2 one spot redshift bigdatacamp 1.02
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OneSpot is an advertising platform that uses Amazon Redshift, a fast, petabyte-scale data warehouse service from AWS, to analyze large amounts of customer data. Redshift uses a column-oriented design and cluster architecture to optimize for read performance at large scales. It provides standard SQL functionality and can scale to petabytes of data, making it easy for OneSpot to manage over 300 billion rows of customer data without requiring a dedicated database administrator.
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Amazon Web Services proporciona una amplia gama de servicios que le ayudarán a crear e implementar aplicaciones de análisis de big data de forma rápida y sencilla. AWS ofrece un acceso rápido a recursos de TI económicos y flexibles, algo que permitirá escalar prácticamente cualquier aplicación de big data con rapidez, incluidos almacenamiento de datos, análisis de clics, detección de elementos fraudulentos, motores de recomendación, proceso ETL impulsado por eventos, informática sin servidor y procesamiento del Internet de las cosas. Con AWS no necesita hacer grandes inversiones iniciales de tiempo o dinero para crear y mantener la infraestructura. En su lugar, puede aprovisionar exactamente el tipo y el tamaño adecuado de los recursos que necesita para impulsar sus aplicaciones de análisis de big data. Puede obtener acceso a tantos recursos como necesite, prácticamente al instante, y pagar únicamente por los utilice.
AWS as a Data Platform for Cloud and On-Premises Workloads | AWS Public Secto...
This session discusses the set of data services that AWS offers for managing all types of data, including files, objects, databases, and data warehouses. We will discuss use cases for each AWS data service, including unique capabilities that the cloud enables and hybrid scenarios for integrating and migrating on-premises data to AWS. This session discusses Amazon S3, AWS Storage Gateway, Amazon EBS, Amazon RDS, Amazon Redshift, and native databases running on AWS. It also covers some of the key data and storage capabilities provided by AWS partners, and considerations for integrating with and migrating enterprise data to the cloud.
Scaling on AWS to the First 10 Million Users
An intro to the world of Amazon Web Services. Learn about AWS services through starting with day one (1 developer) to scaling your app to 10 Million users.
A3 transforming data_management_in_the_cloud
The document discusses transforming data management to the cloud. It describes how Oracle's database cloud services provide complete data management across multiple data types at any scale both on-premises and in the cloud. It highlights how the cloud offers lower costs through pay-as-you-go pricing and lower operational expenses, as well as increased agility through rapid provisioning and elastic scaling. Oracle 12c and new features in 12c Release 2 provide database consolidation, isolation at scale, and online operations for pluggable databases in the cloud.
Scaling on AWS for the First 10 Million Users
Cloud computing gives you a number of advantages, such as being able to scale your application on demand. As a new business looking to use the cloud, you inevitably ask yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from zero to millions of users. We will show you how to best combine different AWS services, make smarter decisions for architecting your application, and best practices for scaling your infrastructure in the cloud.
Database and Analytics on the AWS Cloud
Antoine Genereux takes us on a detailed overview of the Database solutions available on the AWS Cloud, addressing the needs and requirements of customers at all levels. He also discusses Business Intelligence and Analytics solutions.
Amazon Redshift with Full 360 Inc.
Full 360 is a cloud consulting firm that provides big data, API/UX, and cloud operations services. They helped a customer migrate their data from Netezza to Redshift, building a structured data lake and optimizing queries for equivalent or better performance. Lessons from the project included data standardization, tuning techniques like encoding and sort keys, and creating reusable ingestion processes. The migration reduced license costs and improved operational flexibility.
Amazon Redshift in Action: Enterprise, Big Data, and SaaS Use Cases (DAT205) ...
Since Amazon Redshift launched last year, it has been adopted by a wide variety of companies for data warehousing. In this session, learn how customers NASDAQ, HauteLook, and Roundarch Isobar are taking advantage of Amazon Redshift for three unique use cases: enterprise, big data, and SaaS. Learn about their implementations and how they made data analysis faster, cheaper, and easier with Amazon Redshift.
Using Data Lakes
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.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
Weathering the Data Storm – How SnapLogic and AWS Deliver Analytics in the Cl...
In this webinar, learn how SnapLogic and Amazon Web Services helped Earth Networks create a responsive, self-service cloud for data integration, preparation and analytics.
We also discuss how Earth Networks gained faster data insights using SnapLogic’s Amazon Redshift data integration and other connectors to quickly integrate, transfer and analyze data from multiple applications.
To learn more, visit: www.snaplogic.com/redshift
Data Warehouse Modernization - Big Data in the Cloud Success with Qubole on O...
The effective use of big data is the key to gaining a competitive advantage and outperforming the competition. This change demands that companies consume and blend enormous amount of data created from divergent and inherently mismatched sources, which represents a paradigm shift to the traditional data warehouse.
