Speaker: Shafreen Sayyed, AWS
Level: 200
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. We are seeing more and more organisations shift to a data lake solution. This approach allows you to store massive amounts of data in a central location so its readily available to be categorized, processed, analyzed, and consumed by diverse organizational groups. In this session, we’ll assemble a data lake using services such as Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, AWS Glue and integration with Amazon Redshift Spectrum.
The Open Data Lake Platform Brief - Data Sheets | WhitepaperVasu S
An open data lake platform provides a robust and future-proof data management paradigm to support a wide range of data processing needs, including data exploration, ad-hoc analytics, streaming analytics, and machine learning.
by Mamoon Chowdry, Solutions Architect
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
by Ben Willett, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
by Amy Che, Sr Solutions Delivery Manager AWS and Marie Yap, Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Taz Sayed, Sr Technical Account Manager AWS and Marie Yap, Enterprise Solutions Architect AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
A whitepaper is about Qubole on AWS provides end-to-end data lake services such as AWS infrastructure management, data management, continuous data engineering, analytics, & ML with zero administration
https://www.qubole.com/resources/white-papers/qubole-on-aws
by Brian Mitchell, Principal Data Architect, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
The Open Data Lake Platform Brief - Data Sheets | WhitepaperVasu S
An open data lake platform provides a robust and future-proof data management paradigm to support a wide range of data processing needs, including data exploration, ad-hoc analytics, streaming analytics, and machine learning.
by Mamoon Chowdry, Solutions Architect
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
by Ben Willett, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
by Amy Che, Sr Solutions Delivery Manager AWS and Marie Yap, Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Taz Sayed, Sr Technical Account Manager AWS and Marie Yap, Enterprise Solutions Architect AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
A whitepaper is about Qubole on AWS provides end-to-end data lake services such as AWS infrastructure management, data management, continuous data engineering, analytics, & ML with zero administration
https://www.qubole.com/resources/white-papers/qubole-on-aws
by Brian Mitchell, Principal Data Architect, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
What's New with Amazon Redshift ft. Dow Jones (ANT350-R) - AWS re:Invent 2018Amazon Web Services
Learn about the latest and hottest features of Amazon Redshift. We’ll deep dive into the architecture and inner workings of Amazon Redshift and discuss how the recent availability, performance, and manageability improvements we’ve made can significantly enhance your user experience. We’ll also share glimpse of what we are working on and our plans for the future. Dow Jones will join us to share how they leverage a data lake powered by Redshift, Redshift spectrum and Athena to get fast time to insights.
Data Warehousing with Amazon Redshift: Data Analytics Week at the SF LoftAmazon Web Services
Data Warehousing with Amazon Redshift: Data Analytics Week at the San Francisco Loft
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Sudhir Gupta - Partner Solutions Architect, Redshift Specialist, AWS
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftAmazon Web Services
Osemeke Isibor, Solutions Architect, AWS
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
by Manish Mohite, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Leadership Session: AWS Database and Analytics (DAT206-L) - AWS re:Invent 2018Amazon Web Services
We’re witnessing an unprecedented growth in the amount of data collected and stored in the cloud. Getting insights from this data requires database and analytics services that scale and perform in ways not possible before. AWS offers the broadest set of database and analytics services to process, store, manage, and analyze all your data. In this session, we provide an overview of the database and analytics services at AWS, new services and features we launched this year, how customers are using these services, and our vision for continued innovation in this space.
by Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
by Avijit Goswami, Sr Solutions Architect AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Andre Hass, Specialist Technical Account Manager, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data application including 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.
https://aws.amazon.com/webinars/anz-webinar-series/
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
by Peter Dalton, Principal Consultant AWS and Taz Sayed, Sr Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
What's New with Amazon Redshift ft. Dow Jones (ANT350-R) - AWS re:Invent 2018Amazon Web Services
Learn about the latest and hottest features of Amazon Redshift. We’ll deep dive into the architecture and inner workings of Amazon Redshift and discuss how the recent availability, performance, and manageability improvements we’ve made can significantly enhance your user experience. We’ll also share glimpse of what we are working on and our plans for the future. Dow Jones will join us to share how they leverage a data lake powered by Redshift, Redshift spectrum and Athena to get fast time to insights.
