What is a Data Lake?
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
As easy as it is to get swept up by the hype surrounding big data, it’s just as easy for organizations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt.
However, given big data’s power to transform business, it’s critical that organizations overcome these challenges and realize the value of big data.
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IDC’s research has shown the movement of most IT workloads to the cloud in the coming years. Yet, with all the talk about enterprises moving to the cloud, some of them still wonder if such a move is really cost effective and what business benefits may result. While the answers to such questions vary from workload to workload, one area attracting particular attention is that of the data warehouse.
Many enterprises have substantial investments in data warehousing, with an ongoing cost to managing that resource in terms of software licensing, maintenance fees, operational costs, and hardware. Can it make sense to move to a cloud-based alternative? What are the costs and benefits? How soon can such a move pay itself off?
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Defining the Data Lake
“Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
Die Recherchen von IDC haben ergeben, dass in den nächsten Jahren die meisten IT-Workloads in die Cloud verschoben werden. Doch neben all den positiven Berichten über Unternehmen, die in die Cloud umziehen, gibt es auch Unternehmen, die sich noch immer fragen, ob ein solcher Wechsel wirklich kosteneffizient ist und welche Vorteile sich aus einem solchen ergeben. Während die Antworten auf solche Fragen von Workload zu Workload variieren, gibt es ein Element, das besondere Aufmerksamkeit auf sich zieht: das Data-Warehouse.
Il est tout aussi facile d'être submergé par l'omniprésent Big Data qu'il l'est pour les organisations d'être découragées par les défis qu'elles rencontrent lorsqu'elles implémentent une initiative en matière de Big Data. Les préoccupations liées aux ensembles de compétences associées au Big Data (et à leur absence), à la sécurité, à l'imprévisibilité des données, aux coûts non viables et à la nécessité d'effectuer une analyse de rentabilité peuvent mettre brutalement fin à une initiative en matière de Big Data.
La plupart des entreprises ont investi considérablement dans le stockage de leurs données, avec un coût de gestion continu en termes de licences logicielles, frais de maintenance, coûts opérationnels et matériel. Est-il plus judicieux d'opter pour une solution cloud ? Quels en sont les coûts et les avantages ? En combien de temps un tel choix est-il rentabilisé?
Découvrez, dans ce document, la synthèse de l’enquête IDC sur le retour d'expérience de 8 entreprises utilisant Amazon Redshift.
Maintaining a competitive edge today means building a Digital Enterprise capable of taking full advantage of social, mobile, web, cloud, “things,” (sensors and devices), and analytics technologies. Among the terms used to describe this business transition is “the API Economy,” an economy in which APIs are no longer just an IT concern, but the underpinnings of new revenue streams and new business models that are disrupting entire industries.
Read this paper to learn about:
New, modern applications being built for the enterprise
Application ecosystems and extending the value of your company in the API Economy
Two ways to integrate devices in the Internet of Things
The microservices approach to application development
The role of API management in the digital enterprise
Published By: Iterable
Published Date: Sep 07, 2018
Whether they want tickets for the next Lady Gaga concert, the World Series, the Indianapolis 500, or Hamilton, people are quickly discovering that SeatGeekis the place to find the best selection and great bargains.
This relative newcomer to the online ticket business has quickly grown to offer the largest inventory of live event tickets on the web, in addition offering differentiating services like best-bargain ratings and notifications when a fan’s favorite team or entertainer will be performing nearby.
Email and push have been the primary channels for interacting with customers. However, according to Ben Clark, Vice President of Customer Retention, the marketing team previously struggled to deliver consistent, relevant messaging across channels because their email and push tools ran on separate platforms.
The old tools were also cumbersome to use and offered limited functionality. Worse yet, they didn’t support the team’s AI driven, omni-channel marketing strategy, which includes rea
Gartner named Akamai a Leader in their 2017 Magic Quadrant for Web Application Firewalls.
A web application firewall is an essential element in your defense against application-layer attacks, which pose an ever-greater threat to productivity and security.
The Akamai approach to WAF combines:
An anomaly detection model
A repeatable testing framework to measure effectiveness
Threat intelligence to identify the latest threats
A cloud platform for global scale
Managed security services to help organizations better protect their websites and web applications over time
Learn more about the AWS Partner Webinar Series at - https://amzn.to/2ILG0R7.
