This document discusses building a data lake on AWS. It notes that organizations that successfully generate value from data will outperform competitors. It outlines challenges of data visibility, multiple access mechanisms, and analyzers needing access. AWS is presented as the perfect solution with its storage, analysis and security capabilities at scale. Case studies of Celgene and IEP are presented that used AWS for their data lakes. Traditional analytics are separated from data warehousing, but data lakes extend this by including diverse data and analytical engines at larger scale with lower costs. The AWS portfolio for data lakes, analytics and IoT is presented as the most complete toolset. Building value from the data lake is discussed through machine learning, analytics, data movement and visualization.
The document discusses building data lakes with AWS. It recommends using Amazon S3 as the storage layer for the data lake due to its scalability, durability and integration with other AWS analytics services. It also recommends using AWS Glue to catalog and ingest data into the data lake through automated crawlers. This allows for easy discovery, querying and analysis of data in the lake.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Apache Spark is a fast and general engine for large-scale data processing. It was created by UC Berkeley and is now the dominant framework in big data. Spark can run programs over 100x faster than Hadoop in memory, or more than 10x faster on disk. It supports Scala, Java, Python, and R. Databricks provides a Spark platform on Azure that is optimized for performance and integrates tightly with other Azure services. Key benefits of Databricks on Azure include security, ease of use, data access, high performance, and the ability to solve complex analytics problems.
ABD318_Architecting a data lake with Amazon S3, Amazon Kinesis, AWS Glue and ...Amazon Web Services
"Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users - from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways.
In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users."
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
This document provides an overview of AWS Lake Formation and related services for building a secure data lake. It discusses how Lake Formation provides a centralized management layer for data ingestion, cleaning, security and access. It also describes how Lake Formation integrates with services like AWS Glue, Amazon S3 and ML transforms to simplify and automate many data lake tasks. Finally, it provides an example workflow for using Lake Formation to deduplicate data from various sources and grant secure access for analysis.
The document discusses building data lakes with AWS. It recommends using Amazon S3 as the storage layer for the data lake due to its scalability, durability and integration with other AWS analytics services. It also recommends using AWS Glue to catalog and ingest data into the data lake through automated crawlers. This allows for easy discovery, querying and analysis of data in the lake.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Apache Spark is a fast and general engine for large-scale data processing. It was created by UC Berkeley and is now the dominant framework in big data. Spark can run programs over 100x faster than Hadoop in memory, or more than 10x faster on disk. It supports Scala, Java, Python, and R. Databricks provides a Spark platform on Azure that is optimized for performance and integrates tightly with other Azure services. Key benefits of Databricks on Azure include security, ease of use, data access, high performance, and the ability to solve complex analytics problems.
ABD318_Architecting a data lake with Amazon S3, Amazon Kinesis, AWS Glue and ...Amazon Web Services
"Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users - from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways.
In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users."
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
This document provides an overview of AWS Lake Formation and related services for building a secure data lake. It discusses how Lake Formation provides a centralized management layer for data ingestion, cleaning, security and access. It also describes how Lake Formation integrates with services like AWS Glue, Amazon S3 and ML transforms to simplify and automate many data lake tasks. Finally, it provides an example workflow for using Lake Formation to deduplicate data from various sources and grant secure access for analysis.
The document discusses building a data lake on AWS. It describes various AWS services that can be used to ingest, store, transform, analyze and visualize data in the data lake. These services include Amazon S3 for storage, AWS Glue for ETL/data cataloging, AWS Lake Formation for governance, Amazon Athena/EMR for analytics and Amazon QuickSight for visualization. The document also covers data movement options from on-premises to the data lake and real-time streaming of data using services like Kinesis. Machine learning workloads can leverage Amazon SageMaker for training and deployment.
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
This document discusses Apache Spark, a fast and general engine for big data processing. It describes how Spark generalizes the MapReduce model through its Resilient Distributed Datasets (RDDs) abstraction, which allows efficient sharing of data across parallel operations. This unified approach allows Spark to support multiple types of processing, like SQL queries, streaming, and machine learning, within a single framework. The document also outlines ongoing developments like Spark SQL and improved machine learning capabilities.