Data Warehousing with Amazon Redshift: Data Analytics Week at the SF LoftAmazon Web Services
Data Warehousing with Amazon Redshift: Data Analytics Week at the San Francisco Loft
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Sudhir Gupta - Partner Solutions Architect, Redshift Specialist, AWS
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftAmazon Web Services
Osemeke Isibor, Solutions Architect, AWS
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
by Manish Mohite, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Leadership Session: AWS Database and Analytics (DAT206-L) - AWS re:Invent 2018Amazon Web Services
We’re witnessing an unprecedented growth in the amount of data collected and stored in the cloud. Getting insights from this data requires database and analytics services that scale and perform in ways not possible before. AWS offers the broadest set of database and analytics services to process, store, manage, and analyze all your data. In this session, we provide an overview of the database and analytics services at AWS, new services and features we launched this year, how customers are using these services, and our vision for continued innovation in this space.
by Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
by Avijit Goswami, Sr Solutions Architect AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Andre Hass, Specialist Technical Account Manager, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data application including 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.
https://aws.amazon.com/webinars/anz-webinar-series/
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
by Peter Dalton, Principal Consultant AWS and Taz Sayed, Sr Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Big data journey to the cloud rohit pujari 5.30.18Cloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Data Lake Implementation: Processing and Querying Data in Place (STG204-R1) -...Amazon Web Services
Flexibility is key when building and scaling a data lake. The analytics solutions you use in the future will almost certainly be different from the ones you use today, and choosing the right storage architecture gives you the agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore best practices for building a data lake in Amazon S3 and Amazon Glacier for leveraging an entire array of AWS, open source, and third-party analytics tools. We explore use cases for traditional analytics tools, including Amazon EMR and AWS Glue, as well as query-in-place tools like Amazon Athena, Amazon Redshift Spectrum, Amazon S3 Select, and Amazon Glacier Select.
The AWS Big Data services are inherently built to run at @scale. In this session, you will learn how to develop an enterprise scale big data application using AWS services such as Amazon EMR, Amazon Redshift & Redshift Spectrum, Amazon Athena, Amazon Elasticsearch Service, Amazon Kinesis, Amazon QuickSight and AWS Glue. This session will also cover different architectural patterns and customer use cases.
Data Warehouses & Data Lakes: Data Analytics Week at the SF LoftAmazon Web Services
Data Warehouses and Data Lakes: Data Analytics Week at the San Francisco Loft
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Asim Kumar Sasmal - Big Data Consultant, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Saunak Chandra - Partner Solutions Architect, Redshift Specialist, AWS
Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss 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 architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Your data has value for multiple business functions in your organization. Shorten your time to analytics and take faster, better decisions based on data.
In this session you will learn how you can access your data from a myriad of tools such as multiple EMR clusters, Athena & Redshift.
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 AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.
Database Freedom. Database migration approaches to get to the Cloud - Marcus ...Amazon Web Services
Databases are at the heart of many of the software systems we build. Learn how to achieve the benefits of moving your core datastores to the AWS platform, by designing a migration model that retires tech debt and sets the platform for innovation. In this session we'll cover the different database options available on AWS, how you can start migrating your databases to the cloud, and cover tools like the AWS Database Migration Service (DMS) to largely automate this work for you.
Migrating to 21st Century Analytics: Zopa Story
Speakers:
Shafreen Sayyed, Solution Architect, AWS
Varun Gangoor, Senior Big Data Engineer, Zopa
Data makes the world go around these days, and 21st Century Data Analytics means you can store, process and analyze massive amounts of data, often in real time, whilst making that data consumable across diverse groups in your organization. Many traditional tools lock data away in inflexible silos, making this impossible. This session will look at what is needed in a Financial Services organization to achieve a flexible and scalable data architecture, and we will also hear from Zopa, UK's first peer-to-peer lending company, about how they migrated their data analytics estate to AWS and look at what new insight that has given them.
Data Analytics Week at the San Francisco Loft
Data Warehouses and Data Lakes
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Speakers:
John Mallory - Principal Business Development Manager, Storage, AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Using AWS Purpose-Built Databases to Modernize your ApplicationsAmazon Web Services
As you look to modernizing your applications, you will need to consider your database options to meet the new application requirements. AWS offers a series of purpose-built databases that include relational, key value, document, graph and cache use cases to help you deliver new and enhanced functionalities. In this webinar session, we share the different modern application architectures, and how to combine different database services to meet your requirements. Understand how to modernize your relational databases through easy upgrades with Amazon Relational Database Service and learn how to migrate from one database to another with AWS Database Migration Service and AWS Schema Conversion Tool.
Speaker:
Blair Layton, Business Development Manager, Amazon Web Services
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumAmazon Web Services
We will walk through how to migrate and modernise your legacy data warehouse, moving from an on-premises server or application, to the cloud. You will learn how to easily migrate your data by leveraging serverless ETL, data cataloging as well as the techniques needed to successfully modernise your data warehouse, reduce costs, and increase performance and scalability.
Speaker: Paul Macey, Specialist Solutions Architect, AWS
BDA308 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more. In this session you learn how to configure a secure, petabyte-scale Amazon Elasticsearch Service cluster and build Kibana dashboards to analyze your data. In addition, we discuss best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations.
From raw data to business insights. A modern data lakejavier ramirez
In this talk I spoke about the pitfalls when you try to build a data lake, and how you can solve the problem either with unmanaged open source, or with the managed and/or native solutions at AWS. Delivered at the Madrid Data Engineering meetup in May 2019
Similar to Using data lakes to quench your analytics fire - AWS Summit Cape Town 2018 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.