Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
NOVA SQL User Group - Azure Synapse Analytics Overview - May 2020Timothy McAliley
Jim Boriotti presents an overview and demo of Azure Synapse Analytics, an integrated data platform for business intelligence, artificial intelligence, and continuous intelligence. Azure Synapse Analytics includes Synapse SQL for querying with T-SQL, Synapse Spark for notebooks in Python, Scala, and .NET, and Synapse Pipelines for data workflows. The demo shows how Azure Synapse Analytics provides a unified environment for all data tasks through the Synapse Studio interface.
Azure Synapse is Microsoft's new cloud analytics service offering that combines enterprise data warehouse and Big Data analytics capabilities. It offers a powerful and streamlined platform to facilitate the process of consolidating, storing, curating and analysing your data to generate reliable and actionable business insights.
This document provides an overview of Azure Databricks, including:
- Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services.
- Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks.
- The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC
This document provides an overview of Azure Synapse Analytics and its key capabilities. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and big data analytics. It allows querying data on-demand or at scale using serverless or provisioned resources. The document outlines Synapse's integrated data platform capabilities for business intelligence, artificial intelligence and continuous intelligence. It also describes the different types of analytics workloads that Synapse supports and key architectural components like the dedicated SQL pool and massively parallel processing concepts.
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.
Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
NOVA SQL User Group - Azure Synapse Analytics Overview - May 2020Timothy McAliley
Jim Boriotti presents an overview and demo of Azure Synapse Analytics, an integrated data platform for business intelligence, artificial intelligence, and continuous intelligence. Azure Synapse Analytics includes Synapse SQL for querying with T-SQL, Synapse Spark for notebooks in Python, Scala, and .NET, and Synapse Pipelines for data workflows. The demo shows how Azure Synapse Analytics provides a unified environment for all data tasks through the Synapse Studio interface.
Azure Synapse is Microsoft's new cloud analytics service offering that combines enterprise data warehouse and Big Data analytics capabilities. It offers a powerful and streamlined platform to facilitate the process of consolidating, storing, curating and analysing your data to generate reliable and actionable business insights.
This document provides an overview of Azure Databricks, including:
- Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services.
- Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks.
- The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC
This document provides an overview of Azure Synapse Analytics and its key capabilities. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and big data analytics. It allows querying data on-demand or at scale using serverless or provisioned resources. The document outlines Synapse's integrated data platform capabilities for business intelligence, artificial intelligence and continuous intelligence. It also describes the different types of analytics workloads that Synapse supports and key architectural components like the dedicated SQL pool and massively parallel processing concepts.
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.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
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.
Modern DW Architecture
- The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.
Develop scalable analytical solutions with Azure Data Factory & Azure SQL Dat...Microsoft Tech Community
In this session you will learn how to develop data pipelines in Azure Data Factory and build a Cloud-based analytical solution adopting modern data warehouse approaches with Azure SQL Data Warehouse and implementing incremental ETL orchestration at scale. With the multiple sources and types of data available in an enterprise today Azure Data factory enables full integration of data and enables direct storage in Azure SQL Data Warehouse for powerful and high-performance query workloads which drive a majority of enterprise applications and business intelligence applications.
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
HA/DR options with SQL Server in Azure and hybridJames Serra
What are all the high availability (HA) and disaster recovery (DR) options for SQL Server in a Azure VM (IaaS)? Which of these options can be used in a hybrid combination (Azure VM and on-prem)? I will cover features such as AlwaysOn AG, Failover cluster, Azure SQL Data Sync, Log Shipping, SQL Server data files in Azure, Mirroring, Azure Site Recovery, and Azure Backup.
Azure Data Factory is one of the newer data services in Microsoft Azure and is part of the Cortana Analyics Suite, providing data orchestration and movement capabilities.
This session will describe the key components of Azure Data Factory and take a look at how you create data transformation and movement activities using the online tooling. Additionally, the new tooling that shipped with the recently updated Azure SDK 2.8 will be shown in order to provide a quickstart for your cloud ETL projects.