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Speakers:
Steve Abraham - Principal Database Specialist Solutions Architect, AWS
Peter Dachnowicz - Sr. Technical Account Manager, AWS
AWS Glue는 고객이 분석을 위해 손쉽게 데이터를 준비하고 로드할 수 있게 지원하는 완전관리형 ETL(추출, 변환 및 로드) 서비스입니다. AWS 관리 콘솔에서 클릭 몇 번으로 ETL 작업을 생성하고 실행할 수 있습니다. 빅데이터 분석 시 다양한 데이터 소스에 대한 전처리 작업을 할 때, 별도의 데이터 처리용 서버나 인프라를 관리할 필요가 없습니다. 본 세션에서는 지난 5월 서울 리전에 출시한 Glue 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
Power BI for Big Data and the New Look of Big Data SolutionsJames Serra
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
Data saturday Oslo Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
Learning Objectives:
- Learn the common use-cases for using Athena, AWS' interactive query service on S3
- Learn best practices for creating tables and partitions and performance optimizations
- Learn how Athena handles security, authorization, and authentication
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Amazon SageMaker는 머신러닝 프로젝트를 위한 통합 플랫폼입니다. SageMaker의 기능 중 Amazon SageMaker Studio는 머신러닝 통합 개발환경을 제공하여, 데이터를 준비에서부터 모델을 빌드, 교육 및 배포하는 데 필요한 모든 단계를 수행할 수 있습니다. Amazon EMR은 Apache Spark, Apache Hive 및 Presto와 같은 오픈 소스 분석 프레임워크를 사용하여 대규모 분산 데이터 처리 작업, 대화형 SQL 쿼리 및 ML 애플리케이션을 실행하기 위한 빅 데이터 플랫폼입니다. 이 세션에서는 데이터 과학자와 ML 엔지니어가 ML 워크플로우에서 분산 빅 데이터 프레임워크를 쉽게 사용할 수 있도록 상호 서비스 간의 통합에 대하여 데모를 통해 알아봅니다.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017Amazon Web Services
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Demystify Streaming on AWS - 발표자: 이종혁, Sr Analytics Specialist, WWSO, AWS :::...Amazon Web Services Korea
실시간 분석은 AWS 고객의 사용 사례가 점점 늘어나고 있습니다. 이 세션에 참여하여 스트리밍 데이터 기술이 어떻게 데이터를 즉시 분석하고, 시스템 간에 데이터를 실시간으로 이동하고, 실행 가능한 통찰력을 더 빠르게 얻을 수 있는지 알아보십시오. 일반적인 스트리밍 데이터 사용 사례, 비즈니스에서 실시간 분석을 쉽게 활성화하는 단계, AWS가 Amazon Kinesis와 같은 AWS 스트리밍 데이터 서비스를 사용하도록 지원하는 방법을 다룹니다.
Many serverless applications need a way to manage end user identities and support sign-ups and sign-ins. Join this session to learn real-world design patterns for implementing authentication and authorization for your serverless application—such as how to integrate with social identity providers (such as Google and Facebook) and existing corporate directories. We cover how to use Amazon Cognito identity pools and user pools with API Gateway, Lambda, and IAM.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
Leveraging Cloud Analytics to Support Data-Driven DecisionsAmazon Web Services
Learn about AWS business intelligence (BI) analytics, visualization, artificial intelligence, and machine learning services that can transform data into insights.
The document discusses building a data lake on AWS. It describes various AWS services that can be used to ingest, store, transform, analyze and visualize data in the data lake. These services include Amazon S3 for storage, AWS Glue for ETL/data cataloging, AWS Lake Formation for governance, Amazon Athena/EMR for analytics and Amazon QuickSight for visualization. The document also covers data movement options from on-premises to the data lake and real-time streaming of data using services like Kinesis. Machine learning workloads can leverage Amazon SageMaker for training and deployment.
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
This document discusses Apache Spark, a fast and general engine for big data processing. It describes how Spark generalizes the MapReduce model through its Resilient Distributed Datasets (RDDs) abstraction, which allows efficient sharing of data across parallel operations. This unified approach allows Spark to support multiple types of processing, like SQL queries, streaming, and machine learning, within a single framework. The document also outlines ongoing developments like Spark SQL and improved machine learning capabilities.
