AWS provides a broad portfolio of databases and analytics services including data lakes, data movement services, non-relational databases like DynamoDB and ElastiCache, relational databases like RDS and Aurora, analytics services like Redshift and EMR, and machine learning services like SageMaker and Comprehend. These services are purpose-built to help customers build applications and analyze data.
Short overview of AWS Database and Analytics offerings and an introduction of the day’s topics.
Speaker: Bill Baldwin - Database Technical Evangelist, AWS
Big Data and Analytics on Amazon Web Services: Building A Business-Friendly P...Amazon Web Services
If you are crafting a better customer experience, automating your business, or modernizing your systems, you are likely finding that your data and analytics platform is absolutely critical to your success. In this session, we will look at how customers are building on the managed services from Amazon Web Services to meet the needs of the business. Patterns we see gaining popularity are near-real time engagement with customers over mobile, also combining and analyzing unstructured consumer behavior with structured transactional data, as well as managing spiky data workloads. See how our customers use our managed, elastic, secure, and highly available services to change what is possible.
Big Data on EC2: Mashing Technology in the CloudGeorge Ang
This document discusses how a startup serving widgets on popular online publications scaled their infrastructure using Amazon Web Services to handle spikes in traffic from over 1 billion users sharing over 10 billion URLs. They used a hub-and-spoke architecture with components like Cascading, Amazon Elastic MapReduce, and AsterData to analyze user sharing patterns in a cost-effective and horizontally scalable way.
This document discusses cloud computing and Amazon Web Services (AWS). It begins with an introduction to cloud computing, describing it as accessing and delivering services over the internet, such as storage, computing, and software. It then covers the benefits of cloud computing, including scalability, monitoring, and reduced time to market. The document discusses various cloud services models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It also covers cloud deployment models such as public, private, hybrid and community clouds. The remainder focuses on AWS services and provides an overview of compute, storage, database, deployment and security services along with examples of using Lambda for serverless applications
In the next five years, 15 to 40 billion additional connected devices are expected to hit the market. How can we handle such volumes and velocity of data?
Introduction to Dynamo storage systems, Riak, Cassandra, time series databases and edge analytics.
AWS provides a broad portfolio of databases and analytics services including data lakes, data movement services, non-relational databases like DynamoDB and ElastiCache, relational databases like RDS and Aurora, analytics services like Redshift and EMR, and machine learning services like SageMaker and Comprehend. These services are purpose-built to help customers build applications and analyze data.
Short overview of AWS Database and Analytics offerings and an introduction of the day’s topics.
Speaker: Bill Baldwin - Database Technical Evangelist, AWS
Big Data and Analytics on Amazon Web Services: Building A Business-Friendly P...Amazon Web Services
If you are crafting a better customer experience, automating your business, or modernizing your systems, you are likely finding that your data and analytics platform is absolutely critical to your success. In this session, we will look at how customers are building on the managed services from Amazon Web Services to meet the needs of the business. Patterns we see gaining popularity are near-real time engagement with customers over mobile, also combining and analyzing unstructured consumer behavior with structured transactional data, as well as managing spiky data workloads. See how our customers use our managed, elastic, secure, and highly available services to change what is possible.
Big Data on EC2: Mashing Technology in the CloudGeorge Ang
This document discusses how a startup serving widgets on popular online publications scaled their infrastructure using Amazon Web Services to handle spikes in traffic from over 1 billion users sharing over 10 billion URLs. They used a hub-and-spoke architecture with components like Cascading, Amazon Elastic MapReduce, and AsterData to analyze user sharing patterns in a cost-effective and horizontally scalable way.
This document discusses cloud computing and Amazon Web Services (AWS). It begins with an introduction to cloud computing, describing it as accessing and delivering services over the internet, such as storage, computing, and software. It then covers the benefits of cloud computing, including scalability, monitoring, and reduced time to market. The document discusses various cloud services models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It also covers cloud deployment models such as public, private, hybrid and community clouds. The remainder focuses on AWS services and provides an overview of compute, storage, database, deployment and security services along with examples of using Lambda for serverless applications
In the next five years, 15 to 40 billion additional connected devices are expected to hit the market. How can we handle such volumes and velocity of data?
