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Felipe Hoffa
3,202 followers -
From Chile. Working at Google. Living in San Francisco.
From Chile. Working at Google. Living in San Francisco.

3,202 followers
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Felipe Hoffa commented on a post on Blogger.
New easier way of doing this: Dataflow Templates https://cloud.google.com/dataflow/docs/templates/overview

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https://twitter.com/diegoluna_/status/816479341588709377

"""took my father to see Rogue One today. I've wanted to take him for a while. I wanted my Mexican father, with his thick Mexican accent, to experience what it was like to see a hero in a blockbuster film, speak the way he does. And although I wasn't sure if it was going to resonate with him, I took him anyway. When Diego Luna's character came onscreen and started speaking, my dad nudged me and said, 'He has a heavy accent.' I was like, 'Yup.' When the film was over and were walking to the car, he turns to me and says, 'Did you notice that he had an accent?' I said, 'Yeah, dad, just like yours.' Then my dad asked me if the film had made a lot of money. I told it was the second highest grossing film of 2016 despite it only being out for 18 days in 2016 (since New Year just came around). He then asked me if people liked the film. I told him that it had a huge following online and great reviews. He then asked me why Diego Luna hadn't changed his accent and I told him that Diego has openly talked about keeping his accent and how proud he is of it. And my dad was silent for a while and then said, 'And he was the main character.' I said, 'He was.'"""

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I've tried to summarize all the latest updates to #BigQuery in recent months in a blog post. So much to cover. Man, this thing keeps on getting better and better!

#gde #cloudplatform #blogpost

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One of our humongous #Dataflow pipelines sucking away on almost 10TB and over 2.5 billion rows from #GCS. The sinks are writing to #BigQuery where the data will then be smashed even further. With tools like these, you really don't need anything else for ETL and analysis - well except #TensorFlow :-)

#gde #cloudplatform #productfeedback
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What would you do with all the code from #GitHub on #bigquery?
Analyze all the #golang things!!! 🎆🎇🎉

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How do Google BigQuery and Cloud Dataproc stack up to other #bigdata platforms (including #Hadoop, #Spark, ElasticSearch, Presto, and Amazon Redshift)? Big data expert Mark Litwintschik compared them all to find a frontrunner. See the results: https://goo.gl/7FaSo8
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Detailed and informative Cloud comparison article. Conclusion: "Everyone involved unanimously picked GCP. It came down to this: we believe the core technology is better."

One of my favourite quotes: "It's hard to overemphasize how much friendlier GCP pricing is, and how poorly the AWS model works. Amazon's rhetoric for why you should run cloud vs self-hosted infrastructure is the additional agility you get; You can elastically spin up and shut down capacity whenever you want. Reserved Instance pricing effectively negates this advantage, and running all your instances On-Demand is extremely expensive in comparison. So you're left with the decision to over-spend or lock yourself in."

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Google Genomics (http://goo.gl/q4ot5G) has released a dataset that allows you to see how genetic variation is shared among individuals in 26 populations across the world. A Google Dataflow (https://goo.gl/DBRU1W) pipeline computed an analysis on over 5 trillion pairs of variants for each of 31 population groupings of the 2,504 individuals in the 1000 Genomes data set.

To learn more about how Google technologies can be used to to study genomic data, check out the new Linkage Disequilibrium (https://goo.gl/p4Q2bx) data sets at http://goo.gl/A4Od2D and the Jupyter (iPython) notebooks at http://goo.gl/xLh6Sk.
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