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Speakers:
Steve Abraham - Principal Database Specialist Solutions Architect, AWS
Peter Dachnowicz - Sr. Technical Account Manager, AWS
AWS Glue는 고객이 분석을 위해 손쉽게 데이터를 준비하고 로드할 수 있게 지원하는 완전관리형 ETL(추출, 변환 및 로드) 서비스입니다. AWS 관리 콘솔에서 클릭 몇 번으로 ETL 작업을 생성하고 실행할 수 있습니다. 빅데이터 분석 시 다양한 데이터 소스에 대한 전처리 작업을 할 때, 별도의 데이터 처리용 서버나 인프라를 관리할 필요가 없습니다. 본 세션에서는 지난 5월 서울 리전에 출시한 Glue 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
Power BI for Big Data and the New Look of Big Data SolutionsJames Serra
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
Data saturday Oslo Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
Learning Objectives:
- Learn the common use-cases for using Athena, AWS' interactive query service on S3
- Learn best practices for creating tables and partitions and performance optimizations
- Learn how Athena handles security, authorization, and authentication
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Amazon SageMaker는 머신러닝 프로젝트를 위한 통합 플랫폼입니다. SageMaker의 기능 중 Amazon SageMaker Studio는 머신러닝 통합 개발환경을 제공하여, 데이터를 준비에서부터 모델을 빌드, 교육 및 배포하는 데 필요한 모든 단계를 수행할 수 있습니다. Amazon EMR은 Apache Spark, Apache Hive 및 Presto와 같은 오픈 소스 분석 프레임워크를 사용하여 대규모 분산 데이터 처리 작업, 대화형 SQL 쿼리 및 ML 애플리케이션을 실행하기 위한 빅 데이터 플랫폼입니다. 이 세션에서는 데이터 과학자와 ML 엔지니어가 ML 워크플로우에서 분산 빅 데이터 프레임워크를 쉽게 사용할 수 있도록 상호 서비스 간의 통합에 대하여 데모를 통해 알아봅니다.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017Amazon Web Services
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Demystify Streaming on AWS - 발표자: 이종혁, Sr Analytics Specialist, WWSO, AWS :::...Amazon Web Services Korea
실시간 분석은 AWS 고객의 사용 사례가 점점 늘어나고 있습니다. 이 세션에 참여하여 스트리밍 데이터 기술이 어떻게 데이터를 즉시 분석하고, 시스템 간에 데이터를 실시간으로 이동하고, 실행 가능한 통찰력을 더 빠르게 얻을 수 있는지 알아보십시오. 일반적인 스트리밍 데이터 사용 사례, 비즈니스에서 실시간 분석을 쉽게 활성화하는 단계, AWS가 Amazon Kinesis와 같은 AWS 스트리밍 데이터 서비스를 사용하도록 지원하는 방법을 다룹니다.
Many serverless applications need a way to manage end user identities and support sign-ups and sign-ins. Join this session to learn real-world design patterns for implementing authentication and authorization for your serverless application—such as how to integrate with social identity providers (such as Google and Facebook) and existing corporate directories. We cover how to use Amazon Cognito identity pools and user pools with API Gateway, Lambda, and IAM.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
Leveraging Cloud Analytics to Support Data-Driven DecisionsAmazon Web Services
Learn about AWS business intelligence (BI) analytics, visualization, artificial intelligence, and machine learning services that can transform data into insights.
The document discusses building data lakes on AWS. It describes how data lakes extend the traditional data warehouse approach by allowing storage of both structured and unstructured data at massive scales. Amazon S3 provides durable, available, scalable, and easy-to-use storage for the data lake. AWS Glue crawls data to create a data catalog and can automate ETL processes. Amazon Athena and Amazon EMR enable interactive analysis and big data processing through SQL and Spark. The data lake architecture on AWS supports a variety of analytical use cases.
The document discusses big data and machine learning solutions on AWS. It covers why organizations use big data, challenges they face, and how AWS solutions like S3 data lakes, Glue, Athena, Redshift, Kinesis, Elasticsearch, SageMaker, and QuickSight can help overcome these challenges. It also discusses how big data drives machine learning and how AWS machine learning services work. Core tenets discussed include building decoupled systems, using the right tool for the job, and leveraging serverless services.
This document discusses how organizations can leverage big data and artificial intelligence (AI) to drive insights and add intelligence to their solutions. It covers common big data challenges, AWS big data solutions like Amazon S3, Glue, Athena, Redshift, Kinesis, and SageMaker, and how big data can power machine learning. Some key tenets for building big data architectures are using the right tools, leveraging managed services, adopting event-driven design patterns, and enabling ML applications.
AWS tiene un conjunto amplio y específico de servicios AI / ML para su negocio. Aprenda cómo crear, entrenar e implementar modelos de Machine Learning rápidamente o agregar inteligencia a sus aplicaciones de manera sencilla.
