Cloud computing is the present as well as future for the IT industry due to its cost and flexibility , the slide shows various components of cloud computing
AWS Summit Berlin 2013 - Optimizing your AWS applications and usage to reduce...AWS Germany
Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically. Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.
Spark and the Hadoop Ecosystem: Best Practices for Amazon EMRAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The document provides an overview of big data concepts and Amazon Web Services (AWS) products for big data and analytics. It describes challenges of big data including unpredictable resource demand and job orchestration complexities. It then summarizes AWS products for data collection, storage, processing, analytics and machine learning. Specific examples are given using AWS services like Redshift, EMR, Kinesis and DynamoDB for scenarios like data warehousing, real-time streaming and Hadoop workloads. Core principles and common challenges of big data implementations on AWS are also outlined.
This document provides an introduction to various AWS big data services including Kinesis, Kinesis Firehose, Kinesis Analytics, SQS, IoT, Data Pipeline, DynamoDB, EMR, Lambda, Redshift, and QuickSight. It describes the core concepts and components of each service. For services like Kinesis, Kinesis Analytics, and EMR it provides details on architecture, terminology, pricing models, and best practices. The document aims to give readers an overview of the AWS big data landscape and how the different services can be used together in a big data context.
BDA302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Announcing AWS Snowball Edge and AWS Snowmobile - December 2016 Monthly Webin...Amazon Web Services
Whether you’re planning a data center shut down or just need to move large volumes of archived data from your on-premises environment, attend this webinar and learn more about how AWS Snowmobile and AWS Snowball Edge can help you migrate your terabytes or petabytes of critical data in a fast, secure and cost effective way. Hear how customers are using these two new services to transform their business model and advance their IT strategy in a way that was not possible before from a time and cost perspective.
Learning Objectives:
• Learn about the capabilities, features, and benefits of AWS Snowball Edge and AWS Snowmobile
• Learn key use cases for AWS Snowball Edge and AWS Snowmobile
• Learn how AWS Snowball Edge is more than just a data transfer service
• Be able to determine when to use which data transfer service from AWS
AWS re:Invent 2016: How Mapbox Uses the AWS Edge to Deliver Fast Maps for Mob...Amazon Web Services
Ian Ward, Platform and Security Engineer from Mapbox, discusses how the AWS global edge network helps improve the availability and performance of delivering hundreds of billions of map tiles to hundreds of millions of end users across the globe on mobile devices, in cars, and over the web. In this session, Ian shares insights on how Mapbox manages day-to-day edge operations using Amazon CloudFront logs, dashboards, and ad hoc queries, and how Mapbox has configured CloudFront with dozens of behaviors and origins to customize their content delivery. Mapbox has grown from using a single AWS region to using several regions, so Ian also explains how his team uses Amazon Route 53 and open source tools to simplify complexity around regional failover, and how Mapbox leverages AWS WAF to deter attacks and abuse.
AWS Summit Berlin 2013 - Optimizing your AWS applications and usage to reduce...AWS Germany
Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically. Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.
Spark and the Hadoop Ecosystem: Best Practices for Amazon EMRAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The document provides an overview of big data concepts and Amazon Web Services (AWS) products for big data and analytics. It describes challenges of big data including unpredictable resource demand and job orchestration complexities. It then summarizes AWS products for data collection, storage, processing, analytics and machine learning. Specific examples are given using AWS services like Redshift, EMR, Kinesis and DynamoDB for scenarios like data warehousing, real-time streaming and Hadoop workloads. Core principles and common challenges of big data implementations on AWS are also outlined.
This document provides an introduction to various AWS big data services including Kinesis, Kinesis Firehose, Kinesis Analytics, SQS, IoT, Data Pipeline, DynamoDB, EMR, Lambda, Redshift, and QuickSight. It describes the core concepts and components of each service. For services like Kinesis, Kinesis Analytics, and EMR it provides details on architecture, terminology, pricing models, and best practices. The document aims to give readers an overview of the AWS big data landscape and how the different services can be used together in a big data context.
BDA302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Announcing AWS Snowball Edge and AWS Snowmobile - December 2016 Monthly Webin...Amazon Web Services
Whether you’re planning a data center shut down or just need to move large volumes of archived data from your on-premises environment, attend this webinar and learn more about how AWS Snowmobile and AWS Snowball Edge can help you migrate your terabytes or petabytes of critical data in a fast, secure and cost effective way. Hear how customers are using these two new services to transform their business model and advance their IT strategy in a way that was not possible before from a time and cost perspective.