Join our webinar to hear how Lyft and other data-driven organizations benefit from uncovering hidden insights in real time with AI solutions from Amazon Web Services (AWS) and Anodot. Learn how to prevent events that can impact your revenue and brand integrity with a solution that detects anomalies quickly, allowing you to address issues in a timely manner to help ensure a consistently high-quality experience for your customers.
Learn more Salesforce and Amazon Web Services at http://amzn.to/2zhcn1v.
Building and releasing cutting-edge applications quickly can be difficult when you lack proper tooling and integrated customer data. Salesforce Heroku delivers a cloud-native, developer-friendly platform that streamlines application development by integrating formerly siloed customer data and removing the burden of infrastructure management, allowing developers to focus their attention solely on creating customer-centric applications. Join the upcoming webinar to hear how the Financial Times builds customer-driven applications with faster cycle times with Heroku and AWS.
AWS supports healthcare organizations with HIPAA Eligible Services and the AWS Healthcare Compliance program. AWS products and services are being used by many customers that handle electronic patient health information (PHI) to build solutions that meet HIPAA and HITRUST regulatory requirements for cloud-based workloads.
In this webinar, you’ll learn how AWS HIPAA Eligible Services can help you build secure workloads to handle PHI in compliance with HIPAA and HITRUST standards. AWS Healthcare experts will be joined in this webinar by AWS Partner Network (APN) Partners ClearDATA and Cloudticity.
Learn more about AWS Partner Webinars at - https://amzn.to/2I6ogPM.
In this webinar, you will learn about VMware Cloud on AWS, an integrated hybrid cloud offering jointly developed by AWS and VMware delivering a highly scalable, secure and innovative service that enables organizations to seamlessly migrate and extend their on-premises VMware vSphere-based environments to AWS running on next-generation Amazon Elastic Compute Cloud (Amazon EC2) bare metal infrastructure.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making.
Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
AbeBooks, with Amazon Redshift, has been able to upgrade to a comprehensive data warehouse with the enlistment of Matillion ETL for Amazon Redshift. In this case study, we share AbeBooks’ data warehouse success story.
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set:
• Enterprise-class relational database query and management system
• Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools
• Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications.
Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios.
Since starting to work with this technolog
Consumers worldwide continue to adopt and use technology in their shopping experience.
Faced with rising customer expectations and increasing competitive pressures, retailers
now are prioritizing in-store innovation. Many retailers have adopted multichannel
implementations, in which mobile, web, and in-store shopping are enabled but not delivered
consistently to the customer. The next step in this evolution is an omnichannel strategy, now
being deployed by some retailers, which presents a consistent shopping experience across
mobile, web, and in-store channels. Omnichannel also enables retailers to integrate back-end
infrastructure technologies (e.g., servers, databases, etc.) and cloud-based services (e.g., loyalty
programs, personalized recommendations, inventory management, etc.) to improve many
aspects of store and enterprise operations.
An omnichannel strategy relies on several core and supporting technologies. The key factors in
evaluating any omnichannel-enabling solution includ
Published By: MuleSoft
Published Date: Jul 13, 2018
"IT teams across industries face growing pressure to deliver projects faster while reducing costs. All too often, dated legacy systems hinder IT’s ability to accomplish either of these objectives. Legacy systems can also slow the speed at which IT can deliver new projects to support the business. For these reasons, legacy modernization has emerged as a key strategic imperative. But where should organizations start? One large global bank provides a detailed blueprint for how large enterprises can do so.
Read this legacy modernization blueprint to learn:
-Best practices for modernizing legacy SOA web services
-How to think about re-architecting monolithic applications into microservices
-The role that APIs play in driving an effective legacy modernization strategy
-The bank’s legacy modernization strategy, and how they used Anypoint Platform"
While the shift from disk to digital offers tremendous potential opportunities, it also presents a host
of new challenges for gaming companies. As the online channel grows increasingly complex and the pace
of innovation accelerates, many companies struggle to keep up. Not only are there websites and storefronts
to manage, but also real-time gaming servers, large software downloads, and live-streamed competitions and
events. Games are transforming from fixed, boxed products to dynamic, ongoing services – with frequently
updated content, in-game micro-transactions, virtual goods and social interactions. Mobile adds another
dimension to the trend, as consumers increasingly look to play on smart phones and tablets – or on multiple
screens across devices.
To successfully navigate this complex and changing landscape, gaming companies need an agile,
high- performance infrastructure that allows them to turn the Internet into a reliable and effective
online distribution channel. This requires f
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