protection and compliance aspects. Real-world use cases demonstrate Azure Synapse's practical applications in business settings. A comparative analysis with other data platforms highlights Synapse's unique benefits. The presentation concludes with guidance on getting started with Azure Synapse, followed by a summary, inviting audience questions and providing contact information for further engagement.
Non è necessario tirare in ballo l’IoT per immaginare quanto possa essere utile per fare query sui dati mentre questi fluiscono verso il database, e non solamente dopo. Si apre un mondo di possibilità per quanto riguarda alerting & monitoring in tempo reale, che è chiaramente la parte più immediata, ma è anche possibile pensare a cose come real-time dasboarding e soluzioni per aggiustare prezzi ed offerte di prodotti in tempo reale. In questa sessione vedremo come è possibile utilizzare Azure Stream Analytics ed il suo linguaggio SQL-Like per analizzare i dati in streaming, e quindi iniziare a prendere confidenza con questo nuovo approccio ormai sempre pià in voga e sempre più richesto, sia nel mondo dell’IoT che non.
AWS re:Invent 2016: Workshop: Using the Database Migration Service (DMS) for ...Amazon Web Services
It can help you do much more. You can use DMS to consolidate multiple databases into a single database or split a single database into multiple databases. You can also use DMS for data distribution to multiple systems. For both of these use cases your source database can be outside of AWS (on premises) or in AWS (EC2 or RDS). DMS can also be used for near real-time replication of data. Replication can be done to one or more targets within AWS, in the same region or across regions. You can also replicate data from databases within AWS to databases outside of AWS. In this session we will discuss all these usage patterns and help you try them out yourselves.
Prerequisites:
You should have good database knowledge and at least some experience with Amazon RDS or Amazon Aurora.
Participants should have an AWS account established and available for use during the workshop.
Please bring your own laptop.
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Fwdays
We will start from understanding how Real-Time Analytics can be implemented on Enterprise Level Infrastructure and will go to details and discover how different cases of business intelligence be used in real-time on streaming data. We will cover different Stream Data Processing Architectures and discus their benefits and disadvantages. I'll show with live demos how to build Fast Data Platform in Azure Cloud using open source projects: Apache Kafka, Apache Cassandra, Mesos. Also I'll show examples and code from real projects.
Spark is fast becoming a critical part of Customer Solutions on Azure. Databricks on Microsoft Azure provides a first-class experience for building and running Spark applications. The Microsoft Azure CAT team engaged with many early adopter customers helping them build their solutions on Azure Databricks.
In this session, we begin by reviewing typical workload patterns, integration with other Azure services like Azure Storage, Azure Data Lake, IoT / Event Hubs, SQL DW, PowerBI etc. Most importantly, we will share real-world tips and learnings that you can take and apply in your Data Engineering / Data Science workloads
In the PowerPoint presentation about Azure Synapse, we begin by introducing Azure Synapse as an integrated analytics service, emphasizing its role in unifying big data and data warehousing. Key features such as unlimited information processing, querying of both relational and non-relational data, and integration with AI and BI capabilities are highlighted. The presentation delves into the architecture of Azure Synapse, illustrating how it interconnects with Azure Data Lake, Power BI, and Azure Machine Learning. We explore its robust data integration capabilities, including Azure Synapse Pipelines for efficient ETL processes. The discussion then moves to its prowess in analytics and big data processing, supporting various languages like T-SQL, Python, and Scala. The integration of Azure Synapse with AI and machine learning is underscored, showcasing its application in predictive analytics. Security features form a crucial part of the talk, emphasizing data protection and compliance aspects. Real-world use cases demonstrate Azure Synapse's practical applications in business settings. A comparative analysis with other data platforms highlights Synapse's unique benefits. The presentation concludes with guidance on getting started with Azure Synapse, followed by a summary, inviting audience questions and providing contact information for further engagement.
Non è necessario tirare in ballo l’IoT per immaginare quanto possa essere utile per fare query sui dati mentre questi fluiscono verso il database, e non solamente dopo. Si apre un mondo di possibilità per quanto riguarda alerting & monitoring in tempo reale, che è chiaramente la parte più immediata, ma è anche possibile pensare a cose come real-time dasboarding e soluzioni per aggiustare prezzi ed offerte di prodotti in tempo reale. In questa sessione vedremo come è possibile utilizzare Azure Stream Analytics ed il suo linguaggio SQL-Like per analizzare i dati in streaming, e quindi iniziare a prendere confidenza con questo nuovo approccio ormai sempre pià in voga e sempre più richesto, sia nel mondo dell’IoT che non.
