big data analytics

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Published By: Pure Storage     Published Date: Oct 09, 2018
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude
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Pure Storage
Published By: Group M_IBM Q418     Published Date: Oct 02, 2018
Organizations are faced with providing secure authentication, authorization, and Single Sign On (SSO) access to thousands of users accessing hundreds of disparate applications. Ensuring that each user has only the necessary and authorized permissions, managing the user’s identity throughout its life cycle, and maintaining regulatory compliance and auditing further adds to the complexity. These daunting challenges are solved by Identity and Access Management (IAM) software. Traditional IAM supports on-premises applications, but its ability to support Software-as-a-Service (SaaS)-based applications, mobile computing, and new technologies such as Big Data, analytics, and the Internet of Things (IoT) is limited. Supporting on-premises IAM is expensive, complex, and time-consuming, and frequently incurs security gaps. Identity as a Service (IDaaS) is an SaaS-based IAM solution deployed from the cloud. By providing seamless SSO integration to legacy on-premises applications and modern cloud-
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Group M_IBM Q418
Published By: Infosys     Published Date: Sep 11, 2018
Infosys has been recognized as a ‘Leader’ in NelsonHall’s Vendor Evaluation and Assessment (NEAT) report on big data and analytics services 2018.We have also been highly rated for our focus on automation. Our ability to meet future client requirements as well as deliver immediate benefits such as analytics, data management and support functions to our clients with a specific focus on process automation enabled us to secure this position.
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Infosys
Published By: Group M_IBM Q418     Published Date: Sep 10, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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Group M_IBM Q418
Published By: AWS     Published Date: Sep 05, 2018
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
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AWS
Published By: Amazon Web Services     Published Date: Sep 05, 2018
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
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
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
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
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
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Amazon Web Services
Published By: AWS     Published Date: Sep 04, 2018
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.
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AWS
Published By: AWS     Published Date: Sep 04, 2018
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 technology
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AWS
Published By: SAS     Published Date: Aug 28, 2018
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
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SAS
Published By: Splunk     Published Date: Aug 21, 2018
SIEM (security information and event management) software offers a lot of promise, but legacy SIEMs simply can't keep up with the rate and sophistication of today's cyberattacks. Organizations today require access to analytics-driven SIEMs that combine a big data platform that is optimized for machine data with advanced analytics, threat detection, monitoring tools, incident response tools and multiple forms of threat intelligence. Download your complimentary copy of “The Six Essential Capabilities of an Analytics-Driven SIEM” and learn how to dramatically improve your security posture, advanced threat detection and incident response.
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Splunk
Published By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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AWS
Published By: TIBCO Software     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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TIBCO Software
Published By: Hitachi Vantara     Published Date: Aug 14, 2018
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT), big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
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data infrastructure, big data, internet of things
    
Hitachi Vantara
Published By: AWS - ROI DNA     Published Date: Aug 09, 2018
In today's big data digital world, your organization produces large volumes of data with great velocity. Generating value from this data and guiding decision making require quick capture, analysis and action. Without strategies to turn data into insights, the data loses its value and insights become irrelevant. Real-time data inegration and analytics tools play a crucial role in harnessing your data so you can enable business and IT stakeholders to make evidence-based decisions
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AWS - ROI DNA
Published By: IBM     Published Date: Aug 08, 2018
An IBM Cloud configuration completed a big data analytics workload in less time and with greater throughput than an AWS solution
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IBM
Published By: Workday     Published Date: Aug 07, 2018
Today, big data is everywhere. But only companies that know how to realize its true potential are gaining the competitive edge. Join HBR and Eric Siegel, author of Predictive Analytics: Who Will Click, Buy, Lie, or Die, to learn how you can transform data into insight, predict the future, and win.
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Workday
Published By: Hitachi Vantara     Published Date: Aug 02, 2018
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT), big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
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data infrastructure, big data, internet of things
    
Hitachi Vantara
Published By: Amazon Web Services     Published Date: Jul 25, 2018
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.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
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.
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Amazon Web Services
Published By: Hewlett Packard Enterprise     Published Date: Jul 12, 2018
Forward-looking organizations are looking to next-generation all-flash storage platforms to eliminate storage cost and performance barriers. Advancements in all-flash technology have led to remarkable priceperformance improvements in recent years. The latest all-flash solutions from HPE deliver breakthrough economics, speed and simplicity, while improving availability and data durability. All-flash storage can help you reduce TCO and boost the performance of traditional applications as well as accelerate the rollout of new initiatives like IoT, big data and analytics. But moving data to a new storage architecture introduces a variety of organizational and technical challenges.
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Hewlett Packard Enterprise
Published By: IBM     Published Date: Jul 09, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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IBM
Published By: IBM     Published Date: Jul 05, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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IBM
Published By: IBM     Published Date: Jul 05, 2018
Scalable data platforms such as Apache Hadoop offer unparalleled cost benefits and analytical opportunities. IBM helps fully leverage the scale and promise of Hadoop, enabling better results for critical projects and key analytics initiatives. The end-to- end information capabilities of IBM® Information Server let you better understand data and cleanse, monitor, transform and deliver it. IBM also helps bridge the gap between business and IT with improved collaboration. By using Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of- impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time. Since its inception, Information Server has been a massively parallel processing (MPP) platform able to support everything from small to very large data volumes to meet your requirements, regardless of complexity. Information Server can uniquely support th
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IBM
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