Introduction to Dynamo storage systems, Riak, Cassandra, time series databases and edge analytics.
Building a real-time, scalable and intelligent programmatic ad buying platformJampp
After a brief introduction to programmatic ads and RTB we go through the evolution of Jampp's data platform to handle the enormous about of data we need to process.
CTX, a large digital asset exchange, needed to deploy their application to AWS in a highly available, scalable, and compliant environment. Powerup helped provision the infrastructure using CloudFormation according to the AWS Well-Architected Framework and compliance specifications, which automated the infrastructure and application deployment. This resulted in cost savings of 60-70% through the use of spot instances and a secure environment with continuous integration and delivery pipelines. The solution surpassed CTX's expectations by providing scalability and enhanced DevOps and security capabilities.
High availability, real-time and scalable architecturesJampp
Presented at the Architecture Conference (ArqConf) in Buenos Aires, Argentina. Here is a 10,000ft view of our Real Time Bidding and Stream Processing architecture.
Processing 19 billion messages in real time and NOT dying in the processJampp
Here is an introduction in the Jampp architecture for data processing. We walk through our journey of migrating to systems that allows us to process more data in real time
Rob Rastovich of Appirio presents "Migrating Enterprise Apps to the Cloud (PaaS)" at SDForum Cloud Services SIG at Stanford University on Tuesday August 24th
The cloud market has evolved to the point where it’s no longer enough to just offer virtual machines by the hour. Developers are demanding more, and the largest clouds in the world are providing it. Users expect services like Database-as-a-Service, Cache-as-a-Service and Queue-as-a-Service. These types of services are now the new bar for cloud operators, and represent a shift from basic IaaS to IaaS+. In this talk, we will discuss the increasing pace of innovation around higher value services in the public cloud market, and how this impacts your cloud (be it public or private).
AWS is hosting the first FSI Cloud Symposium in Hong Kong, which will take place on Thursday, March 23, 2017 at Grand Hyatt Hotel. The event will bring together FSI customers, industry professional and AWS experts, to explore how to turn the dream of transformation, innovation and acceleration into reality by exploiting Cloud, Voice to Text and IoT technologies. The packed agenda includes expert sessions on a host of pressing issues, such as security and compliance, as well as customer experience sharing on how cloud computing is benefiting the industry.
Speaker: Lijia Xu, Big Data Practice Lead, Professional Services, AWS
Cloudlytics Helps You analyze Amazon Cloud Logs -
- Amazon S3
- Amazon CloudFront
- Amazon ELB
This Presentation Gives a Basic overview of Cloudlytics Features, Pricing Details, Offers to AWS Activate Customers, AWS Marketplace Info & A Sneak Preview of All the Analytics ( The Reports Section will be covered in Detail in our Next Presentation.)
World's best AWS Cloud Log Analytics & Management ToolCloudlytics
This document introduces Cloudlytics, a service that provides analytics and reporting for Amazon Web Services (AWS) cloud logs. It allows users to analyze logs from CloudFront, S3 storage, Elastic Load Balancing, and more to gain insights into end user behavior and optimize AWS costs. The summaries are drag-and-drop customizable and include visual reports on content consumption patterns, popular content, geographic usage, and cost analysis. Cloudlytics claims to be easier, faster, and more cost-effective than alternatives for AWS log analytics.
This talk will serve as a practical introduction to Distributed Tracing. We will see how we can make best use of open source distributed tracing platforms like Hypertrace with Azure and find the root cause of problems and predict issues in our critical business applications beforehand.
This document discusses Infrastructure as a Service (IaaS) and key IaaS services provided by Amazon Web Services (AWS). It introduces AWS IaaS services like Elastic Compute Cloud (EC2) which provides scalable compute capacity, Simple Storage Service (S3) for unlimited storage, and Simple Queue Service (SQS) for reliable messaging between applications. Other services mentioned include SimpleDB for flexible key-value storage and Relational Database Service for managed relational databases. The document explains features and use cases of these AWS IaaS services and how they provide scalable, on-demand infrastructure resources over the internet.