Learning Objectives:
• Learn about the capabilities, features, and benefits of AWS Snowball Edge and AWS Snowmobile
• Learn key use cases for AWS Snowball Edge and AWS Snowmobile
• Learn how AWS Snowball Edge is more than just a data transfer service
• Be able to determine when to use which data transfer service from AWS
AWS re:Invent 2016: How Mapbox Uses the AWS Edge to Deliver Fast Maps for Mob...Amazon Web Services
Ian Ward, Platform and Security Engineer from Mapbox, discusses how the AWS global edge network helps improve the availability and performance of delivering hundreds of billions of map tiles to hundreds of millions of end users across the globe on mobile devices, in cars, and over the web. In this session, Ian shares insights on how Mapbox manages day-to-day edge operations using Amazon CloudFront logs, dashboards, and ad hoc queries, and how Mapbox has configured CloudFront with dozens of behaviors and origins to customize their content delivery. Mapbox has grown from using a single AWS region to using several regions, so Ian also explains how his team uses Amazon Route 53 and open source tools to simplify complexity around regional failover, and how Mapbox leverages AWS WAF to deter attacks and abuse.
(BDT310) Big Data Architectural Patterns and Best Practices on AWS | AWS re:I...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The document discusses Amazon Elasticsearch Service and provides guidance on setting up and optimizing Elasticsearch clusters on AWS. It begins with an overview of Amazon Elasticsearch Service and how it can be used for log analytics and search applications. It then covers topics like data ingestion using Kinesis Firehose, best practices for determining the appropriate number of instances and shards, configuration recommendations, and deployment architectures like dedicated masters and zone awareness. The document aims to help users easily deploy production-ready Elasticsearch clusters on AWS.
This document discusses various AWS services for migrating large data sets to the cloud, including the Snow family of devices (Snowball, Snowball Edge, Snowmobile) for offline data transfer of petabyte and exabyte scale data. It also covers AWS Storage Gateway for integrating on-premises storage with cloud storage using file, volume and tape gateways. Other services mentioned include Amazon S3 Transfer Acceleration for accelerating large file uploads, Amazon EFS for accessing file storage over AWS Direct Connect, and partner solutions for backup, disaster recovery and tiering storage between on-premises and AWS. Use cases are provided for various industries demonstrating how these services can address cloud migration, hybrid scenarios, backup and disaster
Disaster Recovery on AWS Webinar December 2017 - IL WebinarAmazon Web Services
Learn about the use of the AWS Cloud as a disaster recovery (DR) environment and explore how architectural approaches to DR and business continuity on AWS give you the skills and experience you need to start building cloud-based production applications.
- Create DR environments for your existing systems to minimize technology and business risks
- Reduce your infrastructure costs and pay only for the DR resources you use
- Test your DR provision more frequently to ensure your critical systems and data are protected
Rethinking the database for the cloud (iJAWS)Rasmus Ekman
The document discusses rethinking database architecture for cloud environments. Traditional on-premises architectures with a single relational database can have problems with scalability, management difficulty, cost and performance. The cloud allows for distributing data across several specialized services matched to data type and access patterns. Examples show mapping services like DynamoDB, S3, RDS and Redshift to use cases like social gaming and e-commerce based on factors like data temperature, latency and cost. Choosing the right architecture is important for performance, reliability and scalability in the cloud.
February 2016 Webinar Series - Architectural Patterns for Big Data on AWSAmazon Web Services
With an ever-increasing set of technologies to process big data, organizations often struggle to understand how to build scalable and cost-effective big data applications.
In this webinar, we will simplify big data processing as a pipeline comprising various stages; and then show you how to choose the right technology for each stage based on criteria such as data structure, design patterns, and best practices.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
With AWS you can choose the right storage service for the right use case. Given the myriad of choices, from object storage to block storage, this session will profile details and examples of some of the choices available to you, with details on real world deployments from customers using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), Amazon Glacier and AWS Storage Gateway. In addition, this session will also cover all the new AWS storage features introduced in the last 12 months.
El almacenamiento en la nube es un componente crítico de la informática en la nube, que guarda la información que utilizan las aplicaciones. El análisis de big data, los almacenes de datos, el Internet de las cosas, las bases de datos y las aplicaciones de backup y archivado dependen de algún tipo de arquitectura de almacenamiento de datos. El almacenamiento en la nube, por lo general, es más fiable, escalable y seguro que los sistemas de almacenamiento en las instalaciones tradicionales.
Serverlesss Big Data Analytics with Amazon Athena and QuicksightAmazon Web Services
Check out how you can easily query raw data in various formats in Amazon S3, transform it into a canonical form, analyze it, and build dashboards to get more insights from your data.
Streaming data analytics (Kinesis, EMR/Spark) - Pop-up Loft Tel Aviv Amazon Web Services
"Low latency analytics is becoming a very popular scenario. In this session we will discuss several architectural options for doing
analytics on moving data using Amazon Kinesis and EMR/Spark Streaming and share some best practices and real world examples."