Sviluppa, addestra e distribuisci modelli di machine learning.pdfAmazon Web Services
The document discusses Amazon SageMaker, an AWS managed service for building, training, and deploying machine learning models. It provides an overview of the key capabilities of SageMaker such as using built-in algorithms, bringing your own algorithms/containers, hyperparameter tuning, hosting models for inference, and batch transforms. It also discusses how SageMaker integrates with other AWS services like S3, EC2, and Marketplace.
Big Data Meets Machine Learning: Architecting Spark Environment for Data Scie...Amazon Web Services
In this code-level session, we show you how to integrate your Apache Spark application with Amazon SageMaker. We'll include details on how networking, architecture, and code execute across the components of the solution. We will also dive deep into how to perform data exploration and feature engineering on the Spark cluster, starting training jobs from Spark, integrating training jobs in Spark pipelines, and more. Amazon SageMaker, our fully managed machine learning platform, comes with pre-built algorithms and popular deep learning frameworks. Amazon SageMaker also includes an Apache Spark library that you can use to easily train models from your Spark clusters.
Machine Learning with Kubernetes- AWS Container Day 2019 BarcelonaAmazon Web Services
This document discusses machine learning workflows using Kubernetes and KubeFlow. It begins by explaining why Kubernetes is useful for machine learning for its composability, portability, and scalability. It then discusses using Jupyter notebooks for machine learning development workflows. It provides an overview of running machine learning on Kubernetes both with and without KubeFlow. Key components of KubeFlow like Kubeflow Pipelines are explained. Advantages of using KubeFlow on AWS are highlighted. TensorFlow and how AWS optimizes it is covered. The document concludes by discussing characteristics of autonomous vehicle workloads and how Kubeflow on AWS can help.
AWS Data-Driven Insights Learning Series_ANZ Sep 2019 Part 2Amazon Web Services
AWS has been supporting companies across Australia and New Zealand to put their most innovative tools and technologies to work to achieve their business needs and goals. AWS and our ecosystem of partners has helped the likes of CP Mining, IntelliHQ, WesCEF, Oz Minerals, Woodside and many more to modernise their analytics and data architecture in order to successfully generate business value from their data.
This event series aimed to educate customers with a broader understanding of how to build next-gen data lakes and analytics platforms and make connections with AWS.
Connecting the dots - How Amazon Neptune and Graph Databases can transform yo...Amazon Web Services
This document discusses Amazon Neptune, a fully managed graph database service. It provides an overview of graph databases and their advantages over traditional databases for modeling connected data. It then describes Amazon Neptune's key features, like automatic scaling, high availability across Availability Zones, integration with open standards like Gremlin and SPARQL, and ease of use on AWS. Examples are given showing how to model and query graph data using Gremlin and SPARQL. Finally, it discusses Amazon Neptune's architecture and roadmap for general availability later in 2018.
This document discusses how big data and artificial intelligence (AI) can drive insights and add intelligence to solutions. It covers common big data challenges, AWS big data solutions like data lakes, data cataloging, extract-transform-load (ETL) tools, and analytics services. It also discusses how big data can enable machine learning by providing large datasets for model training. Key takeaways include using the right tools for different analytics jobs, leveraging managed services, and focusing on extracting business value from data.
Learn about data lifecycle best practices in the AWS Cloud, so you can optimize performance and lower the costs of data ingestion, staging, storage, cleansing, analytics and visualization, and archiving.
FINRA's Managed Data Lake: Next-Gen Analytics in the Cloud - ENT328 - re:Inve...Amazon Web Services
FINRA faced challenges with their on-premises data infrastructure, including difficulty tracking data, limited scalability, and high costs. They migrated to a managed data lake on AWS to address these issues. This provided centralized data management with a catalog, separation of storage and compute, encryption, and cost optimization. It enabled faster analytics through Presto querying, machine learning model development, and reduced TCO by 30% compared to their on-premises environment. Lessons learned included embracing disruption, automating infrastructure, and treating infrastructure as code. FINRA is exploring additional AWS services like Athena, Lambda, and Step Functions to continue improving their analytics capabilities.
The document discusses mapping biomass in the Amazon forest using Amazon analytics services. It describes how biomass maps are traditionally created using satellite data and LiDAR flights, but the process is computationally intensive. The author aims to generate thousands of maps to capture uncertainty using random forest algorithms, but notes their traditional methods cannot scale to that level. They are exploring using Amazon analytics services to generate the large number of maps needed.