This document provides an overview of Azure Synapse Analytics, a limitless analytics service that brings together data warehousing and big data analytics capabilities. It discusses how businesses currently have to maintain separate systems for operational data/relational data and big data/semi-structured data, which Azure Synapse addresses by providing a single service for end-to-end analytics using technologies like SQL and Spark across data warehouses and data lakes at cloud scale with unmatched speed.
This document discusses Microsoft Azure and its capabilities. It highlights that Azure has over 100 datacenters globally, with 19 regions currently online. It also notes that Azure has one of the top 3 networks in the world and offers larger VM sizes than AWS or Google Cloud. The document then summarizes some of Azure's core capabilities like compute, storage, databases, analytics and more. It provides examples of how customers can use Azure's tools and services.
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
Is there a way that we can build our Azure Data Factory all with parameters b...Erwin de Kreuk
Is there a way that we can build our Data Factory all with parameters all based on MetaData? Yes there's and I will show you how to. During this session I will show how you can load Incremental or Full datasets from your sql database to your Azure Data Lake. The next step is that we want to track our history from these extracted tables. We will do this with Azure Databricks using Delta Lake. The last step that we want, is to make this data available in Azure SQL Database or Azure Synapse Analytics. Oh and we want to have some logging as well from our processes A lot to talk and to demo about during this session.
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.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Azure Data Lake and Azure Data Lake AnalyticsWaqas Idrees
This document provides an overview and introduction to Azure Data Lake Analytics. It begins with defining big data and its characteristics. It then discusses the history and origins of Azure Data Lake in addressing massive data needs. Key components of Azure Data Lake are introduced, including Azure Data Lake Store for storing vast amounts of data and Azure Data Lake Analytics for performing analytics. U-SQL is covered as the query language for Azure Data Lake Analytics. The document also touches on related Azure services like Azure Data Factory for data movement. Overall it aims to give attendees an understanding of Azure Data Lake and how it can be used to store and analyze large, diverse datasets.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Techorama Belgium 2019: top Azure security fails and how to avoid themKarl Ots
Karl Ots has assessed the security of over 100 Azure solutions. He has found that there are 18 security pitfalls that are common across all industry verticals and company sizes. In this session, he will share what these security pitfalls are, why do they matter and how to mitigate them.
As presented by Karl Ots in Techorama Belgium 2019 conference in Antwerpen.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
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.
Modern DW Architecture
- The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.
Develop scalable analytical solutions with Azure Data Factory & Azure SQL Dat...Microsoft Tech Community
In this session you will learn how to develop data pipelines in Azure Data Factory and build a Cloud-based analytical solution adopting modern data warehouse approaches with Azure SQL Data Warehouse and implementing incremental ETL orchestration at scale. With the multiple sources and types of data available in an enterprise today Azure Data factory enables full integration of data and enables direct storage in Azure SQL Data Warehouse for powerful and high-performance query workloads which drive a majority of enterprise applications and business intelligence applications.
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
HA/DR options with SQL Server in Azure and hybridJames Serra
What are all the high availability (HA) and disaster recovery (DR) options for SQL Server in a Azure VM (IaaS)? Which of these options can be used in a hybrid combination (Azure VM and on-prem)? I will cover features such as AlwaysOn AG, Failover cluster, Azure SQL Data Sync, Log Shipping, SQL Server data files in Azure, Mirroring, Azure Site Recovery, and Azure Backup.
Azure Data Factory is one of the newer data services in Microsoft Azure and is part of the Cortana Analyics Suite, providing data orchestration and movement capabilities.
This session will describe the key components of Azure Data Factory and take a look at how you create data transformation and movement activities using the online tooling. Additionally, the new tooling that shipped with the recently updated Azure SDK 2.8 will be shown in order to provide a quickstart for your cloud ETL projects.