AWS re:Invent 2016: Workshop: Using the Database Migration Service (DMS) for ...Amazon Web Services
It can help you do much more. You can use DMS to consolidate multiple databases into a single database or split a single database into multiple databases. You can also use DMS for data distribution to multiple systems. For both of these use cases your source database can be outside of AWS (on premises) or in AWS (EC2 or RDS). DMS can also be used for near real-time replication of data. Replication can be done to one or more targets within AWS, in the same region or across regions. You can also replicate data from databases within AWS to databases outside of AWS. In this session we will discuss all these usage patterns and help you try them out yourselves.
Prerequisites:
You should have good database knowledge and at least some experience with Amazon RDS or Amazon Aurora.
Participants should have an AWS account established and available for use during the workshop.
Please bring your own laptop.
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Fwdays
We will start from understanding how Real-Time Analytics can be implemented on Enterprise Level Infrastructure and will go to details and discover how different cases of business intelligence be used in real-time on streaming data. We will cover different Stream Data Processing Architectures and discus their benefits and disadvantages. I'll show with live demos how to build Fast Data Platform in Azure Cloud using open source projects: Apache Kafka, Apache Cassandra, Mesos. Also I'll show examples and code from real projects.
Spark is fast becoming a critical part of Customer Solutions on Azure. Databricks on Microsoft Azure provides a first-class experience for building and running Spark applications. The Microsoft Azure CAT team engaged with many early adopter customers helping them build their solutions on Azure Databricks.
In this session, we begin by reviewing typical workload patterns, integration with other Azure services like Azure Storage, Azure Data Lake, IoT / Event Hubs, SQL DW, PowerBI etc. Most importantly, we will share real-world tips and learnings that you can take and apply in your Data Engineering / Data Science workloads
This is the slide deck for the DFW Azure User Group meetup of 18 July 2017, presented by Doug Vanderweide and discussing Azure's services that support a microservices architecture.
Migrating on premises workload to azure sql databasePARIKSHIT SAVJANI
Azure SQL Database is a fully managed cloud database service with built-in intelligence, elastic scale, performance, reliability, and data protection that enables enterprises and ISVs to reduce their total cost of ownership and operational cost and overheads. In this session, I will share real-world experience of successfully migrated existing SaaS application and on-premises workload for some our tier 1 customers and ISV partners to Azure SQL Database service. The session walks through planning, assessment, migration tools and best practices from the proven experiences and practices of migrating real world applications to Azure SQL Database service.
Simplify Your Database Migration to AWS | AWS Public Sector Summit 2016Amazon Web Services
Migrating a database from one platform to another has been a pain point for many organizations for a long time. Often times, it involves weeks of careful planning and a migration strategy to minimize impact to the business. Many organizations are locked into a database platform even when there are better options available because they don’t want to take up the migration challenge. AWS Data Migration Service helps with live migration of databases across homogenous or heterogeneous database platforms. The service supports homogenous migrations such as Oracle to Oracle, and also heterogeneous migrations between different database platforms, such as Oracle to Amazon Aurora or Microsoft SQL Server to MySQL. The AWS Schema Conversion Tool is a desktop application that makes heterogeneous database migrations easy by automatically converting the source database schema to a format compatible with the target database. The tool helps with conversion of a database schema from an Oracle or Microsoft SQL Server database to an Amazon RDS MySQL DB instance or an Amazon Aurora DB cluster. Join us in this session to explore how these capabilities can simplify your database migration challenge.
AWS Česko-Slovenský Webinár 03: Vývoj v AWSVladimir Simek
Služba Amazon Web Services poskytuje vysoce spolehlivou, škálovatelnou a nízkorozpočtovou cloudovou platformu, kterou používají stovky tisíc firem v 190 zemích po celém světě. Startupy, malé a střední podniky, velké enterprise firmy a zákazníci ve veřejném sektoru mají přístup ke stavebním kamenům, které slouží na rychlý vývoj aplikací jako reakce na měnící se obchodní požadavky. Bez ohledu na to, zda chcete vytvářet webové nebo mobilní aplikace, prípadně postavené na klasických serverech či kontejnerech, AWS davá vývojářům do rukou mnoho nástrojů, které jim pomáhají vytvářet a nasazovat aplikace jednoduše, rychle a při nízkých nákladech.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
SQL, NoSQL, Distributed SQL: Choose your DataStore carefullyMd Kamaruzzaman
In modern Software Development and Software Architecture, selecting the right DataStore is one of the most challenging and important task. In this presentation, I have summarized the major DataStores and the decision criteria to select the right DataStore according to the use case.