Day 4 - Big Data on AWS - RedShift, EMR & the Internet of ThingsAmazon Web Services
Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
- Learn how AWS can help you process and make better use of your data with meaningful insights.
- Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions.
- Learn about real time data processing with Amazon Kinesis.
Streaming ETL for Data Lakes using Amazon Kinesis Firehose - May 2017 AWS Onl...Amazon Web Services
Learning Objectives:
- Understand key requirements for collecting, preparing, and loading streaming data into data lakes
- Get an overview of transmitting data using Amazon Kinesis Firehose
- Learn how to perform data transformations with Amazon Kinesis Firehose
Data lakes enable your employees across the organization to access and analyze massive amounts of unstructured and structured data from disparate data sources, many of which generate data continuously and rapidly. Making this data available in a timely fashion for analysis requires a streaming solution that can durably and cost-effectively ingest this data into your data lake. Amazon Kinesis Firehose is a fully managed service that makes it easy to prepare and load streaming data into AWS. In this tech talk, we will provide an overview of Amazon Kinesis Firehose and dive deep into how you can use the service to collect, transform, batch, compress, and load real-time streaming data into your Amazon S3 data lakes.
ENT313 Deploying a Disaster Recovery Site on AWS: Minimal Cost with Maximum E...Amazon Web Services
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS features and services along with Disaster Recovery architectures that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your Disaster Recovery plan and discuss example scenarios showing how to recover from a disaster.
This session will begin with an introduction to non-relational (NoSQL) databases and compare them with relational (SQL) databases. We will also explain the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service. Learn the fundamentals of DynamoDB and see the new DynamoDB console first-hand as we discuss common use cases and benefits of this high-performance key-value and JSON document store.
BDA308 Serverless Analytics with Amazon Athena and Amazon QuickSight, featuri...Amazon Web Services
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools. NOTE: Make this more themed towards QuickSight as it applies to other AWS Big Data Services - Redshift, Athena, S3, RDS.
AWS re:Invent 2016: Learn How FINRA Aligns Billions of Time Ordered Events wi...Amazon Web Services
FINRA is a leader in the Financial Services industry who sought to move toward real-time data insights of billions of time-ordered market events by migrating from SQL batch processes on-prem, to Apache Spark in the cloud. By using Apache Spark on Amazon EMR, FINRA can now test on realistic data from market downturns, enhancing their ability to provide investor protection and promote market integrity (FINRA enacts rules and provides guidance that securities exchanges & brokers must follow). By using AWS Spot instances, FINRA has saved up to 50% from its on premises solution, increased elasticity/scalability, and accelerated reprocessing requests (from months to days). Learn best practices on how FINRA moves toward real-time data analytics with Spark and AWS, while managing production workloads in parallel, increasing performance and IT efficiency, reducing cost, and modernizing and scaling their infrastructure to prepare for real-time processing in the future.
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices-from object storage to block storage-that is available to you. We include specifics about real-world deployments from customers who are using Amazon S3, Amazon EBS, Amazon Glacier, and AWS Storage Gateway.
Data comes in a variety of forms and in order to gain insight from this data you need to have the right platform in place. AWS has the services to cover all types of data, whether you need databases for structured data, Hadoop for unstructured data or a streaming engine for high-velocity data. In this session we will cover the various data analytics services on AWS and when to use them.
An Overview of AWS Services for Data Storage and Migration - SRV205 - Atlanta...Amazon Web Services
In this session, we explore the features and functions of AWS storage services. We provide context on the AWS storage portfolio, and we cover the most common use cases for AWS offerings for object, file, block, and migration technologies, including the AWS Partner Network (APN) ecosystem. Then we examine each service, using customer case studies as examples. You gain an understanding of how to select storage and start moving workloads or building new ones.
ENT316 Keeping Pace With The Cloud: Managing and Optimizing as You ScaleAmazon Web Services
"With cloud maturity comes operational efficiencies and endless potential for innovation and business growth. However, the complexities of governing cloud infrastructure are impeding without the right strategy. Visibility, accountability, and actionable insights are some of the most invaluable considerations. The AWS cloud clearly enables convenience and cost savings for organizations that know how to leverage its full potential. Amazon EC2 Reserved Instances (RIs) in particular, present a tremendous opportunity when scaling to save significantly on capacity but there are many considerations to fully reaping the benefits of RIs. In this session, CloudCheckr CTO Patrick Gartlan will present issues that every organization runs into when scaling, provide best practices for how to combat them and help you show your boss how RIs help you save money and move faster.
