Posted by Urs Hölzle, Senior Vice President

(Cross-posted on the Google Cloud Platform Blog)

Editor's note: Tune in to Google Cloud Platform Live for more information about our announcements. And join us during our 27-city Google Cloud Platform Roadshow which kicks off in Paris on April 7.

Today, at Google Cloud Platform Live we’re introducing the next set of improvements to Cloud Platform: lower and simpler pricing, cloud-based DevOps tooling, Managed Virtual Machines (VM) for App Engine, real-time Big Data analytics with Google BigQuery, and more.

Industry-leading, simplified pricing
The original promise of cloud computing was simple: virtualize hardware, pay only for what you use, with no upfront capital expenditures and lower prices than on-premise solutions. But pricing hasn’t followed Moore's Law: over the past five years, hardware costs improved by 20-30% annually but public cloud prices fell at just 8% per year.

We think cloud pricing should track Moore’s Law, so we’re simplifying and reducing prices for our various on-demand, pay-as-you-go services by 30-85%:

• Compute Engine reduced by 32% across all sizes, regions, and classes.
• App Engine pricing simplified, with significant reductions in database operations and front-end compute instances.
• Cloud Storage is now priced at a consistent 2.6 cents per GB. That’s roughly 68% less for most customers.
• Google BigQuery on-demand prices reduced by 85%.

Sustained-Use discounts
In addition to lower on-demand prices, you’ll save even more money with Sustained-Use Discounts for steady-state workloads. Discounts start automatically when you use a VM for over 25% of the month. When you use a VM for an entire month, you save an additional 30% over the new on-demand prices, for a total reduction of 53% over our original prices.

Sustained-Use Discounts automatically reward users who run VMs for over 25% of any calendar month

With our new pricing and sustained use discounts, you get the best performance at the lowest price in the industry. No upfront payments, no lock-in, and no need to predict future use.

Making developers more productive in the cloud
We’re also introducing features that make development more productive:

• Build, test, and release in the cloud, with minimal setup or changes to your workflow. Simply commit a change with git and we’ll run a clean build and all unit tests.
• Aggregated logs across all your instances, with filtering and search tools.
• Detailed stack traces for bugs, with one-click access to the exact version of the code that caused the issue. You can even make small code changes right in the browser.

We’re working on even more features to ensure that our platform is the most productive place for developers. Stay tuned.

Introducing Managed Virtual Machines
You shouldn't have to choose between the flexibility of VMs and the auto-management and scaling provided by App Engine. Managed VMs let you run any binary inside a VM and turn it into a part of your App Engine app with just a few lines of code. App Engine will automatically manage these VMs for you.

Expanded Compute Engine operating system support
We now support Windows Server 2008 R2 on Compute Engine in limited preview and Red Hat Enterprise Linux and SUSE Linux Enterprise Server are now available to everyone.

Real-Time Big Data
BigQuery lets you run interactive SQL queries against datasets of any size in seconds using a fully managed service, with no setup and no configuration. Starting today, with BigQuery Streaming, you can ingest 100,000 records per second per table with near-instant updates, so you can analyze massive data streams in real time. Yet, BigQuery is very affordable: on-demand queries now only cost $5 per TB and 5 GB/sec reserved query capacity starts at $20,000/month, 75% lower than other providers.

Conclusion
This is an exciting time to be a developer and build apps for a global audience. Today we’ve focused a lot on productivity, making it easier to build and test in the cloud, using the tools you’re already familiar with. Managed VMs give you the freedom to combine flexible VMs with the auto-management of App Engine. BigQuery allows big data analysis to just work, at any scale.

And on top of all of that, we’re making it more affordable than it’s ever been before, reintroducing Moore’s Law to the cloud: the cost of virtualized hardware should fall in line with the cost of the underlying real hardware. And you automatically get discounts for sustained use with no long-term contracts, no lock-in, and no upfront costs, so you get the best price and the best performance without needing a PhD in Finance.

We’ve made a lot of progress this first quarter and you’ll hear even more at Google I/O in June.

(Cross-posted on the Google Cloud Platform blog)

Editor's note: Today we hear from Daniel Hasselberg, co-founder and chief executive officer of mobile game development company, MAG Interactive, based in Stockholm, Sweden. MAG Interactive produces some of the most popular games in the world, including Ruzzle, which has more than 45 million players in 142 different countries.

When we launched our word game Ruzzle in 2012, we had no idea it would become an international sensation almost overnight. We initially promoted the game only to our family and friends, but within two weeks of our launch, Ruzzle was the No.1 game on the Swedish App Store.

I believe if we hadn’t used Google App Engine to build the backend of Ruzzle, we wouldn’t have been able to scale fast enough with our own servers, which would have killed the app in the marketplace. There were about a million downloads of Ruzzle per month in the Nordic region, Holland, Spain and Italy through 2012. As we refined the game’s social integration through channels like Facebook and Twitter, we grew rapidly in Italy and the United States. In 2013, Ruzzle became the No. 1 game download on Google Play and the App Store in Italy, Sweden, the United States and many other countries.

