Geek Sync | Deployment and Management of Complex Azure Environments

This document summarizes the evolution of cloud computing technologies from virtual machines to containers to serverless computing. It discusses how serverless computing uses cloud functions that are fully managed by the cloud provider, providing significant cost savings over virtual machines by only paying for resources used. While serverless computing reduces operational overhead, it is not suitable for all workloads and has some limitations around cold start times and vendor lock-in. The document promotes serverless computing as the next wave in cloud that can greatly reduce costs and complexity while improving scalability and availability.

The document discusses strategies for transitioning from monolithic architectures to microservice architectures. It outlines some of the challenges with maintaining large monolithic applications and reasons for modernizing, such as handling more data and needing faster changes. It then covers microservice design principles and best practices, including service decomposition, distributed systems strategies, and reactive design. Finally it introduces Lagom as a framework for building reactive microservices on the JVM and outlines its key components and development environment.

ESBs would look different if built today. Large monolithic applications would be decomposed into microservices with bounded contexts. Services would be independently deployable and designed for failure. An ESB centralized integration but microservices use decentralized approaches like service discovery. While challenging, microservices evolve legacy systems towards modular, scalable architectures. It's a learning process, and the industry is still evolving effective patterns.

Azure Key Vault, Azure Dev Ops and Techno Azure Data Factory ,how do these Azure Services work perfectly together!

Cloud Foundry is an open platform as a service (PaaS) that supports building, deploying, and scaling applications. It uses a loosely coupled, distributed architecture with no single point of failure. The core components include cloud controllers, stagers, routers, execution agents, and services that communicate asynchronously through messaging. This allows the components to be scaled independently and provides a self-healing system.

This document discusses security for Microsoft SQL Azure (now called Windows Azure SQL Database). It provides an overview of SQL Database and its security capabilities, best practices for securing SQL Database like using encryption and configuring firewall rules, and limitations compared to on-premises SQL Server. It also introduces GreenSQL as a software-based database proxy that can provide additional security functionality for SQL Database like preventing SQL injection, auditing, and data masking. GreenSQL aims to offer a more complete solution for security, compliance, and hybrid application support compared to the native capabilities in SQL Database.

Presentation for Dutch Microsoft TechDays 2015 with Marcel de Vries: During this session we will take a look at how to realize a Microservices architecture (MSA) using the latest Microsoft technologies available. We will discuss some fundamental theories behind MSA and show you how this can actually be realized with Microsoft technologies such as Azure Service Fabric. This session is a real must-see for any developer that wants to stay ahead of the curve in modern architectures.

Since HDInsight launched Spark clusters last year, HDInsight spark team’s mission has been making Spark easy-to-use and production-ready. In the process, we have explored many open source technologies such as Livy, Jupyter, Zeppelin. In this talk, we will demo top customer features, deep dive into HDInsight Spark architecture, and share learnings from building the perfect cluster. Speakers: Judy Nash and Lin Chan

Glynn Bird – Cloudant – Building applications for success. All too often, web applications are built to work in development but are not capable of scaling when success arrives. Whether the application is a log aggregator that can't deal with the throughput, a blog that can't handle traffic when it hits the heights of Google's rankings or a mobile game that goes viral, an application can become the victim of its own success. By building with Cloudant from the outset, and architecting the application to scale by design, we can build apps that scale as the traffic, data-volumes and users arrive. Using several real-life use cases, this talk will detail how Cloudant can solve an application's data storage, search and retrieval needs, scaling easily with success!

The document discusses how LinkedIn, the world's largest professional network, was built using Java technologies and agile practices. It describes LinkedIn's architecture evolution from 2003 to today, which now uses a service-oriented architecture with over 40 services built with Java. It also discusses LinkedIn's agile engineering process, use of continuous integration testing, and how the site's large network is cached in the cloud.

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.

This presentation (part of the year AMIS Oracle OpenWorld Review session) discusses the main themes for this year's conference and introduces the all encompassing cloud strategy. It highlights some major changes at Oracle Corporation. It lists the major announcements, the hot terminology and the product roadmaps.