Migrate from SQL Server or Oracle into Amazon Aurora using AWS Database Migra...Amazon Web Services
As organizations look to improve application performance and decrease costs, they are increasingly looking to migrate from commercial database engines into open source. Amazon Aurora is a MySQL-compatible relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this webinar, we will cover how to use Database Migration Service (DMS) to go about the migration, and how to use the schema conversion tool to convert schemas into Amazon Aurora. We’ll then follow with a quick demo of the entire process, and close with tips and best practices.
Learning Objectives:
Understand how AWS Database migration can help you migrate from a commercial database into Amazon Aurora to improve application performance and decrease database costs.
AWS January 2016 Webinar Series - Getting Started with Big Data on AWSAmazon Web Services
With hundreds of new and sometimes disparate tools, it’s hard to keep pace. Amazon Web Services provides a broad and fully integrated portfolio of cloud computing services to help you build, secure and deploy your big data applications.
Attend this webinar to get an overview of the different big data options available in the AWS Cloud – including popular big data frameworks such as Hadoop, Spark, NoSQL databases, and more. Learn about ideal use cases, cases to avoid, performance, interfaces, and more. Finally, learn how you can build valuable applications with a real-life example.
Learning Objectives:
Learn about big data tools available at AWS
Understand ideal use cases
Learn some of the key considerations such as performance, scalability, elasticity and availability, when selecting big data tools
Who Should Attend:
Data Architects, Data Scientists, Developers
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
Best practices on Building a Big Data Analytics Solution (SQLBits 2018 Traini...Michael Rys
From theory to implementation - follow the steps of implementing an end-to-end analytics solution illustrated with some best practices and examples in Azure Data Lake.
During this full training day we will share the architecture patterns, tooling, learnings and tips and tricks for building such services on Azure Data Lake. We take you through some anti-patterns and best practices on data loading and organization, give you hands-on time and the ability to develop some of your own U-SQL scripts to process your data and discuss the pros and cons of files versus tables.
This were the slides presented at the SQLBits 2018 Training Day on Feb 21, 2018.
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
Explore the cutting-edge of AI and search technology with Udaiappa Ramachandran (Udai), CTO/CSO of Akumina Inc. and Microsoft Azure MVP, in his presentation 'RAG Patterns and Vector Search in Generative AI'. This comprehensive overview covers the essentials of Keyword and Vector Search, highlighting their strengths and limitations. Udai brilliantly introduces Hybrid Search, combining the best of both worlds for enhanced accuracy and relevance. Real-world applications in companies like Amazon, Google, and Netflix illustrate the practical implications of these technologies. The presentation also delves into the mechanics of cosine similarity and explores various vector databases, providing a well-rounded understanding of current AI search technologies. Ideal for professionals and enthusiasts in the AI and search technology fields, this presentation offers a glimpse into the future of intelligent search solutions.
In "Level Up Your Security Using Intune," Udaiappa Ramachandran, an expert in cloud technologies, presents a detailed guide on using Microsoft Intune for enhancing mobile application and device security. The presentation covers two main integration strategies: the Intune SDK, which provides fine-grained control, customization, and long-term maintainability, and the Intune App Wrapper, suitable for legacy apps and rapid prototyping with some feature limitations. Udaiappa's talk, aimed at modern developers, emphasizes the importance of robust mobile security and showcases Intune's capabilities in managing both corporate-owned devices and BYOD scenarios, underlining its critical role in contemporary digital security management.
This is the slide deck for the DFW Azure User Group meetup of 18 July 2017, presented by Doug Vanderweide and discussing Azure's services that support a microservices architecture.
Migrating on premises workload to azure sql databasePARIKSHIT SAVJANI
Azure SQL Database is a fully managed cloud database service with built-in intelligence, elastic scale, performance, reliability, and data protection that enables enterprises and ISVs to reduce their total cost of ownership and operational cost and overheads. In this session, I will share real-world experience of successfully migrated existing SaaS application and on-premises workload for some our tier 1 customers and ISV partners to Azure SQL Database service. The session walks through planning, assessment, migration tools and best practices from the proven experiences and practices of migrating real world applications to Azure SQL Database service.