This session is brought to you by AWS Summit New York City sponsor, CloudCheckr. "
AWS webinar - optimize your aws data transfer out for cost and performance.Nazar Spak
This document discusses optimizing AWS Data Transfer Out costs and performance. It introduces AWS Direct Connect and Amazon CloudFront as options to reduce data transfer costs compared to transferring data directly over the public internet. AWS Direct Connect provides a dedicated private connection between an organization's network and AWS, while Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations close to users. The presentation provides an overview of these services and common use cases. It also includes a demo of using Amazon CloudFront to deliver static content from an S3 bucket.
The Future of Digital Advertising with Cloud Computing - co-presented with Ad...Amazon Web Services
Introduction to Amazon Web Services and cloud computing and how it supports the Digital Marketing industry. AdRoll CEO, Aaron Bell, describes their success story on AWS using DynamoDB for retargeting.
In today's world, consumer habits change fast and marketing decisions need to be made within seconds, not days. Delivering engaging advertising experiences requires real time, high performing architectures that provide digital advertisers the ability to measure and improve the performance of their campaigns and tie them more closely to corporate goals. The insights gleaned from the massive amounts of data collected can then be used to dynamically adjust media spend and creative execution for optimal performance. The AWS Cloud enables you to deliver marketing content and advertisements with the levels of availability, performance, and personalization that your customers expect. Plus, AWS lowers your costs. Join us to learn about how big data and low latency / high performing architectures are changing the game for digital advertising.
(BDT310) Big Data Architectural Patterns and Best Practices on AWS | AWS re:I...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The document discusses Amazon Elasticsearch Service and provides guidance on setting up and optimizing Elasticsearch clusters on AWS. It begins with an overview of Amazon Elasticsearch Service and how it can be used for log analytics and search applications. It then covers topics like data ingestion using Kinesis Firehose, best practices for determining the appropriate number of instances and shards, configuration recommendations, and deployment architectures like dedicated masters and zone awareness. The document aims to help users easily deploy production-ready Elasticsearch clusters on AWS.
This document discusses various AWS services for migrating large data sets to the cloud, including the Snow family of devices (Snowball, Snowball Edge, Snowmobile) for offline data transfer of petabyte and exabyte scale data. It also covers AWS Storage Gateway for integrating on-premises storage with cloud storage using file, volume and tape gateways. Other services mentioned include Amazon S3 Transfer Acceleration for accelerating large file uploads, Amazon EFS for accessing file storage over AWS Direct Connect, and partner solutions for backup, disaster recovery and tiering storage between on-premises and AWS. Use cases are provided for various industries demonstrating how these services can address cloud migration, hybrid scenarios, backup and disaster
Disaster Recovery on AWS Webinar December 2017 - IL WebinarAmazon Web Services
Learn about the use of the AWS Cloud as a disaster recovery (DR) environment and explore how architectural approaches to DR and business continuity on AWS give you the skills and experience you need to start building cloud-based production applications.
- Create DR environments for your existing systems to minimize technology and business risks
- Reduce your infrastructure costs and pay only for the DR resources you use
- Test your DR provision more frequently to ensure your critical systems and data are protected
Rethinking the database for the cloud (iJAWS)Rasmus Ekman
The document discusses rethinking database architecture for cloud environments. Traditional on-premises architectures with a single relational database can have problems with scalability, management difficulty, cost and performance. The cloud allows for distributing data across several specialized services matched to data type and access patterns. Examples show mapping services like DynamoDB, S3, RDS and Redshift to use cases like social gaming and e-commerce based on factors like data temperature, latency and cost. Choosing the right architecture is important for performance, reliability and scalability in the cloud.
February 2016 Webinar Series - Architectural Patterns for Big Data on AWSAmazon Web Services
With an ever-increasing set of technologies to process big data, organizations often struggle to understand how to build scalable and cost-effective big data applications.
In this webinar, we will simplify big data processing as a pipeline comprising various stages; and then show you how to choose the right technology for each stage based on criteria such as data structure, design patterns, and best practices.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
With AWS you can choose the right storage service for the right use case. Given the myriad of choices, from object storage to block storage, this session will profile details and examples of some of the choices available to you, with details on real world deployments from customers using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), Amazon Glacier and AWS Storage Gateway. In addition, this session will also cover all the new AWS storage features introduced in the last 12 months.
El almacenamiento en la nube es un componente crítico de la informática en la nube, que guarda la información que utilizan las aplicaciones. El análisis de big data, los almacenes de datos, el Internet de las cosas, las bases de datos y las aplicaciones de backup y archivado dependen de algún tipo de arquitectura de almacenamiento de datos. El almacenamiento en la nube, por lo general, es más fiable, escalable y seguro que los sistemas de almacenamiento en las instalaciones tradicionales.
Serverlesss Big Data Analytics with Amazon Athena and QuicksightAmazon Web Services
Check out how you can easily query raw data in various formats in Amazon S3, transform it into a canonical form, analyze it, and build dashboards to get more insights from your data.