This document provides an overview of Azure Synapse Analytics, a limitless analytics service that brings together data warehousing and big data analytics capabilities. It discusses how businesses currently have to maintain separate systems for operational data/relational data and big data/semi-structured data, which Azure Synapse addresses by providing a single service for end-to-end analytics using technologies like SQL and Spark across data warehouses and data lakes at cloud scale with unmatched speed.
This document discusses Microsoft Azure and its capabilities. It highlights that Azure has over 100 datacenters globally, with 19 regions currently online. It also notes that Azure has one of the top 3 networks in the world and offers larger VM sizes than AWS or Google Cloud. The document then summarizes some of Azure's core capabilities like compute, storage, databases, analytics and more. It provides examples of how customers can use Azure's tools and services.
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
Is there a way that we can build our Azure Data Factory all with parameters b...Erwin de Kreuk
Is there a way that we can build our Data Factory all with parameters all based on MetaData? Yes there's and I will show you how to. During this session I will show how you can load Incremental or Full datasets from your sql database to your Azure Data Lake. The next step is that we want to track our history from these extracted tables. We will do this with Azure Databricks using Delta Lake. The last step that we want, is to make this data available in Azure SQL Database or Azure Synapse Analytics. Oh and we want to have some logging as well from our processes A lot to talk and to demo about during this session.
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.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Azure Data Lake and Azure Data Lake AnalyticsWaqas Idrees
This document provides an overview and introduction to Azure Data Lake Analytics. It begins with defining big data and its characteristics. It then discusses the history and origins of Azure Data Lake in addressing massive data needs. Key components of Azure Data Lake are introduced, including Azure Data Lake Store for storing vast amounts of data and Azure Data Lake Analytics for performing analytics. U-SQL is covered as the query language for Azure Data Lake Analytics. The document also touches on related Azure services like Azure Data Factory for data movement. Overall it aims to give attendees an understanding of Azure Data Lake and how it can be used to store and analyze large, diverse datasets.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Techorama Belgium 2019: top Azure security fails and how to avoid themKarl Ots
Karl Ots has assessed the security of over 100 Azure solutions. He has found that there are 18 security pitfalls that are common across all industry verticals and company sizes. In this session, he will share what these security pitfalls are, why do they matter and how to mitigate them.
As presented by Karl Ots in Techorama Belgium 2019 conference in Antwerpen.
DevSum - Top Azure security fails and how to avoid themKarl Ots
As presented at the DevSum19 conference in Stockholm, Sweden.
Karl Ots has assessed the security of over 100 solutions built on the Microsoft Azure cloud. He has found that there are 6 key security pitfalls that are common across all industry verticals and company sizes. In this session, he will share what these security pitfalls are, why do they matter and how to mitigate them.
Running OpenStack and Midonet - Nobuyuki Tamaoki, Virtual Tech JapanMidoNet
During the MidoNet Community Day in Japan, Nobuyuki discussed his experience with the installer for OpenStack and MidoNet with Docker for multi-node deployment.
https://github.com/midonet/orizuru
Presenter: Nobuyuki Tamaoki, Virtual Tech Japan also writer of @IT article “Tamaoki’s OpenStack Watch"
This document outlines two hands-on Docker labs presented by Anthony Costeseque, Estelle Auberix, and Jean-François Berenguer. The first lab teaches how to create an Azure Container Service, connect to it using SSH, build Docker images, run containers, and delete the service. The second lab adds exercises on pushing images to Docker Hub and orchestrating containers with Swarm, DC/OS and Kubernetes. Both labs are estimated to take 60-75 minutes and provide resources to follow along.
IT Camp 19: Top Azure security fails and how to avoid themKarl Ots
As delivered at the IT Camp 19 in Cluj-Napoca, Romania.
Karl Ots has assessed the security of over 100 solutions built on the Microsoft Azure cloud. He has found that there are 6 key security pitfalls that are common across all industry verticals and company sizes. In this session, he will share what these security pitfalls are, why do they matter and how to mitigate them.