Simplify Your Database Migration to AWS | AWS Public Sector Summit 2016Amazon Web Services
Migrating a database from one platform to another has been a pain point for many organizations for a long time. Often times, it involves weeks of careful planning and a migration strategy to minimize impact to the business. Many organizations are locked into a database platform even when there are better options available because they don’t want to take up the migration challenge. AWS Data Migration Service helps with live migration of databases across homogenous or heterogeneous database platforms. The service supports homogenous migrations such as Oracle to Oracle, and also heterogeneous migrations between different database platforms, such as Oracle to Amazon Aurora or Microsoft SQL Server to MySQL. The AWS Schema Conversion Tool is a desktop application that makes heterogeneous database migrations easy by automatically converting the source database schema to a format compatible with the target database. The tool helps with conversion of a database schema from an Oracle or Microsoft SQL Server database to an Amazon RDS MySQL DB instance or an Amazon Aurora DB cluster. Join us in this session to explore how these capabilities can simplify your database migration challenge.
AWS Česko-Slovenský Webinár 03: Vývoj v AWSVladimir Simek
Služba Amazon Web Services poskytuje vysoce spolehlivou, škálovatelnou a nízkorozpočtovou cloudovou platformu, kterou používají stovky tisíc firem v 190 zemích po celém světě. Startupy, malé a střední podniky, velké enterprise firmy a zákazníci ve veřejném sektoru mají přístup ke stavebním kamenům, které slouží na rychlý vývoj aplikací jako reakce na měnící se obchodní požadavky. Bez ohledu na to, zda chcete vytvářet webové nebo mobilní aplikace, prípadně postavené na klasických serverech či kontejnerech, AWS davá vývojářům do rukou mnoho nástrojů, které jim pomáhají vytvářet a nasazovat aplikace jednoduše, rychle a při nízkých nákladech.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
SQL, NoSQL, Distributed SQL: Choose your DataStore carefullyMd Kamaruzzaman
In modern Software Development and Software Architecture, selecting the right DataStore is one of the most challenging and important task. In this presentation, I have summarized the major DataStores and the decision criteria to select the right DataStore according to the use case.
Migrate from SQL Server or Oracle into Amazon Aurora using AWS Database Migra...Amazon Web Services
As organizations look to improve application performance and decrease costs, they are increasingly looking to migrate from commercial database engines into open source. Amazon Aurora is a MySQL-compatible relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this webinar, we will cover how to use Database Migration Service (DMS) to go about the migration, and how to use the schema conversion tool to convert schemas into Amazon Aurora. We’ll then follow with a quick demo of the entire process, and close with tips and best practices.
Learning Objectives:
Understand how AWS Database migration can help you migrate from a commercial database into Amazon Aurora to improve application performance and decrease database costs.
AWS January 2016 Webinar Series - Getting Started with Big Data on AWSAmazon Web Services
With hundreds of new and sometimes disparate tools, it’s hard to keep pace. Amazon Web Services provides a broad and fully integrated portfolio of cloud computing services to help you build, secure and deploy your big data applications.
Attend this webinar to get an overview of the different big data options available in the AWS Cloud – including popular big data frameworks such as Hadoop, Spark, NoSQL databases, and more. Learn about ideal use cases, cases to avoid, performance, interfaces, and more. Finally, learn how you can build valuable applications with a real-life example.
Learning Objectives:
Learn about big data tools available at AWS
Understand ideal use cases
Learn some of the key considerations such as performance, scalability, elasticity and availability, when selecting big data tools
Who Should Attend:
Data Architects, Data Scientists, Developers
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
Best practices on Building a Big Data Analytics Solution (SQLBits 2018 Traini...Michael Rys
From theory to implementation - follow the steps of implementing an end-to-end analytics solution illustrated with some best practices and examples in Azure Data Lake.
During this full training day we will share the architecture patterns, tooling, learnings and tips and tricks for building such services on Azure Data Lake. We take you through some anti-patterns and best practices on data loading and organization, give you hands-on time and the ability to develop some of your own U-SQL scripts to process your data and discuss the pros and cons of files versus tables.