Streaming data analytics (Kinesis, EMR/Spark) - Pop-up Loft Tel Aviv Amazon Web Services
"Low latency analytics is becoming a very popular scenario. In this session we will discuss several architectural options for doing
analytics on moving data using Amazon Kinesis and EMR/Spark Streaming and share some best practices and real world examples."
Day 4 - Big Data on AWS - RedShift, EMR & the Internet of ThingsAmazon Web Services
Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
- Learn how AWS can help you process and make better use of your data with meaningful insights.
- Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions.
- Learn about real time data processing with Amazon Kinesis.
Streaming ETL for Data Lakes using Amazon Kinesis Firehose - May 2017 AWS Onl...Amazon Web Services
Learning Objectives:
- Understand key requirements for collecting, preparing, and loading streaming data into data lakes
- Get an overview of transmitting data using Amazon Kinesis Firehose
- Learn how to perform data transformations with Amazon Kinesis Firehose
Data lakes enable your employees across the organization to access and analyze massive amounts of unstructured and structured data from disparate data sources, many of which generate data continuously and rapidly. Making this data available in a timely fashion for analysis requires a streaming solution that can durably and cost-effectively ingest this data into your data lake. Amazon Kinesis Firehose is a fully managed service that makes it easy to prepare and load streaming data into AWS. In this tech talk, we will provide an overview of Amazon Kinesis Firehose and dive deep into how you can use the service to collect, transform, batch, compress, and load real-time streaming data into your Amazon S3 data lakes.
ENT313 Deploying a Disaster Recovery Site on AWS: Minimal Cost with Maximum E...Amazon Web Services
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS features and services along with Disaster Recovery architectures that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your Disaster Recovery plan and discuss example scenarios showing how to recover from a disaster.
This session will begin with an introduction to non-relational (NoSQL) databases and compare them with relational (SQL) databases. We will also explain the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service. Learn the fundamentals of DynamoDB and see the new DynamoDB console first-hand as we discuss common use cases and benefits of this high-performance key-value and JSON document store.
BDA308 Serverless Analytics with Amazon Athena and Amazon QuickSight, featuri...Amazon Web Services
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools. NOTE: Make this more themed towards QuickSight as it applies to other AWS Big Data Services - Redshift, Athena, S3, RDS.
AWS re:Invent 2016: Learn How FINRA Aligns Billions of Time Ordered Events wi...Amazon Web Services
FINRA is a leader in the Financial Services industry who sought to move toward real-time data insights of billions of time-ordered market events by migrating from SQL batch processes on-prem, to Apache Spark in the cloud. By using Apache Spark on Amazon EMR, FINRA can now test on realistic data from market downturns, enhancing their ability to provide investor protection and promote market integrity (FINRA enacts rules and provides guidance that securities exchanges & brokers must follow). By using AWS Spot instances, FINRA has saved up to 50% from its on premises solution, increased elasticity/scalability, and accelerated reprocessing requests (from months to days). Learn best practices on how FINRA moves toward real-time data analytics with Spark and AWS, while managing production workloads in parallel, increasing performance and IT efficiency, reducing cost, and modernizing and scaling their infrastructure to prepare for real-time processing in the future.
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices-from object storage to block storage-that is available to you. We include specifics about real-world deployments from customers who are using Amazon S3, Amazon EBS, Amazon Glacier, and AWS Storage Gateway.
Data comes in a variety of forms and in order to gain insight from this data you need to have the right platform in place. AWS has the services to cover all types of data, whether you need databases for structured data, Hadoop for unstructured data or a streaming engine for high-velocity data. In this session we will cover the various data analytics services on AWS and when to use them.
An Overview of AWS Services for Data Storage and Migration - SRV205 - Atlanta...Amazon Web Services
In this session, we explore the features and functions of AWS storage services. We provide context on the AWS storage portfolio, and we cover the most common use cases for AWS offerings for object, file, block, and migration technologies, including the AWS Partner Network (APN) ecosystem. Then we examine each service, using customer case studies as examples. You gain an understanding of how to select storage and start moving workloads or building new ones.
ENT316 Keeping Pace With The Cloud: Managing and Optimizing as You ScaleAmazon Web Services
"With cloud maturity comes operational efficiencies and endless potential for innovation and business growth. However, the complexities of governing cloud infrastructure are impeding without the right strategy. Visibility, accountability, and actionable insights are some of the most invaluable considerations. The AWS cloud clearly enables convenience and cost savings for organizations that know how to leverage its full potential. Amazon EC2 Reserved Instances (RIs) in particular, present a tremendous opportunity when scaling to save significantly on capacity but there are many considerations to fully reaping the benefits of RIs. In this session, CloudCheckr CTO Patrick Gartlan will present issues that every organization runs into when scaling, provide best practices for how to combat them and help you show your boss how RIs help you save money and move faster.
