Apache Spark has become a critical tool for all types of businesses across all industries. It is enabling organizations to leverage the power of analytics to drive innovation and create new business models.
The availability of public cloud services, particularly Amazon Web Services, has been an important factor in fueling the growth of Spark. However, IT organizations and Spark users are beginning to run up against limitations in relying on the public cloud—namely control, cost and performance.
Published By: IBM APAC
Published Date: Aug 25, 2017
Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science, but the barrier to entry has been high, not only in cost, but also in the need for specialized talent.
IBM Analytics for Apache Spark for Bluemix is an open-source cluster computing framework with in-memory processing to speed analytic applications up to 100 times faster compared to other technologies on the market today. Optimized for extremely fast and large scale data processing-you can easily perform big data analysis from one application.
Learn how to create powerful analytic apps with IBM Cloudant, dashDB and Apache Spark. This presentation will contain demos of real-life use cases e.g. machine learning predictive analytics, Graph-parallel computation and more.
Data matters more than ever to business success. But value does not come from data alone. Rather, it comes from the insights enabled by data. No matter what your role is, or where you are in your data journey, you are looking for ways to drive innovation.
FREE O'REILLY EBOOK: BUILDING REAL-TIME DATA PIPELINES Unifying Applications and Analytics with In-Memory Architectures You'll Learn:
- How to use Apache Kafka and Spark to build real-time data pipelines - How to use in-memory database management systems for real-time analytics
- Top architectures for transitioning from data silos to real-time processing
- Steps for getting to real-time operational systems - Considerations for choosing the best deployment option
Watch to learn how an enterprise-grade, multi-tenant solution can help you deploy Spark in a production environment to take advantage of
· Faster time-to-results for big data analytics
· Simplified deployment and management
· Increased utilization of hardware resources"
Apache Spark hit the scene in 2014 and has grown to be the most popular software project in the history of Open Source. Attend this webinar and learn more about;
-What is Apache Spark?
-Why, is it so popular?
-Why is it important to you and your organization?
Apache Spark is allowing companies to drive innovative ways to compete using one of the most valuable assets in the 21st century, Data! Apache Spark is the fastest growing framework for powering Big Data Analytics today and for the future. Register to attend this webcast and learn more.
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization.
This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
NoSQL databases and Apache Spark are a potent combination for rapid
integration, transformation and analysis of all kinds of business data.
With its data syncing and analytics capabilities, IBM Cloudant offers unique
advantages as a NoSQL database for many Spark use cases.
IT decision-makers, data scientists and developers need to know how and when to
apply these technologies most effectively.
IBM can offer a host of resources and tools to help your organization gain value
from Cloudant and Spark quickly, and with minimal up-front investment.
Life revolves around prediction—for example, the route you take to get to work, whether to go on a second date, or whether or not to keep reading this sentence are all forms of prediction. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
Big data is fueling a new economy—one based on insight. How can you create the valuable insights that are the currency for the new economy while controlling complexity? Apache Spark might be the answer.
Machine learning can help us plan our lives so we can increase our likelihood of success. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
As most companies now realize, analytics is increasingly more of an integral part of their day-to-day business operations. In a recent survey by a global research firm, 80% of CIOs stated that transition from backward-looking, passive analysis must shift to forward-looking predictive analytics. The challenge is that many analytic solutions are aligned to a specific platform, tied to inflexible programming models and requiring vast data movement. In this webcast, Forrester and experts from IBM will highlight how technology like Apache Spark on z/OS allows businesses to extract deep customer insight without the cost, latency and security risks of data movement throughout the enterprise.
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