Big Data and Analytics Innovation SummitMartin Yan
This document discusses how customers can use Amazon Web Services (AWS) for big data projects. It outlines the typical big data pipeline of data generation, collection, storage, sharing, and analysis. It then provides examples of how various customers are using AWS services like S3, EMR, and Redshift across different industries to remove constraints to experimentation and gain competitive advantages from big data insights. Overall, AWS allows customers to focus on their data without having to manage the underlying infrastructure.
Load data from AWS S3 to Snowflake in minutessyed_javed
Lyftron enables real-time data streaming and bulk loading onto Snowflake to accelerate data movement using Spark compute. It allows businesses to make data-driven decisions by empowering them with cost-effective and scalable data solutions using ANSI SQL on any data, shortening time to insights by 75% and eliminating complexity to easily access data loaded from AWS S3 to Snowflake in minutes.
Euronext, the 1st European stock exchange with €3.7 trillion in market cap, built a governed data lake based on Amazon AWS to analyze data from one of the largest databases in Europe enriched with 1.5 billion new messages every day. Euronext uses Talend and AWS services - Amazon S3, Amazon Redshift and Amazon EMR for better agility, elasticity, breadth of functionality and cost savings, compared to the previous Netezza-based solution, while guaranteeing data governance and regulatory compliance.
This document discusses how data can be collected from various sources and transformed into useful information using FME. It outlines a three step process: 1) Connect to data from different formats and sources, 2) Clean the data by keeping useful information and filtering out bad data, and 3) Make the data presentable through visualizations, statistics, and reports. Examples of using FME to visualize trial results in PDFs and create dashboards for infrastructure asset management are provided. QlikMaps, Tableau, and other tools for business intelligence and creating visualizations are also mentioned.
Introduction to Data Analysis, Storage & Processing SolutionsAnjani Phuyal
1. The document discusses various data analysis, storage, and processing solutions including data analysis, data analytics, data lakes, data warehouses, data marts, batch processing, and stream processing.
2. It describes challenges of data analytics including volume, velocity, and variety and recommends solutions like Amazon S3, Redshift, EMR, and Kinesis to address these challenges at scale.
3. The key aspects covered are data storage methods like data lakes, data warehouses, and data marts and data processing methods including batch processing using EMR and stream processing using Kinesis.
This presentation from the AWS Lab at Cloud Expo Europe 2014 explores large scale data analysis on AWS. The cost of data generation is falling. Storing, analyzing and sharing data using the tools that AWS offers a low cost and easy to use solution for creating value from your data assets.
Cloud computing allows users to increase resources and capabilities without investing in new infrastructure by providing subscription-based services over the Internet. It encompasses services like software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) that extend existing IT capabilities. Cloud computing provides computation, software, data access, and storage without users needing knowledge of the underlying system infrastructure.
1Spatial Australia: Introduction and getting started with fme 20171Spatial
This document introduces new features in FME 2017 including over 20 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, improved user interface features for workflows, expanded web services and file system capabilities, an updated data inspector, and new automation capabilities for running workflows on demand or on a schedule. The overall goal of FME is to allow data to flow freely between systems and applications while enabling users to spend more time making decisions rather than struggling with data integration tasks.
Slides: Proven Strategies for Hybrid Cloud Computing with Mainframes — From A...DATAVERSITY
Mainframes continue to perform mission-critical transaction processing and contain massive amounts of core business data. But digital transformation initiatives and cloud computing have created both opportunities and challenges for unlocking and utilizing this data. Qlik and AWS will share some of the proven strategies from successful customer deployments across a range of different mainframe to cloud use cases, including legacy application modernization, data analytics, and data migrations.
