apache

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Published By: Pure Storage     Published Date: Jul 03, 2019
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: Pure Storage     Published Date: Jul 03, 2019
University of California-Berkeley researchers employ the latest tools built on Apache Spark to accelerate DNA sequencing in pursuit of precision medicine. Adding Pure FlashBlade™ from Pure Storage® has significantly reduced the time needed to sequence data-intensive DNA samples and analyze results.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Apache® Spark™ has become one of the most rapidly adopted open source platforms in history. Demand is predicted to grow at a compound annual rate of 67% per year between 2017 and 2020, with the cumulative Spark market valued at more than $9 billion during that period, according to the research firm MarketAnalysis.com.
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Pure Storage
Published By: Red Hat APAC     Published Date: Jun 27, 2019
Red Hat® JBoss® Fuse is a lightweight integration platform that reduces the pain of connecting applications, services, processes, and devices for comprehensive and efficient solutions. JBoss Fuse includes the popular and versatile Apache Camel project, an implementation of the most commonlyused enterprise integration patterns. With integration patterns and more than 150 connectors ready to use, JBoss Fuse supports integration across the extended enterprise—including applications and services on-premise, on mobile devices, or in the cloud. JBoss Fuse is complemented by Red Hat JBoss Developer Studio for easier development of integration solutions and Red Hat JBoss Operations Networkfor monitoring of deployed solutions.
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Red Hat APAC
Published By: Datastax     Published Date: May 20, 2019
DataStax Enterprise and Apache Kafka are designed specifically to fit the needs of modern, next-generation businesses. With DataStax Enterprise (DSE) providing the blazing fast, highly-available hybrid cloud data layer and Apache Kafka™ detangling the web of complex architectures via its distributed streaming attributes, these two form a perfect match for event-driven enterprise architectures.
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Datastax
Published By: Datastax     Published Date: May 20, 2019
Apache Cassandra™ comes with the typical benefits of any NoSQL database, and much more. When enterprises need something easily scalable and ready for today’s hybrid cloud environments, it’s hard to find a database better suited for the job than Cassandra. From performance to availability to hybrid cloud readiness, this ebook explains the five main benefits of Cassandra.
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Datastax
Published By: Datastax     Published Date: May 20, 2019
Data management challenges have evolved drastically over the last decade, leading most companies to rethink how they manage their data. The need for more powerful and far more flexible databases resulted in the birth of the NoSQL database Apache Cassandra™. Read this white paper to learn how Cassandra has evolved and how it works.
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Datastax
Published By: Pure Storage     Published Date: Apr 10, 2019
Pure Storage, a pioneer in block-based flash arrays, has developed a technology called FlashBlade, designed specifically for file and object storage environments. With FlashBlade, IT teams now have a simple-to-manage shared storage solution that delivers the performance and capacity needed to bring Spark deployments on premise. To help gain a deeper understanding of the storage challenges related to Spark and how FlashBlade addresses them, Brian Gold of Pure Storage sat down with veteran technology journalist Al Perlman of TechTarget for a far-reaching discussion on Spark trends and opportunities.
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Pure Storage
Published By: Datastax     Published Date: Apr 08, 2019
For decades, organizations relied on traditional relational database management systems (RDBMS) to organize, store, and analyze their data. But then Facebook came along, and an RDBMS was suddenly not quite enough. The social giant needed a powerful database solution for its Inbox Search feature, and Apache Cassandra—a distributed NoSQL database—was born. Released as an open source project in July 2008, Cassandra—named after the mythological prophet who famously put a curse on an oracle—became an Apache Incubator project in March 2009. It graduated to a top-level project in February 2010
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Datastax
Published By: Attunity     Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
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data streaming, kafka, metadata integration, metadata, data streaming, apache kafka, data integration, data analytics, database transactions, streaming environments, real-time data replication, data configuration
    
Attunity
Published By: SAS     Published Date: Jan 04, 2019
How can you open your analytics program to all types of programming languages and all levels of users? And how can you ensure consistency across your models and your resulting actions no matter where they initiate in the company? With today’s analytics technologies, the conversation about open analytics and commerical analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics technology systems within your organization. As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Within that cycle, there’s a role for open source and commercial analytics. For example, machine learning algorithms can be developed in SAS or Python, then deployed in real-time data streams within SAS Event Stream Processing, while also integrating with open systems through Java and C APIs, RESTful web services, Apache Kafka, HDFS and more.
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SAS
Published By: Pure Storage     Published Date: Dec 05, 2018
With the growth of unstructured data and the challenges of modern workloads such as Apache Spark™, IT teams have seen a clear need during the past few years for a new type of all-flash storage solution, one that has been designed specifically for users requiring high levels of performance in file- and object-based environments. With FlashBlade™, it addresses performance challenges in Spark environments by delivering the consistent performance of all-flash storage with no caching or tiering, as well as fast metadata operations and instant metadata queries.
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Pure Storage
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
Tags : 
    
