Category Archives: Big Data

Big Data in the Cloud at Google I/O

Last week was a great party for the entire Google developer family, including Google Cloud Platform. And within the Cloud Platform, Big Data processing services. Which is where my focus has been in the almost two years I’ve been at Google.

It started with a bang, when our fearless leader Urs unveiled Cloud Dataflow in the keynote. Supported by a very timely demo (streaming analytics for a World Cup game) by my colleague Eric.

After the keynote, we had three live sessions:

In “Big Data, the Cloud Way“, I gave an overview of the main large-scale data processing services on Google Cloud:

  • Cloud Pub/Sub, a newly-announced service which provides reliable, many-to-many, asynchronous messaging,
  • the aforementioned Cloud Dataflow, to implement data processing pipelines which can run either in streaming or batch mode,
  • BigQuery, an existing service for large-scale SQL-based data processing at interactive speed, and
  • support for Hadoop and Spark, making it very easy to deploy and use them “the Cloud Way”, well integrated with other storage and processing services of Google Cloud Platform.

The next day, in “The Dawn of Fast Data“, Marwa and Reuven described Cloud Dataflow in a lot more details, including code samples. They showed how to easily construct a streaming pipeline which keeps a constantly-updated lookup table of most popular Twitter hashtags for a given prefix. They also explained how Cloud Dataflow builds on over a decade of data processing innovation at Google to optimize processing pipelines and free users from the burden of deploying, configuring, tuning and managing the needed infrastructure. Just like Cloud Pub/Sub and BigQuery do for event handling and SQL analytics, respectively.

Later that afternoon, Felipe and Jordan showed how to build predictive models in “Predicting the future with the Google Cloud Platform“.

We had also prepared some recorded short presentations. To learn more about how easy and efficient it is to use Hadoop and Spark on Google Cloud Platform, you should listen to Dennis in “Open Source Data Analytics“. To learn more about block storage options (including SSD, both local and remote), listen to Jay in “Optimizing disk I/O in the cloud“.

It was gratifying to see well-informed people recognize the importance of these announcement and partners understand how this will benefit their customers. As well as some good press coverage.

It’s liberating to now be able to talk freely about recent progress on our quest to equip Google Cloud users with easy to use data processing tools. Everyone can benefit from Google’s experience making developers productive while efficiently processing data at large scale. With great power comes great productivity.

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Filed under Big Data, BigQuery, Cloud Computing, Cloud Dataflow, Everything, Google Cloud Platform, Implementation, Open source, Query, Spark, Tech, Utility computing

Big Data career adviser says you should be a… Big Data analyst

LinkedIn CEO Jeff Weiner wrote an interesting post on “the future of LinkedIn and the economic graph“. There’s a lot to like about his vision. The part about making education and career choices better informed by data especially resonates with me:

With the existence of an economic graph, we could look at where the jobs are in any given locality, identify the fastest growing jobs in that area, the skills required to obtain those jobs, the skills of the existing aggregate workforce there, and then quantify the size of the gap. Even more importantly, we could then provide a feed of that data to local vocational training facilities, junior colleges, etc. so they could develop a just-in-time curriculum that provides local job seekers the skills they need to obtain the jobs that are and will be, and not just the jobs that once were.

I consider myself very lucky. I happened to like computers and enjoy programming them. This eventually lead me to an engineering degree, a specialization in Computer Science and a very enjoyable career in an attractive industry. I could have been similarly attracted by other domains which would have been unlikely to give me such great professional options. Not everyone is so lucky, and better data could help make better career and education choices. The benefits, both at the individual and societal levels, could be immense.

Of course, like for every Big Data example, you can’t expect a crystal ball either. It’s unlikely that the “economic graph” for France in 1994 would have told me: “this would be a good time to install Linux Slackware, learn Python and write your first CGI script”. It’s also debatable whether that “economic graph” would have been able to avoid one of the worst talent waste of recent time, when too many science and engineering graduates went into banking. The “economic graph” might actually have encouraged that.

But, even under moderate expectations, there is a lot of potential for better informed education and career decision (both on the part of the training profession and the students themselves) and I am glad that LinkedIn is going after that. Along with the choice of a life partner (and other companies are after that problem), this is maybe the most important and least informed decision people will make in their lifetime.

Jeff Weiner also made proclamation of openness in that same article:

Once realized, we then want to get out of the way and allow all of the nodes on this network to connect seamlessly by removing as much friction as possible and allowing all forms of capital, e.g. working capital, intellectual capital, and human capital, to flow to where it can best be leveraged.

I’m naturally suspicious of such claims. And a few hours later, I get a nice email from LinkedIn, announcing that as of tomorrow they are dropping the “blog link” application which, as far as I can tell, fetches recent posts form my blog and includes them on my LinkedIn profile. Seems to me that this was a nice and easy way to “allow all of the nodes on this network to connect seamlessly by removing as much friction as possible”…

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Filed under Big Data, Everything, Linked Data, People, Social networks