This session is brought to you by AWS Summit New York City sponsor, CloudCheckr. "
AWS webinar - optimize your aws data transfer out for cost and performance.Nazar Spak
This document discusses optimizing AWS Data Transfer Out costs and performance. It introduces AWS Direct Connect and Amazon CloudFront as options to reduce data transfer costs compared to transferring data directly over the public internet. AWS Direct Connect provides a dedicated private connection between an organization's network and AWS, while Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations close to users. The presentation provides an overview of these services and common use cases. It also includes a demo of using Amazon CloudFront to deliver static content from an S3 bucket.
The Future of Digital Advertising with Cloud Computing - co-presented with Ad...Amazon Web Services
Introduction to Amazon Web Services and cloud computing and how it supports the Digital Marketing industry. AdRoll CEO, Aaron Bell, describes their success story on AWS using DynamoDB for retargeting.
In today's world, consumer habits change fast and marketing decisions need to be made within seconds, not days. Delivering engaging advertising experiences requires real time, high performing architectures that provide digital advertisers the ability to measure and improve the performance of their campaigns and tie them more closely to corporate goals. The insights gleaned from the massive amounts of data collected can then be used to dynamically adjust media spend and creative execution for optimal performance. The AWS Cloud enables you to deliver marketing content and advertisements with the levels of availability, performance, and personalization that your customers expect. Plus, AWS lowers your costs. Join us to learn about how big data and low latency / high performing architectures are changing the game for digital advertising.
AWS re:Invent 2016: NEW LAUNCH! AWS announced several new services and capabilities including elastic GPUs for EC2, new EC2 instance types like T2.xlarge and T2.2xlarge, next generation R4 memory optimized instances, programmable F1 instances, Amazon Lightsail for simple virtual private servers, interactive queries on data in S3 with Amazon Athena, image and facial recognition with Amazon Rekognition, text-to-speech with Amazon Polly, natural language processing with Amazon Lex, PostgreSQL compatibility for Amazon Aurora, local compute and messaging for connected devices with AWS Greengrass, petabyte-scale data transport with storage and compute using AWS Snowball Edge and AWS Snow
The document provides an overview of Amazon Web Services (AWS) and its capabilities across compute, storage, database, analytics, artificial intelligence, developer tools, and other services. It highlights the scalability, reliability, and security of the AWS platform and introduces new and expanded capabilities across compute types, databases, analytics, artificial intelligence, edge computing, data transfer, and migration services. It also summarizes AWS' global infrastructure and support offerings.
The document provides an overview of Amazon Web Services (AWS) and its capabilities across compute, storage, database, analytics, artificial intelligence, developer tools, and other services. It highlights the scalability, reliability, and security of the AWS platform and describes various compute instance types, databases, analytics tools, artificial intelligence services, migration services, edge computing capabilities using Greengrass and Snowball Edge, and exabyte-scale data transport with Snowmobile.
Enterprise Cloud Computing with AWS for internal partner use. The document discusses how AWS provides cloud computing services including compute, storage, database, networking and other platform services. It highlights how AWS services like EC2, S3, EBS, RDS allow customers to improve agility, reduce costs and easily scale infrastructure compared to on-premise solutions. Examples are given of how various companies use AWS for applications, big data, disaster recovery and more.
AWS Webcast - AWS 101 - Journey to the AWS Cloud: Introduction to AWSAmazon Web Services
Are you new to cloud computing and would like to learn more about Amazon Web Services? If you intend to implement a project and would like to discover the basics of the AWS Cloud, or if you are a startup looking to evaluate cloud computing, attend this complimentary webinar.
Getting Started with Managed Database Services on AWS - AWS Summit Tel Aviv 2017Amazon Web Services
In addition to running databases in Amazon EC2, AWS customers can choose among a variety of managed database services. These services save effort, save time, and unlock new capabilities and economies. In this session, we make it easy to understand how they differ, what they have in common, and how to choose one or more. We explain the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service; Amazon RDS, a relational database service in the cloud; and Amazon Redshift, a fully managed, petabyte-scale data-warehouse solution that can be surprisingly economical. We will cover how each service might help support your application and how to get started.
AWS Summit 2014 Melbourne - Breakout 1
Amazon Workspaces is a new service from AWS that delivery fully managed desktops in the Cloud. In this session you be able to learn more about the benefits and capabilities of Workspaces and see a demo of the user's experience when using Workspaces and the administrators experience in managing it.
