Blogs

Charting the data lake: Using the data models with schema-on-read and schema-on-write

Charting the data lake: Using the data models with schema-on-read and schema-on-write

March 14, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
In many cases the data lake can be defined as a super set of repositories of data that includes the traditional data warehouse, complete with traditional relational technology. One significant example of the different components in this broader data lake, is in terms of different approaches to the...
Analytics and the cloud: The Internet of Things

Analytics and the cloud: The Internet of Things

Managing the growth of a connected world to gain new insights

March 7, 2017 | by Steven Lockwood, Executive Information Architect, IBM
This is the second in a series of blogs on analytics and the cloud. We will consider the rise of the Internet of Things (IoT), analytics used on that data and how the cloud can be utilized to drive value out of instrumenting a very wide range of ‘things’.
Fundamentals for sure-fire cloud data warehouse optimization

Fundamentals for sure-fire cloud data warehouse optimization

An interview with James Kobielus

March 2, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
There is a growing need for versatile, hybrid architectures that can combine the best of both data warehousing and big data analytics. The cloud is the perfect solution, because it makes it easier to build a robust data warehouse as a central “hub”, and then add other environments that can be...
Analytics and the cloud: A perfect match

Analytics and the cloud: A perfect match

February 7, 2017 | by Steven Lockwood, Executive Information Architect, IBM
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.

5 key attributes of effective data monetization strategy

February 6, 2017 | by Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM
In cognitive computing era, new revenue generation stream has emerged with data at center of the modern digital business model. One of the key capabilities cognitive computing enables for an organization is the ability to generate additional revenue streams by using data effectively. In the big...
IBM’s big data meetup program approaches a significant milestone

IBM’s big data meetup program approaches a significant milestone

February 3, 2017 | by Nancy Berlin, WW Event Manager, Analytics Platform Services, IBM
IBM’s community of big data developers continues to grow. As our Big Data Developer meetup program moves into its fifth year, this worldwide community of customers, partners and IBM developers is on the verge of enlisting its 100,000th member—when we published this blog, we counted 99,100. 
Stream processing and the IBM Open Platform

Stream processing and the IBM Open Platform

Choosing the right engine for real-time data processing with Hadoop

January 27, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
Analyzing streams of big data in real time can have a big impact on competitive advantage. In a world of bewildering stream processing engine choices, explore the use-case-dependent alternatives that can provide well-suited business outcomes, courtesy of expertise from Roger Rea and Jacques Roy.
Internet of Things: Setting business vision on speed and agility

Internet of Things: Setting business vision on speed and agility

January 27, 2017 | by James Kobielus, Big Data Evangelist, IBM
Internet of Things data, devices and technologies are evolving into a core platform that is expected to impact business flexibility and more. Take a look at some key comprehensive best practices for Internet of Things–enabled application development that can put speed and agility into your business...
10 expert tips to boost agility with Hadoop as a service

10 expert tips to boost agility with Hadoop as a service

January 11, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
In a recent CrowdChat discussion, a group of Hadoop and Spark subject matter experts from the IBM Analytics group discussed using cloud-based Hadoop and Spark services as a lever for business agility. From their contributions we drew ten hot topics and themes for experts in all areas of the big...
How to build an all-purpose big data engine with Hadoop and Spark

How to build an all-purpose big data engine with Hadoop and Spark

Interview with Rohan Vaidyanathan and Niru Anisetti

January 4, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
Some organizations misunderstand the optimized way to use Hadoop and Spark together, primarily because of their complexity. But investing in both technologies enables a broad set of big data analytics and application development use cases. See what Niru Anisetti and Rohan Vaidyanathan have to say...

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