Sharing and Deploying Data Science with KNIME Server - February 2019

KNIME

You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems.
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Spotlight

Large-scale data sets are nothing new. After all, before the term “Big Data” was coined, airline reservation systems tracked millions of flight segments and bookings, and phone companies kept billions of call detail records. But now it is possible for small companies and individuals to access to the same massive computational and storage resources using inexpensive commodity hardware and the cloud. Central to this data ubiquity story is the open-source distributed computational framework called Hadoop. Created at Yahoo, based on Google’s MapReduce and Google File System publications, Hadoop allows large data sets to be stored and parallel-processed by spreading files across a large number of small commodity servers. Hadoop is now an Apache project with a large following in both the commercial and open-source software communities.

OTHER ON-DEMAND WEBINARS

Transforming the Database: Critical Innovations for Performance at Scale

Your team is serious about ensuring database performance at scale. But legacy NoSQL technology could be eroding the impact of your achievements. Following best practices for efficient data modeling, query optimization and observability is fundamental. But their power can be limited – or enhanced – by specific database capabilities. Often-overlooked database innovations can serve as a force multiplier, paving a much smoother path to speed at scale (e.g., millions of read/write operations and millisecond P99 response).
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Setting Up a Data Integration Pipeline for Repeatable Analytics Delivery

GoodData Corporation

As part of its platform, GoodData provides a fault-tolerant, high performance and scalable system for data integration. While built for large-scale analytic applications, it is a metadata-driven, modular system that can start small and grow with your business. In this session, Cameron demonstrates how to set up and schedule regular data extraction from SQL databases and other sources. He also covers some of the issues requiring attention in data extraction such as data merging and incremental loads. A future session will cover transformations and data enrichment along with data distribution.
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COMPETE WITH THE GIANTS 7 ELEMENTS OF A DATA STRATEGY

analytics8.com

For most companies, data is viewed as a problem instead of an asset. Data is often stuck in systems that dont talk to each other, manual processes affect data quality, and analytics tools arent providing clear insights. But those companies who use their data to drive business strategy are out-performing their competitors. To be more competitive in any industry, you must take advantage of the ever-growing amount of available data and that starts with a Data Strategy. A documented roadmap that clearly defines company goals and the specifics on how to get there will put you on the path towards data driven decision making.
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Deliver Big Data and Databases as-a-Service on Kubernetes

Robin Systems, Inc

You need greater efficiency, agility, scalability, and cost-effectiveness from your IT infrastructure. Legacy storage solutions just aren’t cutting it anymore! What many IT Organizations don’t know is that there are many different solutions available to solve these IT challenges! The one thing that all businesses have in common is that they want and need IT solutions to make their lives easier, more efficient, and more affordable, particularly as digital transformation efforts take center stage. Converged, hyperconverged, composable, and other integrated platforms (collectively, integrated systems) all have the potential to accomplish these goals which can allow the IT organization to focus their efforts more on business outcomes rather than hardware.
Watch Now

Spotlight

Large-scale data sets are nothing new. After all, before the term “Big Data” was coined, airline reservation systems tracked millions of flight segments and bookings, and phone companies kept billions of call detail records. But now it is possible for small companies and individuals to access to the same massive computational and storage resources using inexpensive commodity hardware and the cloud. Central to this data ubiquity story is the open-source distributed computational framework called Hadoop. Created at Yahoo, based on Google’s MapReduce and Google File System publications, Hadoop allows large data sets to be stored and parallel-processed by spreading files across a large number of small commodity servers. Hadoop is now an Apache project with a large following in both the commercial and open-source software communities.

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