Presenter: Dean Samuels, Solutions Architect, Amazon Web Services
Migrating Data to the Cloud: Exploring Your Options from AWS (STG205-R1) - AW...Amazon Web Services
The document discusses various options for migrating data to the AWS cloud, including AWS Direct Connect for private connectivity, AWS DataSync for online data transfer, the AWS Snowball and Snowball Edge devices for offline data transfer of large volumes, AWS Storage Gateway for hybrid storage, AWS Transfer for SFTP, and Amazon S3 Transfer Acceleration. It provides overviews and use cases for each service and how they can help with migrating and managing data in hybrid cloud environments.
AWS Summit 2014 Perth - Breakout 6
Amazon Workspaces is a new service from AWS that delivery fully managed desktops in the Cloud. In this session you be able to learn more about the benefits and capabilities of Workspaces and see a demo of the user's experience when using Workspaces and the administrators experience in managing it.
Presenter: Dean Samuels, Solutions Architect, Amazon Web Services
이 강연에서는 NoSQL 데이터베이스 서비스인 Amazon DynamoDB 서비스를 간단하게 소개하고, 새롭게 발표된 신규 시간 기반 (TTL) 데이터 관리 기능 및 인메모리 캐시 신규 기능 (Amazon DynamoDB Accelerator) 등에 대해 함께 설명해 드릴 예정입니다.
연사: Pranav Nambiar, 아마존 웹서비스 Amazon DynamoDB 총괄 프로덕트 매니저
Enterprise Workloads love running on AWS! In this session come and learn about the ways that enterprises have successfully migrated their critical Microsoft, SAP and Oracle workloads to AWS to improve operational performance.
Speaker:
Danny Jenkins, Solutions Architect, Amazon Web Services
Are you deploying Windows on AWS? Are you interested in taking advantage of existing investments when running Windows workloads on AWS? In this session we will discuss real world customer examples including as SharePoint, Exchange, SQL Server, and Remote Desktop Services with licensing options. We will explore deployment options and provide an overview of the AWS created QuickStarts and QuickLaunches to help with speed of deployment. This session will also include migration options for customer running End of Extended support products such as Windows Server2003 and SQL2005.
Lunch and Learn - Store and Move your Data To & From the AWS Cloud, Markku Le...Amazon Web Services
This document provides an overview and summary of options for storing and moving data to and from AWS cloud storage services. It discusses the problems with traditional on-premise storage solutions and how AWS storage services can help by providing scalable, cost-effective storage across different services optimized for various data and access needs such as block storage, file storage, archive storage, and backup storage. The document also covers data transfer options, choosing the right data store, disaster recovery strategies using AWS, and examples of companies using AWS storage services successfully.
As part of the Introduction to AWS Workshop Series, see how to scale your website from your first user, right up to a complex architecture to support 10 million users.
This document provides an overview of Amazon Web Services (AWS). It begins with a high-level introduction to AWS and why organizations are adopting cloud computing on AWS. It then provides a 1,000 foot view of the various compute, storage, database, analytics and application services available in the AWS toolbox. Finally, it addresses some top questions people have when first approaching AWS.
This document provides an overview of Amazon Web Services (AWS) including its history, services, pricing model, global infrastructure, and how customers can get started with AWS. It describes how AWS began as Amazon's internal infrastructure and has grown to serve over 1 million customers globally across industries like startups, enterprises, and government agencies. The document outlines AWS's broad range of cloud computing services across categories like compute, storage, databases, analytics, mobile, and more. It emphasizes AWS's focus on innovation with new services and features, lower prices through economies of scale, and its utility-based on-demand pricing model. Finally, it suggests steps for getting started like using the free tier, training, and certification programs.
The document provides an overview of Amazon Web Services (AWS) and how enterprises are using and benefiting from AWS, including using AWS for development and testing environments, building new applications, enhancing existing on-premises applications with AWS services, integrating cloud-based and on-premises applications, migrating applications to AWS fully or partially, and moving entire data centers to AWS. It also discusses the broad range of AWS services available and strategies for how enterprises can get started with AWS.
You’re interested in the cloud, and you want to start learning more. In this webcast we will answer the following questions:
• What is Cloud Computing?
• What are the benefits of Cloud Computing?
• What are AWS’s products and what workloads can I run with them?
• Who is using the cloud and what are they using it for?
Similar to Amazon web services | Cloud Computing |Rahul SIngh (20)
PMP Project management simple to complex | Rahul SinghRahul Singh
Project management involves applying skills and techniques to meet project requirements. Key skills for project managers include communication, flexibility, planning, and team building. Project management areas include scope, quality, risk, procurement, schedule, cost, integration, and communication. Projects are profiled and assigned to managers based on their type and complexity. Project phases include initiation, planning, execution, and closeout. Managing client expectations, values, and effective communication are important for success.
Microsoft sql server integration services| Rahul Singh Rahul Singh
The document discusses SQL Server Integration Services (SSIS), an Extract, Transform, Load (ETL) tool. It covers key SSIS concepts like packages, control flow, data flow, tasks, containers, variables, parameters and event handling. It also discusses best practices for performance, debugging, testing, deployment and administration of SSIS packages.