This were the slides presented at the SQLBits 2018 Training Day on Feb 21, 2018.
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
Explore the cutting-edge of AI and search technology with Udaiappa Ramachandran (Udai), CTO/CSO of Akumina Inc. and Microsoft Azure MVP, in his presentation 'RAG Patterns and Vector Search in Generative AI'. This comprehensive overview covers the essentials of Keyword and Vector Search, highlighting their strengths and limitations. Udai brilliantly introduces Hybrid Search, combining the best of both worlds for enhanced accuracy and relevance. Real-world applications in companies like Amazon, Google, and Netflix illustrate the practical implications of these technologies. The presentation also delves into the mechanics of cosine similarity and explores various vector databases, providing a well-rounded understanding of current AI search technologies. Ideal for professionals and enthusiasts in the AI and search technology fields, this presentation offers a glimpse into the future of intelligent search solutions.
In "Level Up Your Security Using Intune," Udaiappa Ramachandran, an expert in cloud technologies, presents a detailed guide on using Microsoft Intune for enhancing mobile application and device security. The presentation covers two main integration strategies: the Intune SDK, which provides fine-grained control, customization, and long-term maintainability, and the Intune App Wrapper, suitable for legacy apps and rapid prototyping with some feature limitations. Udaiappa's talk, aimed at modern developers, emphasizes the importance of robust mobile security and showcases Intune's capabilities in managing both corporate-owned devices and BYOD scenarios, underlining its critical role in contemporary digital security management.
Semantic Kernel, an open-source SDK, streamlines the integration and orchestration of AI models, supporting a diverse range of languages like C#, Python, and Java. It offers a suite of tools for AI application development, including specialized plugins for extending functionalities and planners for automating complex workflows and improving efficiency. A key feature of Semantic Kernel is its focus on memory and context management, enhancing AI agent performance and understanding. The copilot feature stands out for its real-time user interaction capabilities and its seamless integration with existing systems. Aimed at facilitating the development of sophisticated AI-driven applications, Semantic Kernel provides comprehensive support for task automation, model integration, and responsible AI practices, backed by extensive documentation and community support on Microsoft's platforms and GitHub repositories.
The presentation "Semantic Kernel" covers the Semantic Kernel, an open-source Software Development Kit (SDK) for AI model integration and agent development. It discusses key concepts like plugins, planners, personas, and co-pilots in AI applications, emphasizing their roles in task automation and AI orchestration. The presentation highlights features such as prompt engineering, AI memory management, and embedding storage for enhanced AI performance. It also outlines steps for building AI agents using Semantic Kernel, integrating AI models, and managing memory and context. Additionally, the importance of real-time assistance and user feedback in enhancing AI interactions is discussed, along with supported languages for the Semantic Kernel SDK.
.NET 8 is poised to deliver significant advancements with features such as Primary Constructors for cleaner code, enhanced Garbage Collection for better memory management, and optimized JSON Serialization for efficient data handling. Performance is further bolstered by Fast Search, Dynamic Profile Guided Optimization (PGO), and Native AOT for faster runtime and startup. Time Abstraction offers refined time operations, while improved Cryptography and Compression with ZipFile support enhance security and data management. Immutable data structures are introduced with FrozenSet, and RegEx Code Generation promises more efficient pattern matching. Additionally, Redis Output Caching could enhance distributed caching mechanisms, Background Worker enhancements may improve asynchronous task execution, and Semantic Kernel suggests more intelligent code analysis capabilities. Collectively, these features aim to streamline development workflows and boost application performance in the .NET 8 framework.
Discover the power of Vector Search using OpenAI in Azure Cognitive Search through a comprehensive .NET application tutorial. This presentation will delve into the intricacies of integrating Azure OpenAI with your .NET applications, focusing specifically on the creation and utilization of vector embeddings. Learn how to effectively harness the capabilities of Azure OpenAI for generating precise vector embeddings, which are crucial for enhancing search functionalities in your applications. We will explore the concept of Hybrid search, demonstrating how it combines traditional keyword search with the advanced vector search to provide more relevant and context-aware results. This session is designed to equip developers with the knowledge and skills needed to implement state-of-the-art search capabilities in their .NET applications, leveraging the cutting-edge AI and machine learning technologies provided by Azure OpenAI.