Value of Data Beyond Analytics by Darin BriskmanSameer Kenkare
The document discusses analytics capabilities provided by Amazon Web Services (AWS). It describes how AWS offers a variety of services for building data lakes, loading and querying data, and performing analytics. These services include Amazon S3, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon QuickSight. It also provides examples of how customers like Epic Games and a large media company use these AWS analytics services.
The document discusses Amazon SageMaker, a fully managed service that allows users to build, train, and deploy machine learning models at scale. It provides an overview of SageMaker's key features like notebooks for preprocessing data and building models, built-in algorithms for common tasks, one-click training of models, hyperparameter tuning, and deployment of trained models onto managed hosting infrastructure. SageMaker aims to make machine learning accessible to every developer by handling the complexities of training and deploying models.
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)Julien SIMON
The document discusses Amazon Web Services (AWS) machine learning capabilities including frameworks like TensorFlow and MXNet. It highlights how AWS provides optimized infrastructure for deep learning workloads and services like Amazon SageMaker for building, training and deploying machine learning models at scale.
See how Public Sector organisations and AWS Partners are leveraging Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. By integrating learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Observe how to architect these solutions using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexas
Speaker: Craig Lawton, Smart Australia & IoT Specialist, AWS
O documento discute opções para Disaster Recovery na nuvem AWS, incluindo Backup e Restore, Pilot Light, Warm Standby e Multi-Site. A AWS oferece várias soluções para atender a diferentes requisitos de RTO e RPO a um custo variável. A nuvem permite testes fáceis e dimensionamento flexível dos recursos de recuperação de desastres.
O documento discute opções para Disaster Recovery na nuvem AWS, incluindo Backup e Restore, Pilot Light, Warm Standby e Multi-Site. A AWS oferece várias soluções para atender a diferentes requisitos de RTO e RPO a um custo variável. A nuvem permite testes fáceis e dimensionamento flexível dos recursos de recuperação de desastres.
O documento descreve várias soluções de segurança da nuvem da AWS, incluindo ferramentas para gestão de acessos e identidade, detecção, segurança de infraestrutura, resposta a incidentes e proteção de dados. A AWS oferece 203 certificações de segurança e mais de 2.600 controles auditados anualmente para ajudar clientes a manterem a conformidade e segurança na nuvem.
En este webinar, aprenderá cómo las empresas pueden aprovechar la nube de AWS para automatizar los pipelines de desarrollo de software. Este enfoque permite que su equipo sea más ágil, mejorando su capacidad para entregar aplicaciones y servicios rápidamente.
Neste webinar, você aprenderá como as empresas podem se valer da nuvem da AWS para automatizar os pipelines de desenvolvimento de software. Essa abordagem permite que sua equipe seja mais ágil, melhorando sua capacidade para entregar aplicações e serviços mais rapidamente.
Las tecnologías como los contenedores y kubernetes pueden hacer que sus procesos de entrega de software sean más fáciles y más rápidos. En este webinar, hablaremos sobre cómo usar el Amazon Kubernetes Service (EKS) para construir aplicaciones modernas con grupos Kubernetes totalmente administrados.
Tecnologias como containers e Kubernetes podem tornar seus processos de entrega de software mais fáceis e rápidos. Neste webinar, falaremos sobre como usar o Amazon Elastic Kubernetes Service (EKS) para criar aplicativos modernos com clusters de Kubernetes totalmente gerenciados.
Ransomware é uma das ameaças de crescimento mais rápido para qualquer organização. Nenhuma empresa, grande ou pequena, está imune a ataques de cibercriminosos. Nesta sessão, mostramos como você pode aproveitar os serviços e recursos da nuvem AWS para proteger seus dados mais valiosos de ataques cibernéticos e acelerar a restauração de operações.
El ransomware es una de las amenazas de más rápido crecimiento para cualquier organización. Ninguna empresa, grande o pequeña, es inmune a los ataques de los ciberdelincuentes. En esta sesión, mostramos cómo puede aprovechar los servicios y las capacidades de la nube AWS para proteger sus datos más valiosos de los ataques cibernéticos y acelerar la restauración de las operaciones.
Ransomware é uma prática maliciosa que tem se popularizado nos últimos anos. Nessa sessão, mostraremos como através da Amazon Web Services nossos clientes podem desenvolver uma estratégia pró-ativa de mitigação a ataques de ransomware, tanto em cenários on-premises como operando na nuvem.