Krishna teaches Arjuna about the true nature of the soul and reality. He explains that the soul is eternal and distinct from the temporary physical body and mind. He urges Arjuna to fight in the battle not for land, respect or out of attachment, but as a selfless sacrifice and worship of the supreme consciousness (God) within. By fighting without attachment to outcomes, Arjuna can achieve purity, peace and ultimately self-realization.
How to manage people | Managers |Rahul Gulab SInghRahul Singh
This document provides tips for managing employees effectively. It recommends having regular check-ins with employees to discuss their work and get feedback. Managers should set clear and measurable goals, address any issues factually by citing specific examples rather than opinions, and handle difficult employees professionally by following company policies and seeking help from superiors and HR as needed. The document also suggests asking employees for feedback on the manager's own performance and developing self-development plans to help employees improve their skills and confidence.
Big data | Hadoop | components of hadoop |Rahul Gulab SingRahul Singh
This document provides an overview of big data concepts and the Hadoop framework. It discusses the volume, variety, velocity, and veracity challenges of big data. It introduces Hadoop distributed file system (HDFS) and MapReduce programming model for processing large datasets across clusters of computers. Examples are given of how Google, Facebook, and the New York Times use Hadoop to manage petabytes of data and perform tasks like processing web searches, photos, and converting historical newspaper articles to PDF.
This document discusses database performance tuning. It covers the physical structure of databases, phases of query processing and query plans, indexes and maintenance, monitoring queries, performance issues and resolutions. Specific topics include the purpose of data and log files, separating files, query execution steps, index types (clustered, non-clustered, covering), index maintenance, monitoring tools like Profiler and DMVs, and resolving issues like blocking, deadlocks and high CPU usage. Demos are provided of indexes, monitoring features, and troubleshooting techniques.
A Harvard study found that 85% of people attribute their success to attitude, while only 15% attribute it to expertise. Positive attitudes like anticipating good outcomes, caring for others, confidence, patience, and humility lead to success in relationships, career, health, spirituality, and other endeavors. In contrast, bitterness, resentment, purposelessness, poor health, and high stress are associated with negative influences and not achieving success. The document provides steps to develop a positive attitude, including changing one's focus, developing gratitude, continuous learning, building self-esteem, avoiding negatives, starting the day positively, and keeping goals visible.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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.
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.
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.
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.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
5. Aws Data Centre
(19 geographic Regions
around the world)
Internet
Custo
mer 1
Custo
mer 2
Cust
omer
N
6. Parameter Traditional IT Infra Cloud Solutions(Aws ,
Azure)
Deployment Servers are hosted on
premise
Servers are hosted at Vendor
site
Cost Upfront investment , Have
to pay associated hardware
and IT costs
NO upfront investment , pay
as you use
Security Data security is in the hands
of organization
Data security is in the hands
of Vendor
Implementatio
n
Comparatively very high Comparatively very less
Customization Less comparatively High comparatively
9. IaaS -Infrastructure as a Service
PaaS-Platform as a Service.
SaaS-Software as a Service.
Example: Server , Networking
between server , security etc
Example : Java , .Net etc
Example: Email service , VDI ,
Sqs
12. All App requires Server
Large , Small , Midsize
One , Many , Clusters
Too much analysis
Amazon Clicks
Pre-configure Templates
Using Api configure Servers
On demand pricing
16. Amazon S3 (Simple Storage Service)
Need for storage is growing everyday
Building and maintaining Storage is expensive
Simple Web interface to access , upload ,
download at any time
S3 Standard ,S3 Standard Infrequent access ,
Amazon Glacier , lifecycle management
Flexibility in Identity access control
23. Significant Bandwidth
Dedicated Line
Corporate data Centre AWS Cloud
Direct
Connect (1
gb/sec to
10 gb/sec)
24. Fully managed data warehouse service in the
cloud
Range from 100s of gigabytes to a petabyte.
Seamlessly Integrate with popular toolls
namely BO and Analysis services
No new language to learn
Easy to Manage Data warehouse
1000$ per Year for 1 Tb data
25. Data log and data feed intake
Real-time graphs:
Real-time data analytics:
High Capacity Data Pipeline
Just Feed data and see the patterens using
different types of algorithm
Modelling and Simulation , Dynamic reports
26.
27.
28. Workspaces
WorkSpace is a persistent Windows Server
2008 R2 instance that looks like Windows7
Data backup will be taken on every 12 hours by
default
Client Reconnect, Auto Resume Session,
Choice of devices and applications, Cost-
effective
Virtual Desktop on Aws
29. AWS ─ WorkMail
Building and Maintaing Email Solutions are
costly