Blogs

Driving in-memory data warehousing into the big data cloud

Driving in-memory data warehousing into the big data cloud

July 27, 2015 | by James Kobielus, Big Data Evangelist, IBM
The modern data warehouse (DW) lives in the cloud and is rapidly evolving into an in-memory platform for high-performance in-database analytics. As evidenced by IBM’s launch last year of dashDB and the latest enhancement release to the service, the fully managed in-memory cloud DW is already a...
Merging datasets using graph analytics

Merging datasets using graph analytics

July 27, 2015 | by Daniel Darabos, Software Engineer, Lynx Analytics
Using Apache Spark, we built an end-to-end fingerprinting tool to identify matching candidates among two independent data sets, calculating a similarity score and solving the stable marriage problem. Integration with a graphical environment not only saved us time, but also allowed us to easily...

The secret reason why IBM is dominating the cloud

July 24, 2015 | by Debra Pesek, Portfolio Marketing Manager – Performance Management, IBM
Which company is seeing massive growth and increasing dominance in cloud services? You’d be surprised.

How to deliver actionable insights from data streams

July 22, 2015 | by Avi Patwardhan, Product Marketing Manager
Stream computing makes dependably analyzing continuous data streams from sensors, social media or mobile device data efficient and effective. Even with multiple uses for big data in every industry, the end goal for organizations is to take advantage of stream computing to capture previously...

How Spark is tuning up the logical data warehouse

July 21, 2015 | by James Kobielus, Big Data Evangelist, IBM
Apache Spark will become a core technology in the logical data warehouse (LDW), and its sweet spot is as the workbench of choice for data scientists who interactively and iteratively explore, build and tune statistical models for machine learning, graph and streaming analytics.
Analyzing time series data with stream processing and machine learning

Analyzing time series data with stream processing and machine learning

July 21, 2015 | by Jim Sharpe, President., Sharpe Engineering Inc.
Time series data can contain highly valuable insights—if organizations can detect and classify the events within it. An approach that combines stream processing and machine learning holds the key to analyzing large, fast data streams.

How to settle insurance claims with the speed of a Formula One pit stop

July 21, 2015 | by Kimberly Trimble, Social Business Manager, IBM
In claims resolution, speed and efficiency are paramount. Just as drivers choose the optimal racing lines to put together the fastest lap during qualifying, insurers also must optimize their claims resolution process to avoid being overtaken by a competitor. Don’t be the insurer who is left in a...
Answers to your questions about becoming a digital business

Answers to your questions about becoming a digital business

July 20, 2015 | by Louis Cherian, Digital Marketing Manager, Big Data & Analytics, IBM
How do you define a digital business? We organized a recent CrowdChat to discuss advantages, disadvantages, infrastructure needs, analytics, security and other key concerns for digital business. The participants, who included industry experts, also talked about how organizations need to change to...
Context is key to deriving analytic value with Hadoop

Context is key to deriving analytic value with Hadoop

July 15, 2015 | by David Birmingham, Senior Principal Consultant, Brightlight Business Analytics, a division of Sirius Computer Solutions
Big data without context is pretty much useless, especially when that context can fluctuate so widely—which is why the role of Hadoop in creating accurate analytics is crucial for deriving value from big data.

5 disruptive technologies that are challenging the traditional banking model

July 14, 2015 | by Kimberly Trimble, Social Business Manager, IBM
Something interesting is happening, and it is causing banks and other financial services to rethink how they are doing business. Customers are embracing mobile and digital channels more and more each year, and to be successful, companies must deliver customer engagement via those channels.

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