Companies need to modernize their data warehouse, augmenting it with a platform that allows storage, processing, exploration and analysis of large and diverse datasets without limiting the ability to deliver the data access, and flexibility responding to the needs of the business. That’s where Oracle Cloud and Qubole work together delivering a new breed of data platform —capable of storing and processing the overwhelming amount of data that on-premises big data deployments cannot handle.
Watch this on-demand webinar to understand:
- Why deploying big data on-premises is expensive, complex to maintain and limits your ability to scale across new use cases and data sources
- How Oracle Bare Metal Cloud's predictable and fast performance compute and network services deliver the foundation of a cost-effective, high-performance big data platform
- How Qubole leverages Oracle Bare Metal Cloud to provide a turnkey big data service that optimizes cost, performance, and scale, enabling self-service data exploration.
Qubole delivers a cloud-based, turnkey, self-service big data service that removes the complexity and reduces the cost of doing big data. It leverages Oracle Bare Metal Cloud’s next generation of scalable, inexpensive and performant compute, network and storage public cloud infrastructure to provide a solution that accelerates time to market and reduces the risk of your big data initiatives.
Deep Dive: Scaling Up to Your First 10 Million Users
This document discusses best practices for scaling infrastructure on AWS to support over 10 million users. It begins by recommending using multiple AWS regions and availability zones for redundancy. It then walks through scaling a simple single-instance application to be horizontally and vertically scaled across multiple instances, database read replicas, caching, and content delivery. Key services discussed include EC2, RDS, DynamoDB, ElastiCache, S3, CloudFront, Route 53, and Auto Scaling. Automating management using tools and separating concerns like static assets are also recommended.
Building_a_Modern_Data_Platform_in_the_Cloud.pdf
The document discusses the speaker's journey in building analytics capabilities over time, from using a separate server for reporting to using Hadoop and NoSQL databases to address scaling issues. It notes the challenges of maintaining these systems and data preparation work. The presentation then outlines Amazon Web Services offerings for building a modern data lake and analytics platform in AWS, including services for data storage, movement, processing, analytics and machine learning.
Scaling on AWS for the First 10 Million Users at Websummit Dublin
Ian Massingham gave a presentation on scaling applications on AWS from initial launch to over 1 million users. He began by discussing foundational AWS services and database options. He then walked through examples of scaling an application from 1 user to over 500,000 users by leveraging services like EC2, RDS, DynamoDB, ElastiCache, S3, CloudFront, and Auto Scaling. Key strategies included separating components across instances, adding redundancy, implementing caching, and leveraging auto scaling to dynamically scale resources based on demand. Massingham concluded by discussing strategies for scaling beyond 500,000 users such as service-oriented architectures and workload distribution across availability zones.
Scaling on AWS for the First 10 Million Users at Websummit Dublin
In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Ian Massingham discusses the techniques that AWS customers can use to create highly scalable infrastructure to support the operation of large scale applications on the AWS cloud.
Includes a walk-through of how you can evolve your architecture as your application becomes more popular and you need to scale up your infrastructure to support increased demand.
Welcome & AWS Big Data Solution Overview
講師: Ivan Cheng, Solution Architect, AWS
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
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1 big datacampdell2013
The document discusses big data analytics. It defines big data analytics as extracting value from large volumes of a wide variety of data by analyzing it to reveal patterns, trends and associations. This analysis can help companies gain competitive advantages by using the insights for applications like marketing automation, predictive analytics, risk analysis and fraud detection. The document also notes that big data analytics techniques like machine learning, collective intelligence and predictive analytics are complex. It proposes that a big data analytics suite could help more businesses take advantage of these techniques by making them easier to implement.
2 one spot redshift bigdatacamp 1.02
OneSpot is an advertising platform that uses Amazon Redshift, a cloud data warehouse service, to analyze large amounts of customer data. Redshift provides petabyte-scale storage, fast query performance, and easy management without the need for a database administrator. It uses a columnar data structure and compression techniques to optimize for read-heavy workloads. Redshift clusters data across nodes in the cloud and replicates it for high availability and scalability.
3 analytic strategies shree dandekar dell 12-10-13
The document discusses big data analytic strategies and emerging technologies. It notes that data volumes are growing exponentially from terabytes to zettabytes. Data can come from unstructured sources like text, images, videos and system logs. Technologies used include distributed parallel processing on commodity hardware with an ACID-free approach and MapReduce programming. There are needs around faster insights, real-time integration, industry analytics, and data scientist services. Attaining the optimal solution requires incremental investments in capabilities from casual data access to predictive and cognitive analytics.
