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’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.