AKS Azure Kubernetes Services - Azure Nights melbourne feb 2018Jorge Arteiro
This document outlines a presentation on Azure Kubernetes Services (AKS) and the steps to set up a development environment and deploy applications to AKS. It includes enabling Windows features for containers and the Windows Subsystem for Linux (WSL), installing client tools like Docker, Helm, Azure CLI and Visual Studio Code, creating an AKS cluster with Azure CLI commands, and deploying applications from source code to Kubernetes using Helm. It also discusses integrating AKS with other Azure services and includes demo use cases and relevant links.
Using GitHub Actions to Deploy your Workloads to AzureKasun Kodagoda
This presentation provides an introduction to GitHub Actions and the core concepts of GitHub Actions. Then dives into details about how you can use GitHub Actions for Azure to deploy your workloads to Azure Cloud Platform.
1) Ansible is being used at Backbase to automate the provisioning of different server configurations for testing their Customer Experience Platform (CXP).
2) A REST API and UI allow users to easily provision new environments from available server stacks configured with Ansible for testing.
3) This enables Backbase to implement continuous delivery practices like automated testing of new versions without affecting production environments.
Splunk forwarders were used to gain initial access to a network by exploiting their default credentials and REST API. This allowed deploying a malicious app that provided a shell. The shell was then used to pillage other systems by abusing credentials and data found in Chef scripts and GitHub repositories. Mitigations include changing default credentials, disabling the REST API on forwarders, improving logging and monitoring for unusual app deployments, using TLS for deployment server communications, and running Splunk in a less privileged manner.
Jupyter con meetup extended jupyter kernel gatewayLuciano Resende
Data Scientists are becoming a necessity of every company in the data-centric world of today, and with them comes the requirement to make available a elastic and interactive analytics platform. This session will describe our experience and best practices putting together an Analytical platform based on Jupyter stack and different kernels running in a distributed Apache Spark cluster.
Open Source Private Cloud Management with OpenStack and Security Evaluation w...XHANI TRUNGU
Nowadays, we hear about terms like, cloud computing, cloud architectures, virtualization technologies, cloud management systems, clustering and cloud security systems. By a first glance these terms are a bit vague, and questions arise about what is a cloud, what is virtualization and finally what is clustering.
Your data is much safer at home than it is letting some corporation "take care of it" for you, right? Security reviews for some of the top vendors' devices reveal many interesting findings. Like everything else, there are bugs. But knowing what kinds of bugs and how the vendors have responded will allow you to better understand the impact of plugging these devices into your network. Jeremy will show you just how low access control and least privilege are their list of priorities. He'll also explore the amount of test collateral and debug interfaces sloppily left shipping to consumers. From remote roots to stealing social network tokens to just plain weird stuff, he'll expand on how it's not just about what they do, but also what they don't do. And, he'll give you some useful guidelines on how to close the gaps yourself.
OWASP 2014 AppSec EU ZAP Advanced FeaturesSimon Bennetts
The document discusses the advanced features of OWASP ZAP, an open source web application penetration testing tool. It provides an overview of ZAP's main features like its intercepting proxy, scanners, spiders, and add-ons marketplace. It then describes some advanced features in more depth, including contexts for organizing tests, advanced scanning options, scripting with languages like Zest, and the Plug-n-Hack framework for deeper browser integration. The document concludes by noting various work-in-progress projects and encouraging involvement in ZAP's ongoing development.
DevOpsDaysRiga 2017: Mandi Walls - Building security into your workflow with ...DevOpsDays Riga
This document discusses using InSpec to build security checks into development workflows. It provides an example of using InSpec to check that an SSH configuration is using version 2. InSpec makes it possible to write tests against system configurations and services in a human-readable format. Tests can be packaged into shareable profiles and run during development and deployment to automate compliance checking.
BSides Manchester 2014 ZAP Advanced FeaturesSimon Bennetts
The document discusses the advanced features of OWASP ZAP, an open source web application penetration testing tool. It provides statistics on ZAP's usage and development community. Key advanced features discussed include contexts for scoping tests, advanced scanning options, scripting through languages like Zest and JavaScript, plug-n-hack for browser integration, and various works in progress. The source code is currently hosted on Google Code but may move to GitHub.