Things were especially crazy at the end of last year. We were seeing about 700,000 new players each day from December 2012 through January 2013. We added 20 million users in a single month! It was incredible to see App Engine scale – and just keep on working – as we grew from about 5 million players to 25 million players in just a few weeks.

Our decision to use App Engine as the platform for Ruzzle and our new game, QuizCross, was strategic. Some of us at MAG Interactive helped develop the server platform for one of the most popular music download services in the Nordic region, so we knew about the challenges of having to scale quickly. While we didn’t anticipate Ruzzle’s popularity, we did recognize even before creating the game that we could face scaling problems if we were successful. So we decided from day one to use a cloud solution.

We looked at Amazon’s platform but preferred Google’s approach to cloud solutions. Google’s scalability was an important factor in our decision, but we also appreciated the company’s transparent pricing. The more efficient we became with App Engine, the less we paid.

The Google Cloud Platform team has been great to work with, as well. They are very supportive and appreciate our feedback. The technical support experts at Google are amazing, too – very hands-on. They know the platform extremely well and can help us work through any challenge.

We’re also using Google BigQuery for business intelligence. We track millions of events in the game every day so we know what users are doing – or not doing – and how we should improve the experience. We really like that we can throw enormous amounts of data at BigQuery, and it still performs. It only takes a few seconds to get results, and there are no scaling issues. It’s also easy to use. We have just one data analyst doing all the work with BigQuery but could probably use more people. If there are a few brilliant data mining experts out there who can imagine a future in Stockholm, please give us a call!

One thing we’ve learned from our BigQuery analysis is that the more users play Ruzzle, the more they improve their skills. New players typically find about 18 words in the two-minute time frame they’re given. After they play 100 games, they can find about 50 words, on average. I think that tracking player improvement is what keeps people playing and has helped to make Ruzzle so popular.

BigQuery offers our company a lot of insight into the use of our games and how we can improve them. We’re looking forward to expanding our relationship with Google as App Engine and Cloud Platform evolves.

Posted by Meghdutt Brahmachari, Product Marketing Manager, Google Enterprise

When it’s raining out, do people’s shopping habits change? Those are the kind of questions the team at Interactions Marketing, working with Tableau Software, think about when analyzing massive data sets on behalf of retailers. In a highly competitive market, retailers need the edge they can gain from business data – and with the analysis they can generate using Google BigQuery. By analyzing these data sets, you can find what Interactions Marketing calls “unexpected insights,” which help businesses make predictions that can improve sales. For example, they look at how external factors like the weather will affect retail sales.

Find out more about the value of Big Data and unexpected insights for retailers – and how Google BigQuery supports these analytics projects – in our Hangout On Air on Thursday, September 26, at 9 a.m. PT. Giovanni DeMeo, Vice President of Global Marketing and Analytics for Interactions Marketing; Paul Lilford, Global Director for Technology Partners at Tableau Software; and Daniel Powers, Director of Sales for Google Cloud Platform will explain how retailers can understand their businesses better and boost success:

• How can unexpected insights help retailers attract and keep customers?
• What are the pressures on retailers to glean insights from their data?
• How does cloud storage make Big Data analysis possible?
• How can you make it easier to visualize and understand your data?

If you missed our previous Hangout On Air with Speedway Motors, the world’s largest manufacturer of specialty hot-rodding and racing products, you can catch up on the recording here.

RSVP for the Interactions Marketing/Tableau Software Hangout On Air, and participate in the Q&A by posting your questions on Google+ or Twitter using the hashtag #GoneGoogle.

Posted by Ju-kay Kwek, BigQuery Product Manager

Today, we live in a world where businesses are generating large amounts of real-time data from web applications that serve millions of users, online sales transactions, or customer activity created by an explosion of connected devices. Being able to react quickly to changes in the data being generated is critical to remain competitive. At the same time, businesses are gathering, storing and analyzing data -- sometimes 100s of gigabytes per day -- using legacy systems that struggle to keep up.

We built Google BigQuery to enable businesses to tackle this problem without having to invest in costly and complex infrastructure. And today this gets even easier with two key new features:

• Real-time data streaming: you can now stream events row-by-row into BigQuery via a simple new API call. This enables you to store data as it comes in, rather than building and maintaining systems just to cache and upload in batches. The best part? The new data is available for querying instantaneously. Streaming ingestion is free for an introductory period until January 1st, 2014. After that it will be billed at a flat rate of 1 cent per 10,000 rows inserted. The existing batch-based ingestion will continue to be free.
• Query portions of a table: you can now query a specific subset of a table using a simple new @<t> that we call a “table decorator” in your SQL statements. Though restricted to data inserted within the last 24 hours, this capability provides significant benefits beyond just cost efficiency -- for example, in conjunction with real-time data streaming, you can now use table decorators to monitor the last 30 minutes of user activity after a new change is pushed to your application.