El ransomware es una práctica maliciosa que se ha popularizado en los últimos años. En esta sesión les mostraremos cómo desde Amazon Web Services nuestros clientes pueden desarrollar una estrategia proactiva de mitigación frente a ataques de ransomware, tanto en escenarios on-premises, como operando en la nube.
Al mover datos a la nube, los clientes deben comprender los métodos óptimos para los diferentes casos de uso, los tipos de datos que están moviendo y los recursos disponibles en la red, entre otros. Las soluciones de migración y transferencia de AWS contemplan desde la migración de datos con conectividad limitada, almacenamiento en la nube híbrida, transferencias frecuentes de archivos B2B, hasta transferencias de datos en línea y sin conexión. En esta sesión, le mostramos cómo puede acelerar la migración y transferencia de datos de manera simplificada desde y hacia la nube de AWS.
O documento discute estratégias para migração de dados para a AWS, incluindo serviços como AWS Transfer Family para transferência de arquivos, AWS DataSync para mover dados entre ambientes on-premises e AWS, e AWS Snow Family para transferência offline de grandes quantidades de dados.
El almacenamiento de archivos tiene diversos casos de uso; como directorios de usuarios, datos de aplicaciones, archivos multimedia y almacenamiento compartido para cargas de trabajo de alto rendimiento. La administración del almacenamiento de archivos en instalaciones propias suele ser un trabajo pesado, indiferenciado, con altos costos de adquisición, carga operativa para configurar y administra, lo que conlleva a desafíos de escalabilidad. En esta sesión, le mostramos cómo puede aprovechar las soluciones de archivos totalmente administradas de AWS para dejar de preocuparse por la sobrecarga administrativa de configurar, proteger, mantener y realizar copias de seguridad de su infraestructura de archivos.
La visualización de datos analíticos es un reto al que se enfrentan muchas organizaciones, el poder crear tableros, alertas, agregar predicciones a sus datos y actuar de acuerdo a estas de manera rápida es una necesidad de todos los negocios actuales. Únase a nuestros arquitectos para aprender como Amazon QuickSight le permite agregar inteligencia de negocios a sus aplicaciones y crear predicciones a futuro de sus datos. Amazon QuickSight es un servicio de inteligencia de negocios escalable y serverless creado para la nube, a través del cual podrá explotar sus datos de negocio para convertirlos en insights para hacer decisiones informadas sobre su negocio sin preocuparse de la gestión, escalamiento y la disponibilidad de la infraestructura de cómputo.
A visualização de dados é um desafio que muitas organizações enfrentam hoje. Criar dashboards, alertas, fazer previsões e agir rapidamente de acordo com os insights dos dados é uma necessidade de todas as empresas. Junte-se aos nossos arquitetos para aprender como o Amazon QuickSight o ajudará a adicionar BI aos seus aplicativos. O Amazon Quicksight é um serviço de BI escalável e serverless criado para a nuvem. Com ele, você pode explorar seus dados para obter insights e tomar decisões embasadas em seus negócios, sem se preocupar em gerenciar e dimensionar servidores e manter a disponibilidade de sua infraestrutura.
1) O documento discute os benefícios de migrar workloads de Big Data para a AWS, incluindo tornar mais fácil construir data lakes e analytics, oferecer maior abrangência de serviços e fornecer infraestrutura mais segura e escalável.
2) É apresentada a plataforma Amazon EMR para executar aplicativos de Big Data de forma gerenciada na AWS, proporcionando melhor desempenho a menor custo em comparação a clusters on-premises.
3) A separação de computação e armazenamento no Amazon EMR permite
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
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
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
6. “Escolhemos a AWS pela liderança. São
pioneiros e hoje não existe nada melhor no
mercado. Além disso, a AWS tem na sua
estrutura todos os dados do projeto 1000
Genomes, o que facilita o acesso e reduz o
tempo de processamento”
Dr. Pedro Galante
Pesquisador do IEP
https://aws.amazon.com/pt/solutions/case-studies/sirio-libanes/
27. “The pace of change in healthcare is moving
extremally fast. AWS is going to help us achieve the
scale that we need to be able to deliver innovations
to our clients”
David Cohen
VP do Departamento
de Inteligência
HealtheIntent
Uma pesquisa da Alberdeen observou que organizações que implementam data lakes se sobressaem a companhias similares em 9% do crescimento orgânico do seu faturamento
1. Agility – Fail fast, and try more before going all-in with a Big Data solution.
2. Broadest & Deepest Capabilities – Build or support virtually any Big Data workload regardless of volume, velocity, and variety of data. AWS offers deep and rapidly expanding functionality for: data warehousing, distributed analytics (supporting Hadoop, Spark, HBase, Hive, Pig and Yarn), Machine Learning and Business Intelligence.