Key less access to Azure Services using AD Authentication using Managed Identity, User Managed Identity or Service Principal. Some samples include Cosmos DB, Azure Storage, Application Insight, Key Vault, etc.,
Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3, GPT-4, DALL-E, Codex, and Embeddings model series. These models can be easily adapted to any specific task, including but not limited to content generation, summarization, semantic search, translation, transformation, and code generation. Microsoft offers the accessibility of the service through REST APIs, Python or C# SDK, or the Azure OpenAI Studio.
ChatGPT (Chat Generative pre-defined transformer) is OpenAI's application that performs human like interactions. GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. Deck contains more details about ChatGPT, AI, AGI, CoPilot, OpenAI API, and use case scenarios.
.NET 7 is the latest version of .NET that was released in Nov 2022. .NET 7 ecosystem offers simplifications on development, high performance, and ultimate productivity.
Azure DevOps provides developer services for allowing teams to plan work, collaborate on code development, and build and deploy applications. Azure DevOps supports a collaborative culture and set of processes that bring together developers, project managers, and contributors to develop software. It allows organizations to create and improve products at a faster pace than they can with traditional software development approaches.
Azure Billing features are used to review your invoiced costs and manage access to billing information. In larger organizations, procurement and finance teams usually conduct billing tasks.
Billing is the process of invoicing customers for goods or services and managing the commercial relationship.
Cost Management shows the organizational cost and usage patterns with advanced analytics. Azure Portal let you manage both Billings and cost management for all your accounts.
.NET 6 is the latest version of .NET that was released in Nov 2021. .NET 6 ecosystem offers simplifications on development, high performance, and ultimate productivity.
Azure Automation delivers cloud-based automation, operating system updates, and configuration service that supports consistent management across your Azure and non-Azure environments. It includes process automation, configuration management, update management, shared capabilities, and heterogeneous features.
Azure Static Web Apps allows you to develop modern full-stack web apps quickly and easily with a static front-end and dynamic back end powered by Serverless APIs with custom routing, security including authentication/authrization, custom domains, private endpoint, etc. Azure Static Web Apps offers cost-effective pricing from hobby to production apps.
Azure Private Link provides private connectivity from a virtual network to Azure platform as a service (PaaS), customer-owned, or Microsoft partner services.
Azure Security Center provides security posture management and threat protection for your hybrid cloud workloads. Cloud Security Posture Management includes Policies, initiatives, recommendations, secure scores, and security controls. Cloud Workload Protection protects threats against servers, cloud-native workloads, databases, and storage security alerts and incidents.
Azure SignalR Service simplifies the process of adding real-time web functionality to applications over HTTP. Eliminates the need for polling and provides high availability, resiliency, and disaster recovery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
2. About me
• Udaiappa Ramachandran ( Udai )
• CTO-Akumina, Inc.
• Microsoft Azure MVP
• Cloud Expert
• Microsoft Azure, Amazon Web Services, and Google
• New Hampshire Cloud User Group (http://www.meetup.com/nashuaug )
• https://udai.io
3. Agenda
• Intro to SQL API
• SQL API SDK
• Partition and Indexes
• Data movement
• Change Feed
• Integration (Azure Functions and Search)
• Consistency Models
• Replication and Multi-write
• Cosmos DB Emulator
• Demo…Demo…Demo…
4. Introduction
• No SQL Feature(s)
• High Volume of data
• Non relational
• Data from many different sources and forms
• Dynamic data schema to store different types of data
• High velocity and real time data
• Designed for scale-out
• Cosmos DB is a Platform as a service for the following No SQL Databases
• SQL API (Document DB) – JSON Document (Sql Queries)
• MongoDB – BSON Document (MondoDB queries)
• Gremlin – Graph (vertices and Edges)
• Cassandra – Columnar (Schema)
• Table API – Key Value (Azure Table Storage)
5. Cosmos DB SQL API
• Fast NoSQL database
• Globally distributed
• Guaranteed speed at any scale
• Single digit milliseconds response time
• Ready for mission-critical applications (99.999)
• Fully managed and cost-effective serverless database