Overview of new Windows Azure features since June 7, 2012. This covers Windows Azure Web Sites, Windows Azure Virtual Machines, and

In this session, we will discuss: * reactive architecture tenets * distributed “fast data” streams * application and analytics focused Data Lake Enterprise level concerns and the importance of holistic governance, operational management, and a Metadata Lake will be conceptually investigated. The next level of detail will be to explore what a prospective architecture looks like at scale with Terabytes of ingestion per day, how scale puts pressure on an architecture, and how to be successful without losing data in a mission critical system via resilient, self-healing, scalable technologies. DevOps and application architecture concerns will be first-class themes throughout. Reactive principles and technology will be the second act of this talk. Kafka. Akka. Spark. Various streaming technologies (Kafka Streams, Akka Streams, Spark Streaming) will be reviewed to identify what they are best suited for. The fast data pipeline discussion will center around Kafka, Akka, and Apache Flink (Lightbend Fast Data platform). We’ll also walk through an exciting addition to the Akka family, Alpakka, which is a Camel equivalent for Enterprise Integration Patterns. The final act will be to dive into the Data Lake, from both an analytics and application development perspective. Technologies used to explain concepts will include Amazon and Hadoop. A Data Lake may service multiple analytics consumers with various “views” (and access levels) of data. It may also be a participant of various applications, perhaps by acting as a centralized source for reference data or common middleware (in turn feeding the analytics aspect). The concept of the Metadata Lake to apply structure, meaning and purpose will be an over-arching success factor for a Data Lake. The difference between the Data Lake and Metadata Lake is conceptually similar to a Halocline… Various technologies (Iglu/Snowplow and more) will be discussed from a feature standpoint to flesh out the technology capabilities needed for Data Lake governance.

Windows Phone 7 and Windows Azure are a good match because they both provide easy and familiar development environments, connectivity through the cloud, and scalability. They are compatible in these areas. The document discusses how Windows Phone 7 and Windows Azure can be used together through features like data storage in Windows Azure tables and blobs, push notifications, and identity management with Access Control Services. It provides examples of how to integrate the platforms for storing, retrieving, and displaying data stored in the cloud.

Data gravity is a reality when dealing with massive amounts and globally distributed systems. Processing this data requires distributed analytics processing across InterCloud. In this presentation we will share our real world experience with storing, routing, and processing big data workloads on Cisco Cloud Services and Amazon Web Services clouds.

The document discusses how cloud services are impacting the work of Oracle technology experts. It notes that many database administrator and fusion middleware administrator roles will transition to cloud providers as more systems move to the cloud. It outlines a roadmap for technology experts that includes trialing cloud services, ongoing learning, and adopting a hybrid approach using both on-premises and cloud systems. It concludes that while some tasks will shift to cloud providers, technology experts still have opportunities consulting on cloud services, developing cloud software, and supporting hybrid environments.

Pass Summit 2018 presentation Use Case Story of customer work at Microsoft with Higher Ed Customers.

This slide deck will show you techniques and technologies necessary to take a large, transaction SQL Server database and migrate it to Azure, Azure SQL Database, and Azure SQL Database Managed Instance

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.

An introduction to the concepts and requirements to get started developing data pipelines in Azure Data Factory version 1.

This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing, modeling and serving data on Azure. Finally, it discusses architectures like the lambda architecture and common data models.

This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing and modeling data in Azure. Finally, it discusses architectures like the lambda architecture and common data models.

This document discusses techniques for optimizing Power BI performance. It recommends tracing queries using DAX Studio to identify slow queries and refresh times. Tracing tools like SQL Profiler and log files can provide insights into issues occurring in the data sources, Power BI layer, and across the network. Focusing on optimization by addressing wait times through a scientific process can help resolve long-term performance problems.

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,

- Azure Data Lake makes big data easy to manage, debug, and optimize through services like Azure Data Lake Store and Azure Data Lake Analytics. - Azure Data Lake Store provides a hyper-scale data lake that allows storing any data in its native format at unlimited scale. Azure Data Lake Analytics allows running distributed queries and analytics jobs on data stored in Data Lake Store. - Azure Data Lake is based on open source technologies like Apache Hadoop, YARN, and provides a managed service with auto-scaling and a pay-per-use model through the Azure portal and tools like Visual Studio.

Slide deck by Nik Shahriar for the presentation topic Azure Data Factory, Azure Logic Apps at the C# Corner Toronto chapter Feb 2019 meetup

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components. 

The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.

1. Overview of DevOps 2. Infrastructure as Code (IaC) and Configuration as code 3. Identity and Security protection in CI CD environment 4. Monitor Health of the Infrastructure/Application 5. Open Source Software (OSS) and third-party tools, such as Chef, Puppet, Ansible, and Terraform to achieve DevOps. 6. Future of DevOps Application