Ed Burns looks back at the World of Watson – an IBM event held recently in Las Vegas – and finds much of interest to followers of AI. At WoW, GM said it would work with IBM to improve drivers’ experiences in traffic and at the gas pump. Staples rolled out a new version of its “Easy Button” that was hooked up to Watson Natural Language Processing and connected to the Staples order system. These and other use cases show IBM can read the tea leaves. Apple’s Siri and Amazon’s Alexa have often been front and center in AI discussions these days, and it appears the consumer angles is not lost on IBM, as GM, Staples and other Watson use cases indicate. “Household names seem to be out ahead in this,” said Burns. Hear more by clicking below to access this edition of the Talking Data podcast.
In some ways, big data these days is a very much a conglomeration of open source application frameworks. It started with Apache Hadoop, but it has come to include Apache Spark, Apache Flink, Apache Kudu and others, with more in the wings. In this edition of the Talking Data podcast, Craig Stedman outlines the pros and cons of such bounty. For IT teams, he said, putting together the pieces can be a daunting task. Also discussed is Hadoop on the cloud. Surprisingly, perhaps, Hadoop configurations by and large evolved in on-premise implementations, rather than on the cloud. That is changing quickly as users opt for easy-to-spin-up pay-as-you-go setups. Learn more. Listen to the podcast.
Oracle 12 C release 2 carries forward the expectations of the company to compete aggressively with Amazon and its Aurora and Redshift databases in the cloud. While it has been used around the industry for time, sharding appears as a new feature in the database. This and other Oracle advances – including better in-memory and JSON support, and improved multitenancy — are discussed in this podcast, as are the engineered system efforts that set the stage for Oracle’s cloud incursions.
While there is much focus on building out new data-oriented applications using open-source data frameworks, some development managers still opt for something more turnkey. This podcast looks at the new tenor of the age-old build vs. buy conundrum from both sides. Actually, there are more than two sides, because ‘rent’ is becoming part of the equation. Just because you can do it, doesn’t mean you should, right?
A visit to the MIT Chief Data Officer and Information Quality Symposium was a springboard for musing upon a leadership role that emerged in the wake of the 2008 Wall Street debacle. Back then, the question was ‘where is your data?’ The question now is ‘where are you going with your data, and can a CDO take you there?’ Listen to this podcast.
Many businesses have yet to fully capitalize on their investments in big data and analytics technologies.
That’s one of the main takeaways from a new report from the Economist Intelligence Unit and ZS Associates. Report authors surveyed businesses across the country to gauge their success with analytics technologies and many of the responses were surprising.
Analytics tools are now seen as almost a no-brainer where the value seems fairly obvious. To be sure, when implemented and used wisely, the tools can be game changers for enterprises. But the report suggests that few are reaping the full promised benefits. Take a listen to this interview with ZS associate principal Dan Wetherill to hear more about why some businesses are struggling.
The opportunity to celebrate 50 years of publication at Computer Weekly proved also the occasion for some musings on the big picture of British computing over the years, when CW’s Brian McKenna joined Ed Burns and Jack Vaughan for the Talking Data podcast. The discussion turned to the role of new technologies like Hadoop in London’s frenetic financial services industry. It included a consideration of the influence that the vaunted Bletchley Park development center had on the course of U.K technology.
While adoption of Spark continues to grow, this year’s Spark Summit highlighted some of the ways in which the big data processing engine is still a work in progress. In particular, it’s stream analytics processing engine continues to struggle with a few hiccups that can limit its utility. That said, presenters still by and large believe Spark is the best option for streaming analytics, as other tools are even further behind in maturity.
The Summit also featured comments from Doug Cutting, the originator of Hadoop, on where the open source data processing industry is headed and what might be next. Take a listen to this edition of Talking Data to hear more about these topics and more from the 2016 Spark Summit.
The development of AI had been stalled out for years, but all of a sudden there’s been a huge surge in interest. Why now? The answer is big data. It turns out big data was the missing knowledge bank that was needed to make machines truly intelligent. But now that enterprises have stashed huge volumes of data, they are starting to unleash learning algorithms on it, creating large-scale learning opportunities.
Kafka and Spark Streaming are arising as central parts of new real time data architecture. In this podcast, the Talking Data crew hooks up with Jay Kreps and Doug Cutting, of Confluent and Cloudera, respectively, who outline some of big data streaming’s pertinent facts.