FaaS by Microsoft: Azure Functions and Azure Durable FunctionsChristian Lechner
Serverless and especially Functions as a Service are en vogue these days and every cloud provider has their own offering. In this talk, Christian Lechner will show you what Microsoft brings to the table offering Azure Functions. In addition, we will take a look at an extension of these functions, namely Durable Functions. They make your life easier when dealing with real world workflow-like scenarios. As talk is cheap, you will see some live coding to get insights to Azure Functions as well as into the experience when working in the Azure environment.
Codership's galera cluster installation and quickstart webinar march 2016Sakari Keskitalo
In this webinar, we will describe how to get started with Galera Cluster and build a functional multi-master cluster. First, will show how to easily install the required packages using the new preferred installation method – the dedicated Galera package repository. Then we will discuss the important Galera configuration settings and how to select values for them. Finally, we will demonstrate how to bootstrap a 3-node Galera installation with the right sequence of steps.
Once the nodes are up and running we will discuss how to monitor the health of the cluster and which status variables are important to watch.
Galera Cluster is trusted by thousands of users. Galera Cluster powers Percona XtraDB Cluster and MariaDB Enterprise Cluster. This is a webinar presented by Codership, the developers and experts of Galera Cluster.
In this webinar, we will describe how to get started with Galera Cluster and build a functional multi-master cluster. First, will show how to easily install the required packages using the new preferred installation method – the dedicated Galera package repository. Then we will discuss the important Galera configuration settings and how to select values for them. Finally, we will demonstrate how to bootstrap a 3-node Galera installation with the right sequence of steps.
Once the nodes are up and running we will discuss how to monitor the health of the cluster and which status variables are important to watch.
Galera Cluster is trusted by thousands of users. Galera Cluster powers Percona XtraDB Cluster and MariaDB Enterprise Cluster. This is a webinar presented by Codership, the developers and experts of Galera Cluster.
Codership's galera cluster installation and quickstart webinar march 2016Sakari Keskitalo
In this webinar, we will describe how to get started with Galera Cluster and build a functional multi-master cluster. First, will show how to easily install the required packages using the new preferred installation method – the dedicated Galera package repository. Then we will discuss the important Galera configuration settings and how to select values for them. Finally, we will demonstrate how to bootstrap a 3-node Galera installation with the right sequence of steps.
Once the nodes are up and running we will discuss how to monitor the health of the cluster and which status variables are important to watch.
Galera Cluster is trusted by thousands of users. Galera Cluster powers Percona XtraDB Cluster and MariaDB Enterprise Cluster. This is a webinar presented by Codership, the developers and experts of Galera Cluster.
Similar to azure synapse analytics end-to-end solution-hands-on at 20200728 (20)
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
2. # hands-on note
1. Enable “Synapse” resource provider on your subscription with following next pages
2. Visit https://github.com/microsoft/MCW-Azure-Synapse-Analytics-and-AI
3. Open “Hands-on lab/Before the HOL - Azure Synapse Analytics and AI.md”
• Task 2: Put
• Require to choose your region among below
* 'westus2,eastus,northeurope,westeurope,southeastasia,australiaeast,
westcentralus,southcentralus,eastus2,uksouth,westus'
• “Unique Suffix” as unique globally but avoid long one
• Confirm on other page to avoid long “Unique Suffix” error
• Azure resources deployments would take about 10 min
• Task 4: Make sure to execute and complete “az login“ on Cloud Shell – Azure Portal
• Task 5: Command completion would take about 8 min
4. Open “Hands-on lab/HOL step-by step - Azure Synapse Analytics and AI.md”
• Today’s goal is to complete until exercise 4
• Exercise 2
• Task 2
• 18: Refresh browser with F5 if mapping between input and output columns wouldn’t come up
• 26: Data copy will takes about 45 min