Pure Storage
Published By: StreamSets     Published Date: Sep 24, 2018
If you’ve ever built real-time data pipelines or streaming applications, you know how useful the Apache Kafka™ distributed streaming platform can be. Then again, you’ve also probably bumped up against the challenges of working with Kafka. If you’re new to Kafka, or ready to simplify your implementation, we present common challenges you may be facing and five ways that StreamSets can make your efforts much more efficient and reliable
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apache, kafka, steam, sets, data
    
StreamSets
Published By: StreamSets     Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
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replatforming, age, data, lake, apache, hadoop
    
StreamSets
Published By: Splunk     Published Date: Sep 10, 2018
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential. Download this white paper to learn: *How organizations can answer critical questions that have been impeding business success *How the financial services industry can make great strides in security, compliance and IT *Common machine data sources in financial services firms
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Splunk     Published Date: Sep 10, 2018
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers. For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy. Download this report to learn: How to reduce monitoring blind spots when creating an ITOM strategy How to address ITOM requirements across IT and non-IT groups Distinct layers across ITOM Potential functionality gaps with domain-specific products
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Datastax     Published Date: Aug 28, 2018
Today’s enterprises continue to differentiate themselves via their cloud applications. But differentiation is getting harder and harder to accomplish. To stand out, you need a flexible, nimble, scalable, always-on data platform that can deliver powerful, game-changing, customer-facing applications. Read this white paper to learn how DataStax Enterprise delivers comprehensive data management through a secure, operationally simple platform built on the best distribution of Apache Cassandra™.
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Datastax
Published By: Datastax     Published Date: Aug 28, 2018
Today’s companies require serious POWER at the data layer if they’re going to thrive in the Right-Now Economy. That’s why DataStax upped the ante with the latest version of its flagship product: DataStax Enterprise (DSE) 6. DSE 6 offers double the performance of DataStax Enterprise 5.1 and has now been independently verified as being at least twice as fast as open source Apache Cassandra. Read this white paper to learn all about DSE 6 performance improvements.
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Datastax
Published By: Datastax     Published Date: Aug 28, 2018
With DataStax Enterprise 6 (DSE 6), we upped the bar substantially for us, our partners, our customers, and our competitors. We also came out and said that DSE 6 was twice as fast as open source Apache Cassandra™, and now, we have a third-party validation of this claim. Read this benchmark report from zData to get the results of their test of DSE 6 against Cassandra, for which they ran a different series of workloads on an AWS-built cluster.
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Datastax
Published By: Datastax     Published Date: Aug 27, 2018
With DataStax Enterprise 6 (DSE 6), we upped the bar substantially for us, our partners, our customers, and our competitors. We also came out and said that DSE 6 was twice as fast as open source Apache Cassandra™, and now, we have a third-party validation of this claim. Read this benchmark report from zData to get the results of their test of DSE 6 against Cassandra, for which they ran a different series of workloads on an AWS-built cluster.
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Datastax
Published By: Datastax     Published Date: Aug 27, 2018
Today’s enterprises continue to differentiate themselves via their cloud applications. But differentiation is getting harder and harder to accomplish. To stand out, you need a flexible, nimble, scalable, always-on data platform that can deliver powerful, game-changing, customer-facing applications. Read this white paper to learn how DataStax Enterprise delivers comprehensive data management through a secure, operationally simple platform built on the best distribution of Apache Cassandra™.
Tags : 
    
Datastax
Published By: Datastax     Published Date: Aug 27, 2018
Today’s companies require serious POWER at the data layer if they’re going to thrive in the Right-Now Economy. That’s why DataStax upped the ante with the latest version of its flagship product: DataStax Enterprise (DSE) 6. DSE 6 offers double the performance of DataStax Enterprise 5.1 and has now been independently verified as being at least twice as fast as open source Apache Cassandra. Read this white paper to learn all about DSE 6 performance improvements.
Tags : 
    
Datastax
Published By: Datastax     Published Date: Aug 27, 2018
With DataStax Enterprise 6 (DSE 6), we upped the bar substantially for us, our partners, our customers, and our competitors. We also came out and said that DSE 6 was twice as fast as open source Apache Cassandra™, and now, we have a third-party validation of this claim. Read this benchmark report from zData to get the results of their test of DSE 6 against Cassandra, for which they ran a different series of workloads on an AWS-built cluster.
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Datastax
Published By: Splunk     Published Date: Aug 17, 2018
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs. Download “The Essential Guide to Infrastructure Machine Data” for: The benefits of machine data for network, remote, web, cloud and server monitoring IT infrastructure monitoring data sources to include in your machine data platform Machine data best practices
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
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