In this presentation, you will learn how to:
• Replicate very large volumes of mainframe data in real-time to the cloud
• Automate the creation of analytics-ready data lakes and data warehouses
• Achieve a 30% reduction in cost of compute
This document summarizes an AWS summit that took place in 2014. It had over 3,000 attendees and 25 breakout sessions. The summit celebrated AWS's 8th birthday since launching in 2006. It highlighted how startups, enterprises, the public sector, and systems integrators are using AWS. It also discussed how AWS provides infrastructure as a service with the largest market share according to Gartner, and how its services allow for agility, breadth of platform, and continual innovation.
Building a real-time, scalable and intelligent programmatic ad buying platformJampp
After a brief introduction to programmatic ads and RTB we go through the evolution of Jampp's data platform to handle the enormous about of data we need to process.
CTX, a large digital asset exchange, needed to deploy their application to AWS in a highly available, scalable, and compliant environment. Powerup helped provision the infrastructure using CloudFormation according to the AWS Well-Architected Framework and compliance specifications, which automated the infrastructure and application deployment. This resulted in cost savings of 60-70% through the use of spot instances and a secure environment with continuous integration and delivery pipelines. The solution surpassed CTX's expectations by providing scalability and enhanced DevOps and security capabilities.
High availability, real-time and scalable architecturesJampp
Presented at the Architecture Conference (ArqConf) in Buenos Aires, Argentina. Here is a 10,000ft view of our Real Time Bidding and Stream Processing architecture.
Processing 19 billion messages in real time and NOT dying in the processJampp
Here is an introduction in the Jampp architecture for data processing. We walk through our journey of migrating to systems that allows us to process more data in real time
Rob Rastovich of Appirio presents "Migrating Enterprise Apps to the Cloud (PaaS)" at SDForum Cloud Services SIG at Stanford University on Tuesday August 24th
The cloud market has evolved to the point where it’s no longer enough to just offer virtual machines by the hour. Developers are demanding more, and the largest clouds in the world are providing it. Users expect services like Database-as-a-Service, Cache-as-a-Service and Queue-as-a-Service. These types of services are now the new bar for cloud operators, and represent a shift from basic IaaS to IaaS+. In this talk, we will discuss the increasing pace of innovation around higher value services in the public cloud market, and how this impacts your cloud (be it public or private).
AWS is hosting the first FSI Cloud Symposium in Hong Kong, which will take place on Thursday, March 23, 2017 at Grand Hyatt Hotel. The event will bring together FSI customers, industry professional and AWS experts, to explore how to turn the dream of transformation, innovation and acceleration into reality by exploiting Cloud, Voice to Text and IoT technologies. The packed agenda includes expert sessions on a host of pressing issues, such as security and compliance, as well as customer experience sharing on how cloud computing is benefiting the industry.
Speaker: Lijia Xu, Big Data Practice Lead, Professional Services, AWS
Cloudlytics Helps You analyze Amazon Cloud Logs -
- Amazon S3
- Amazon CloudFront
- Amazon ELB
This Presentation Gives a Basic overview of Cloudlytics Features, Pricing Details, Offers to AWS Activate Customers, AWS Marketplace Info & A Sneak Preview of All the Analytics ( The Reports Section will be covered in Detail in our Next Presentation.)
World's best AWS Cloud Log Analytics & Management ToolCloudlytics
This document introduces Cloudlytics, a service that provides analytics and reporting for Amazon Web Services (AWS) cloud logs. It allows users to analyze logs from CloudFront, S3 storage, Elastic Load Balancing, and more to gain insights into end user behavior and optimize AWS costs. The summaries are drag-and-drop customizable and include visual reports on content consumption patterns, popular content, geographic usage, and cost analysis. Cloudlytics claims to be easier, faster, and more cost-effective than alternatives for AWS log analytics.
This talk will serve as a practical introduction to Distributed Tracing. We will see how we can make best use of open source distributed tracing platforms like Hypertrace with Azure and find the root cause of problems and predict issues in our critical business applications beforehand.
This document discusses Infrastructure as a Service (IaaS) and key IaaS services provided by Amazon Web Services (AWS). It introduces AWS IaaS services like Elastic Compute Cloud (EC2) which provides scalable compute capacity, Simple Storage Service (S3) for unlimited storage, and Simple Queue Service (SQS) for reliable messaging between applications. Other services mentioned include SimpleDB for flexible key-value storage and Relational Database Service for managed relational databases. The document explains features and use cases of these AWS IaaS services and how they provide scalable, on-demand infrastructure resources over the internet.