1 big datacampdell2013
The document discusses big data analytics. It defines big data analytics as extracting value from large volumes of a wide variety of data by analyzing it to reveal patterns, trends and associations. The 3Vs of big data are described as volume, variety and velocity. Big data comes from sources like cloud computing, social media, mobile devices and sensors. The document outlines how big data analytics uses techniques like machine learning, collective intelligence and predictive analytics to provide business advantages in areas such as marketing, operations and security. It notes that while big data analytics is powerful, it can also be difficult for small and medium businesses without data science expertise.
3 analytic strategies shree dandekar dell 12-10-13
The document discusses big data analytic strategies and emerging technologies. It notes that data volumes are growing exponentially from terabytes to zettabytes. Data can come from unstructured sources like text, images, videos and system logs. Technologies used include distributed parallel processing on commodity hardware with an ACID-free approach and MapReduce programming. There are needs around faster insights, real-time integration, industry analytics, and data scientist services. Attaining the optimal solution requires incremental investments in capabilities from casual data access to predictive and cognitive analytics.
1 big datacampdell2013
The document discusses big data analytics. It defines big data analytics as extracting value from large volumes of a wide variety of data by analyzing it to reveal patterns, trends and associations. This analysis can help companies gain competitive advantages by using the insights for applications like predictive analytics, risk analysis, recommendation systems, and more. It notes that while big data analytics can provide opportunities, it also presents challenges due to its technical complexity. It proposes a big data analytics suite that would make these tools more accessible to businesses.
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2 one spot redshift bigdatacamp 1.02
- 1. Amazon Redshift: How we managed 300 billion rows with no DBA Matt Cohen Founder & President matt@onespot.com December 10th, 2013 Copyright©2013OneSpot,Proprietary&Confidential 1
- 2. What is OneSpot? • OneSpot is a content advertising platform that distributes content as ads that people want to click on. – Fortune 2000 clients – Realtime ad exchange bidding – Adaptive machine learning – Seed funded until $5.3M Series A last month • Big data, big analysis Copyright©2013OneSpot,Proprietary&Confidential 2
- 3. What is Redshift? 1. When light from a receding object appears shifted to the red end of the spectrum – A consequence of the expanding universe. 2. A cheap, fast, Petabyte-scale, managed SQL data warehouse service from Amazon Web Services – A consequence of the expanding cloud ecosystem Copyright©2013OneSpot,Proprietary&Confidential 3
- 4. Why Redshift? • • • • • • • Cheap Fast Petabyte-scale Managed Service SQL Data Warehouse From AWS Copyright©2013OneSpot,Proprietary&Confidential 4
- 5. SQL Data Warehouse • Based on the commercial ParAccel database – Which is based on Postgres • Standards-based tools and knowledge • Built for data warehousing – – – – – Column-oriented Cluster architecture Read optimized No relational integrity Almost no SQL extensions Copyright©2013OneSpot,Proprietary&Confidential 5
- 6. SQL Data Warehouse • Column-oriented Copyright©2013OneSpot,Proprietary&Confidential 6
- 7. SQL Data Warehouse • Column-oriented • 11 different compression techniques Copyright©2013OneSpot,Proprietary&Confidential 7
- 8. SQL Data Warehouse • Cluster architecture Copyright©2013OneSpot,Proprietary&Confidential 8
- 9. SQL Data Warehouse • Read optimized • No relational integrity – Large block size (1MB) – Data replication – No indexes: sort and distribution keys • 2x live, 1x S3 • Almost no SQL extensions Copyright©2013OneSpot,Proprietary&Confidential 9
- 10. Fast = Cheap • Starts with 1 XL node – 85¢ an hour ($620/month) on demand – 50¢ an hour ($365) 1 year reserved • Benchmarks say: – Scales linearly – 5-10x faster than Hadoop/Hive Copyright©2013OneSpot,Proprietary&Confidential 10
- 11. Petabyte scale • Up to – 32 XL nodes (64 Terabytes) – 100 8XL nodes (1.6 Petabytes) Copyright©2013OneSpot,Proprietary&Confidential 11
- 12. Managed Service from AWS • Managed Service – Incredibly easy – Nice UI – Most SQL tools • From AWS – Free data transfer – Easy load from S3 – Use AWS Data Pipeline Copyright©2013OneSpot,Proprietary&Confidential 12
- 13. The TL;DR • Pros – – – – – Standard SQL Super easy Very fast Affordable Integrates with AWS – No DBA – No Sysadmin • Cons – Standard SQL – Almost no SQL extensions – Best with Star Schema • Big joins can be slow – – – – Copyright©2013OneSpot,Proprietary&Confidential No MapReduce Fixed columns Consistency 1.6 Pbyte limit 13
- 14. Amazon Redshift: How we managed 300 billion rows with no DBA Matt Cohen Founder & President matt@onespot.com December 10th, 2013 Copyright©2013OneSpot,Proprietary&Confidential 14