6. Database, Containers and Items
• Indexing
• Data Types
• Time to live
• Transactions and
optimistic
• Concurrency
control
7. Throughput
• Measured as Request Units (RUs)
• Assigned at Database or Container level
• Standard
• AutoScale
• Serverless
13. .NET SDK
• Client-Side (SDK)
• Working with Databases, Containers, Documents
• Bulk insert
• Transaction support
• Stateful and Stateless paging
• Server-Side
• Stored Procedure
• User-defined Functions
• Triggers
• Bulk insert using Stored Procedure
14. Multi-Region Write
• Sliding scale of well-defined consistency models (strong consistency is not
supported)
• Low latency write operations across the globe
• High availability with financially backed SLA
• Multi-region support in the SDK
• Conflict resolution policy (built-in and custom)
• insert, replace, delete
15. Replication & Failover
• Distribute data across global data centers
• Automatic failover and manual failover
• Configure region in SDK
16. Cosmos DB Emulator
• Run cosmos DB locally
• Runs on Windows, Linux or Container
17. Response Status Code & Headers
• HTTP Status Codes for Azure Cosmos DB | Microsoft Docs
• Common response headers - Azure Cosmos DB REST API | Microsoft Docs
18. Reference
• Introduction to Azure Cosmos DB | Microsoft Docs
• Complete Training Video: https://docs.microsoft.com/en-us/events/learn-
events/learnlive-azure-cosmos-db-certification-study-hall/?WT.mc_id=AZ-
MVP-5004665
• Azure Cosmos DB Capacity Calculator
• Demo Samples: https://github.com/nhcloud/techtalk
IoT device telemetetry
personalization and recommendations
Global, mission-critical
multi-region write
replication and manage failovers
distribute data across global data centers
Automatic failover and manual failover
Configure region in SDK
multi-write
sliding scale of weel-defined consistency models (strong consistency is not supported)
low latency write operations acroos the globe
High availability with finacially backed SLA
multi-region support in the SDK
conflict resolution policy (built-in and custom)
--insert, replace, delete
throughput
standard
autoscale
serverless
SDK
.net(C#)
java, python, JavaScript (Node.js)
integrate the Microsoft.Azure.Cosmos SDK library from NuGet (.CosmosClient, .Database, .Container)
Connect to an Azure Cosmos DB SQL API account using the SDK and .NET
consistency
bounded staleness
consistentprefix
eventual
session
strong
data movement
kafka connector 1way
stream analytics 1way
spark connector /synapse link 2way
azure data factory--etl 2way
cosmicwork tool
log handler (requesthandler), queryrequestoptions-maxconcurren (||sm)
IoT device telemetetry
personalization and recommendations
Global, mission-critical
multi-region write
replication and manage failovers
distribute data across global data centers
Automatic failover and manual failover
Configure region in SDK
multi-write
sliding scale of weel-defined consistency models (strong consistency is not supported)
low latency write operations acroos the globe
High availability with finacially backed SLA
multi-region support in the SDK
conflict resolution policy (built-in and custom)
--insert, replace, delete
throughput
standard
autoscale
serverless
SDK
.net(C#)
java, python, JavaScript (Node.js)
integrate the Microsoft.Azure.Cosmos SDK library from NuGet (.CosmosClient, .Database, .Container)
Connect to an Azure Cosmos DB SQL API account using the SDK and .NET
consistency
bounded staleness
consistentprefix
eventual
session
strong
data movement
kafka connector 1way
stream analytics 1way
spark connector /synapse link 2way
azure data factory--etl 2way
cosmicwork tool
log handler (requesthandler), queryrequestoptions-maxconcurren (||sm)
IoT device telemetetry
personalization and recommendations
Global, mission-critical
multi-region write
replication and manage failovers
distribute data across global data centers
Automatic failover and manual failover
Configure region in SDK
multi-write
sliding scale of weel-defined consistency models (strong consistency is not supported)
low latency write operations acroos the globe
High availability with finacially backed SLA
multi-region support in the SDK
conflict resolution policy (built-in and custom)
--insert, replace, delete
throughput
standard
autoscale
serverless
SDK
.net(C#)
java, python, JavaScript (Node.js)
integrate the Microsoft.Azure.Cosmos SDK library from NuGet (.CosmosClient, .Database, .Container)
Connect to an Azure Cosmos DB SQL API account using the SDK and .NET
consistency
bounded staleness
consistentprefix
eventual
session
strong
data movement
kafka connector 1way
stream analytics 1way
spark connector /synapse link 2way
azure data factory--etl 2way
cosmicwork tool
log handler (requesthandler), queryrequestoptions-maxconcurren (||sm)
Kafka Connector 1way
Stream Analytics 1way
Spark Connector /synapse link 2way
Azure Data Factory--ETL 2way