Big Data and Analytics Innovation SummitMartin Yan
This document discusses how customers can use Amazon Web Services (AWS) for big data projects. It outlines the typical big data pipeline of data generation, collection, storage, sharing, and analysis. It then provides examples of how various customers are using AWS services like S3, EMR, and Redshift across different industries to remove constraints to experimentation and gain competitive advantages from big data insights. Overall, AWS allows customers to focus on their data without having to manage the underlying infrastructure.
Load data from AWS S3 to Snowflake in minutessyed_javed
Lyftron enables real-time data streaming and bulk loading onto Snowflake to accelerate data movement using Spark compute. It allows businesses to make data-driven decisions by empowering them with cost-effective and scalable data solutions using ANSI SQL on any data, shortening time to insights by 75% and eliminating complexity to easily access data loaded from AWS S3 to Snowflake in minutes.
Euronext, the 1st European stock exchange with €3.7 trillion in market cap, built a governed data lake based on Amazon AWS to analyze data from one of the largest databases in Europe enriched with 1.5 billion new messages every day. Euronext uses Talend and AWS services - Amazon S3, Amazon Redshift and Amazon EMR for better agility, elasticity, breadth of functionality and cost savings, compared to the previous Netezza-based solution, while guaranteeing data governance and regulatory compliance.
This document discusses how data can be collected from various sources and transformed into useful information using FME. It outlines a three step process: 1) Connect to data from different formats and sources, 2) Clean the data by keeping useful information and filtering out bad data, and 3) Make the data presentable through visualizations, statistics, and reports. Examples of using FME to visualize trial results in PDFs and create dashboards for infrastructure asset management are provided. QlikMaps, Tableau, and other tools for business intelligence and creating visualizations are also mentioned.
Introduction to Data Analysis, Storage & Processing SolutionsAnjani Phuyal
1. The document discusses various data analysis, storage, and processing solutions including data analysis, data analytics, data lakes, data warehouses, data marts, batch processing, and stream processing.
2. It describes challenges of data analytics including volume, velocity, and variety and recommends solutions like Amazon S3, Redshift, EMR, and Kinesis to address these challenges at scale.
3. The key aspects covered are data storage methods like data lakes, data warehouses, and data marts and data processing methods including batch processing using EMR and stream processing using Kinesis.
This presentation from the AWS Lab at Cloud Expo Europe 2014 explores large scale data analysis on AWS. The cost of data generation is falling. Storing, analyzing and sharing data using the tools that AWS offers a low cost and easy to use solution for creating value from your data assets.
Cloud computing allows users to increase resources and capabilities without investing in new infrastructure by providing subscription-based services over the Internet. It encompasses services like software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) that extend existing IT capabilities. Cloud computing provides computation, software, data access, and storage without users needing knowledge of the underlying system infrastructure.
1Spatial Australia: Introduction and getting started with fme 20171Spatial
This document introduces new features in FME 2017 including over 20 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, improved user interface features for workflows, expanded web services and file system capabilities, an updated data inspector, and new automation capabilities for running workflows on demand or on a schedule. The overall goal of FME is to allow data to flow freely between systems and applications while enabling users to spend more time making decisions rather than struggling with data integration tasks.
Slides: Proven Strategies for Hybrid Cloud Computing with Mainframes — From A...DATAVERSITY
Mainframes continue to perform mission-critical transaction processing and contain massive amounts of core business data. But digital transformation initiatives and cloud computing have created both opportunities and challenges for unlocking and utilizing this data. Qlik and AWS will share some of the proven strategies from successful customer deployments across a range of different mainframe to cloud use cases, including legacy application modernization, data analytics, and data migrations.