3. Computational Power Second to None – AWS offers twice as many as any other cloud provider– each EC2 instance is optimized for CPU, memory, storage and networking capacity to satisfy the computational requirements of any big data use cases.
4. Low-Cost Analytics - Now petabyte-scale analytics are affordable for everyone. Big Data storage is as low as $28.16/TB; data archiving as low as $0.007/GB/month; data warehousing and BI is 1/10th the cost of traditional enterprise software solutions; real-time streaming data loads for only $0.35/GB; managed Hadoop, Spark, Presto clusters for as little as $0.15 per hour.
5. Trusted & Secure – AWS environments are continuously audited and certified for compliance with 20+ standards: HIPAA, FedRAMP, CESG, and more. AWS offers efficient and scalable encryption for data at rest and in transit, Key Management Services, and Cloud HSM so customers always have 100% control over which country their data resides in.
6. Data Migrations Made Easy - AWS makes data migration fast, low cost, secure and easy with Amazon S3 Transfer Acceleration (a simple web API that you can call directly to load data and improve data upload speeds by 300%), Amazon Kinesis Firehose for streaming data, AWS Snowball Import/Export appliances (100TB data migrations in 1 day vs. 100s of days), Direct Connect (a dedicated private network connection for low-latency connectivity to the cloud) and AWS Database Migration Service to ensure zero downtime.
7. Largest Partner Ecosystem - AWS offers ISVs and integrators across the data management stack, and a catalog of 290 AWS Marketplace products pre-integrated with the AWS Cloud.o not have to guess capacity needs and can support high velocity use cases.
Farmaceutica
In order to help customers with their data management strategy, we’ve developed solutions to help store, protect, and optimize healthcare data. In doing so, your data can be centralized and downstream use cases are unlocked. Celgene, for example, deployed their data lake on AWS to drive analytics across their global business units.
[If this resonates with customer, refer to SPO industry solution as followup]
Open Data -> TCGA, ICGC
“cada indivíduo tem cerca de 3 GB de informações de DNA, o que demanda muita capacidade de processamento para encontrar pequenas diferenças dentro dessa enorme quantidade de informações.”
Na área de oncologia, por exemplo, já não são raros os pedidos de sequenciamento de parte do DNA de pacientes com o objetivo de entender a origem de determinados tumores ou predizer a resposta a um determinado medicamento. Quando esse trabalho é feito, as informações podem, em caso de consentimento do paciente, ficar disponíveis em bancos de dados abertos a toda a comunidade médica para consulta e benchmark.
You can run crawlers on a schedule, on-demand, or trigger them based on an event to ensure that your metadata is up-to-date.
Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results.
For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.
The Philips HealthSuite digital platform analyzes and stores 15 PB of patient data gathered from 390 million imaging studies, medical records, and patient inputs to provide healthcare providers with actionable data, which they can use to directly impact patient care. Running on AWS provides the reliability, performance and scalability that Philips needs to help protect patient data as its global digital platform grows at the rate of one petabyte per month.
Eles pretendem crescer 1 PB/mês
Eles carregaram 35 milhões de registros no Redshift em 90 minutos, 870 vezes melhor que a performance da solução anterior on premises. Em menos de 1 dia eles estavam com o ambiente montado
AI/ML
Big Data
Dados de 150 milhões de pessoas, 10PB, 1700 nós de processamento,
HealtheIntent é um population health management platform aggregates longitudinal healthcare data que permite que provedores gerenciem populações e a saúde da comunidade
Healthy Data Lab
Cerner uses AWS and big data to gain actionable, real-time insights, simplifying healthcare delivery while reducing costs for payers, providers, and patients. Cerner, one of the leading suppliers of health information technology (HIT) solutions, chose AWS for its global reach and breadth of services, including machine learning and artificial intelligence.
Porque Cerner escolheu a AWS
Dados de 150 milhões de pessoas, 10PB, 1700 nós de processamento,