In addition to these features, we’ve also expanded BigQuery’s window functions to include SUM and COUNT -- statistical capabilities that many customers have asked for -- as well as regular analytic functions for calculating Correlation and Standard Deviation.

And to make the entire querying experience smoother, the BigQuery user interface has also received numerous productivity-enhancing updates. These include an expanding information panel when clicking on a query, as well as action buttons at the bottom of the query box to make it easier to edit, run, save, and show results.

You can get details about these new capabilities and examples from our Developer Blog and in our updated product documentation.

Whether it’s for capturing streams of application event logging or real-time user behavior analysis, we can’t wait to hear how you’re using BigQuery’s new features. And we hope you’ll share with our community via the #BigQuery tag on Google+.

Posted by Ju-kay Kwek, BigQuery Product Manager

We know that today, more than ever, businesses need ways to store and rapidly analyze vast amounts of data and are looking for ways to accomplish this without huge infrastructure investments. To help make this possible, recent BigQuery features include the ability to join across multi-terabyte tables, and the ability to connect popular analysis tools such as Tableau®, BIME® and Excel®. In the past few months we’ve seen several interesting use cases enabled -- Shutterfly improving their customers’ experience, Gamesys understanding complex user behaviors, tracking and mapping the world’s ships, and monitoring Google I/O 2013 using a real-time sensor network.

Today we’re announcing another update to BigQuery packed with new capabilities.

• Large results: run queries that return arbitrarily large numbers of rows and save them as a new table for follow-up analysis. 
• Window functions: take advantage of built-in functions like Rank and Partition to create sophisticated statistical analyses with far simpler SQL than before. 
• Query caching: now recent queries that are re-run return a cached result when the underlying table is unchanged, providing more cost-effective analysis.

Gamesys, who previewed these features, was able to efficiently identify different cohorts of gaming customers and understand how to create a better in-game experience for distinct groups of users. "Rank and Partition are our 'go to' functions for examining player behaviour over time”, said Tom Newton, Director of Social Gaming at Gamesys. “These new functions combined with large results sets and query caching, help us efficiently and cost effectively improve and scale our analysis to create actionable intelligence that drives product enhancement.”

We’ve also rolled out a host of UI improvements, including the ability to validate a query and estimate its cost prior to running it, and to save frequently used queries. And thanks to recent operational improvements, we’ve been able to double existing query quotas.

Finally, BigQuery customers will have new pricing options starting in July. Data storage costs in BigQuery are becoming even more affordable for everyone, going from $0.12/GB/month to $0.08/GB/month effective July 1st. Furthermore, in addition to the existing on-demand rate for interactive queries, customers with higher-volume usage will soon be able to opt in for tiered query pricing. This provides more economical and predictable cost for interactive queries. Customers who are interested are encouraged to contact a sales representative.

You can get details about these new capabilities and more in our Developer Blog and in our updated product documentation. Got an inspiring use case? Share it in the blog comments or with our community using the #BigQuery tag on Google+.

Posted by Joshua Lowcock, Head of Commercial Platforms and Products for News Limited

(Cross-posted on the Official Google Australia Blog)

Editor's note: Today’s guest blogger is Joshua Lowcock, Head of Commercial Platforms and Products for News Limited, an Australian media company. See what other organizations that have gone Google have to say.

News Limited is one of Australia’s largest media companies, spanning newspapers, magazines, online, and subscription TV. We publish over 140 online and printed newspapers in major Australian cities including Sydney, Melbourne, Brisbane, Adelaide, and Perth, as well as in suburban areas.

Classified advertising is a key revenue stream across all our markets, but traditionally booking and billing classifieds had been a manual and time-consuming process. We wanted to implement a solution that would allow customers to serve themselves by placing ads online.

Google App Engine has enabled customers to do just that. We chose Google App Engine as the application because it is easy to build, easy to maintain and simple to scale as the user base and data storage grows. Functionalities within the Google App Engine environment, such as Google BigQuery, have also been useful. We can do an in-depth analysis of our ads and item pricing, as well as provide an internal reporting tool, all using BigQuery.

The end result is a self-service, production booking and billing system - www.traderoo.com.au - which we have developed on Google App Engine. It’s proving to be a real winner for both our business and our customers. It’s fundamentally changed the way customers engage with our company, creating a more usable experience and superb responsiveness. It’s easy to use, and gives more control over ad content, as well as the ability to publish ads online immediately. Online ads are free, while print ads are optional and require a small fee, but complement online ads by extending the advertiser’s reach.

When customers book ads using the Traderoo website, they get automatic email notification from the platform that tells them how their advertisement is performing. Traderoo is optimised for PC, laptop, smartphone and tablet, so the browser and ad placement remain consistent, no matter what device our customers are using.

The real advantage for us is that our classified business has achieved faster time to market, lower costs and less overheads in the form of call centre time and manual data entry. The site has been a huge success, and we look forward to continuing to use Google App Engine as we develop Traderoo further.