In this presentation, you will learn how to:
• Replicate very large volumes of mainframe data in real-time to the cloud
• Automate the creation of analytics-ready data lakes and data warehouses
• Achieve a 30% reduction in cost of compute
This document summarizes an AWS summit that took place in 2014. It had over 3,000 attendees and 25 breakout sessions. The summit celebrated AWS's 8th birthday since launching in 2006. It highlighted how startups, enterprises, the public sector, and systems integrators are using AWS. It also discussed how AWS provides infrastructure as a service with the largest market share according to Gartner, and how its services allow for agility, breadth of platform, and continual innovation.
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• 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 a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Getting Started with Amazon Redshift - AWS July 2016 Webinar SeriesAmazon Web Services
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for as low as $1000/TB/year. This webinar will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Learning Objectives:
• Get an introduction to Amazon Redshift's massively parallel processing, columnar, scale-out architecture
• Learn how to configure your data warehouse cluster, optimize schema, and load data efficiently
• Get an overview of all the latest features including interleaved sorting and user-defined functions
This document discusses Amazon Web Services' database and analytics services. It begins by noting that 85% of businesses want to be data-driven but only 37% have been successful. It then presents the "data flywheel" concept of breaking from legacy databases, modernizing data infrastructure and data warehouses, turning data into insights, and building data-driven applications to gain momentum with data. The document provides overviews and benefits of AWS services like Amazon Aurora, Athena, Redshift, RDS, DMS, and Elasticsearch. It also introduces new capabilities for these services like machine learning with Aurora, RDS on Outposts, UltraWarm storage for Elasticsearch, and materialized views in Redshift.
This presentation summarizes Amazon Redshift data warehouse service, its architecture and best practices for application development using Amazon Redshift.
The document discusses Microsoft's data platform and cloud services. It highlights:
1) Microsoft's data platform provides intelligence over all data with SQL and Apache Spark, enabling AI and machine learning over any data.
2) Microsoft offers data modernization solutions for migrating to the cloud or managing data on-premises and in hybrid environments.
3) Migrating databases to Azure provides cost savings, security, high performance, and intelligent capabilities through services like Azure SQL Database and Azure Cosmos DB.
Building with Purpose - Built Databases: Match Your Workloads to the Right Da...Amazon Web Services
In this session, Darin Briskman dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, and more. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
Darin Briskman, Chief Evangelist, Database, Analytics, & Machine Learning, Amazon Web Services
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
NetApp Cloud Data Services & AWS Empower Your Cloud ChampionsAmazon Web Services
The document discusses enabling cloud champions with AWS and NetApp cloud data services. It highlights how hyperscale computing is leading the way for government agencies to use data to innovate and reduce costs. NetApp cloud volumes provide enterprise-level file services on AWS to accelerate all types of cloud workloads. Examples are given of how NetApp solutions help with data migration, disaster recovery, and meeting data storage needs on AWS.
MariaDB is an open source relational database that is easy to use, deploy, and extend. It offers lower costs than proprietary databases like Oracle. MariaDB encourages community collaboration and integration of community code and plugins. It has an extensible architecture that allows for things like Galera cluster, InnoDB storage engine, security key management plugins and more. It is used by many large companies and is the default database for major Linux distributions.
AWS March 2016 Webinar Series - Building Big Data Solutions with Amazon EMR a...Amazon Web Services
Building big data applications often requires integrating a broad set of technologies to store, process, and analyze the increasing variety, velocity, and volume of data being collected by many organizations.
Using a combination of Amazon EMR, a managed Hadoop framework, and Amazon Redshift, a managed petabyte-scale data warehouse, organizations can effectively address many of these requirements.
In this webinar, we will show how organizations are using Amazon EMR and Amazon Redshift to build more agile and scalable architectures for big data. We will look into how you can leverage Spark and Presto running on EMR, to address multiple data processing requirements. We will also share best practices and common use cases to integrate EMR and Redshift.
Learning Objectives:
• Best practices for building a big data architecture that includes Amazon EMR and Amazon Redshift
• Understand how to use technologies such as Amazon EMR, Presto and Spark to complement your data warehousing environment
• Learn key use cases for Amazon EMR and Amazon Redshift
Who Should Attend:
• Data architects, Data management professionals, Data warehousing professionals, BI professionals
The Most Trusted In-Memory database in the world- AltibaseAltibase
This document provides an overview of an in-memory database company and its product capabilities. It discusses the company's history and growth, the changing data landscape driving demand for real-time analytics, and how the company's in-memory and hybrid database technologies provide extremely fast transaction processing, high availability, scalability, and flexibility for deploying on-premise or in the cloud. Example customer use cases and implementations are described to demonstrate how the database has helped organizations tackle challenges of high volume data processing and analytics.
(1) Amazon Redshift is a fully managed data warehousing service in the cloud that makes it simple and cost-effective to analyze large amounts of data across petabytes of structured and semi-structured data. (2) It provides fast query performance by using massively parallel processing and columnar storage techniques. (3) Customers like NTT Docomo, Nasdaq, and Amazon have been able to analyze petabytes of data faster and at a lower cost using Amazon Redshift compared to their previous on-premises solutions.
This document provides an overview of an AWS event. It includes details about the AWS business including $16B in annual revenue and over 135,000 active customers. It discusses the breadth of AWS services and tools available, positioning AWS as a leader in cloud infrastructure. The document outlines how AWS gives customers superpowers with super sonic speed and pace of innovation. It provides examples of how customers are using AWS services to transform their businesses.
This document provides an overview of MariaDB's 2017 roadshow, including what they are doing, where they are going, and who the field CTO is. It discusses trends in the database market moving away from expensive proprietary databases toward lower-cost open source options with subscriptions and community involvement. It highlights cost savings of MariaDB compared to Oracle and MariaDB's extensible architecture and community contributions. It also summarizes MariaDB products and technologies like the database server, MaxScale proxy, and ColumnStore, as well as MariaDB's customers, use cases, services, and how to get started with MariaDB.
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
Astroinformatics 2014: Scientific Computing on the Cloud with Amazon Web Serv...Jamie Kinney
An overview of Amazon Web Services (AWS) and a survey of scientific computing applications of cloud computing. Examples come from the fields of Astronomy, High Energy Physics and include examples from CERN, NASA and others.
This document discusses HiFX helping Malayala Manorama, one of the largest media conglomerates in India, build a data lake on AWS. Malayala Manorama faced challenges with data silos, lack of access to data for analysis, and poor data management. HiFX designed a solution connecting multiple data sources and repositories into a unified data pipeline on AWS. Raw data is stored in the data lake on S3 for low-cost storage. Spark on EMR is used for processing, with results stored in Redshift, Druid and DynamoDB. A custom app called Lens provides business users visualizations and insights. The benefits include improved user experience, campaign management, and faster product decisions based on data insights
20. Aggregate Ad Serving data Log Files File Export APIs Internet Client Provided Data Data Sources Presentation Layer Talend Data Flow Manager Direct Analytics Processing via EMR Web Application Layer ODBC Edge Provisioning DB OLAP Cache Cloud Storage S3 HBase/SDB 15 Elastic MapReduce
24. Drive a personalized message User recently purchased a home theater system and is now looking for sports games Target Ad ( 1.7 million per day )
25. We import Atlas transaction level data 24 servers S3 file storage Compress and upload 200 + GB of data per day ( 180 days = ½ Trillion ICA records )
26. We use EMR to process and segment EMR S3 100 Machinecluster created on demand ( 3.5 Billion records, 71 million unique cookies a day)
27. Process and Cost This all happens in about 8 hours every day and is fully automated (previously 2+ days) And increased ROAS by 500% (to $74)
28. Why AWS Efficient Elastic infrastructure from AWS allows capacity to be provisioned as needed based on load, reducing cost and the risk of processing delays Ease of integration Amazon Elastic MapReduce with Cascading allows data processing in the cloud without any changes to the underlying algorithms Flexible Hadoop with Cascading is flexible enough to allow “agile” implementation and unit testing of sophisticated algorithms. Adaptable Cascading simplifies the integration of Hadoop with external ad system Scalable AWS infrastructure helps reliably store and process huge (Petabytes) data setss