Posted by Ju-kay Kwek, BigQuery Product Manager

Simple data analytics can sometimes seem like a misnomer. Technology that’s simple for engineers and IT may add complexity to business processes. And the inverse can be true for implementing agile analytics into traditional Business Intelligence (BI) systems. We often hear from customers in industries--ranging from retail to hospitality--that highly skilled teams struggle to deliver what the organization really needs: actionable and data-driven business insights.

Today we’ve added some new capabilities to Google BigQuery that will give your business new ways to work effectively with large amounts of data.

• Big JOIN: use SQL-like queries to join very large datasets at interactive speeds
• Big Group Aggregations: perform groupings on large numbers of distinct values 
• Timestamp: native support for importing and querying Timestamp data 

With these capabilities, you will now be able to join and perform aggregate analysis on multi-terabyte datasets using SQL-like queries or integrated 3rd party tools, instead of having to initiate complex coding projects.

We’ve been using this technology within Google. For example, when our App Engine team needed to reconcile app billing and usage information, Big JOIN allowed the team to merge 2TB of usage data with 10GB of configuration data in 60 seconds. Big Group Aggregations enabled them to immediately segment those results by customer. Using the integrated Tableau client the team was able to quickly visualize and detect some unexpected trends.

Pricing remains the same: you pay only for the actual data that’s processed by your queries.

Joining terabyte-sized tables has traditionally been a challenging task for data analysts, requiring sophisticated MapReduce development skills, powerful hardware, or a lot of time--often all three. Today with BigQuery you can get directly to business insights using SQL-like queries, with far less effort and far greater speed than you could before.

For those interested in learning more, we’ve also provided technical details in our Developer Blog.

Posted by Brett McCully, Google Cloud Platform Team

Support is as important as product features when choosing a platform for your applications. And let’s face it, sometimes we all need a bit of help. No matter which Google Cloud Platform services you are using—App Engine, Compute Engine, Cloud Storage, Cloud SQL, BigQuery, etc.—or what time of day, you should be able to get the answers you need. While you can go to Stack Overflow or Google Groups, we realize some of you may need 24x7 coverage, phone support or direct access to a Technical Account Manager team.

To meet your support requirements, we’re introducing a comprehensive collection of support packages for services on Google Cloud Platform, so you can decide what level best fits your needs:

• Bronze: All customers get access to online documentation, community forums, and billing support. (Free) 
• Silver: In addition to Bronze, you can email our support team for questions related to product functionality, best practices, and service errors. ($150/month) 
• Gold: In addition to Silver, you'll receive 24x7 phone support and consultation on application development, best practices or architecture for your specific use case. (Starts at $400/month) 
• Platinum: The most comprehensive and personalized support. In addition to Gold, you’ll get direct access to a Technical Account Manager team. (Contact Sales for more information)

Sign up or click here to find out more information about the new Google Cloud Platform support options.

Posted by Ju-kay Kwek, Product Manager

We know you have a lot of data to work with within your organization, which can present big challenges. Your data can be large in volume and complex in structure. For example, large-scale web applications have millions of users, documents and events to manage. As a result, many engineering teams choose highly scalable NoSQL databases over relational databases. Though this approach is effective in storing and retrieving data, it poses challenges for interactive data analysis.

Today’s release of Google BigQuery tackles these hurdles with several new features:

• Support for JSON: JSON is used to power most modern websites, is a native format for many NoSQL databases hosting large scale web applications, and is used as the primary data format in many REST APIs. With this update, it’s now possible to import data formatted in JSON directly to BigQuery without the hassle of writing extra code to convert the data format.
  
  
• Nested and Repeated Fields: If you’re using App Engine Datastore or other NoSQL databases, it’s likely you’re taking advantage of nested and repeated data in your data model. For example, a customer data entity might have multiple accounts, each storing a list of invoices. Now, instead of having to flatten that data, you can keep your data in a hierarchical format when you import to BigQuery.
   
• Additional improvements: 
• Increased import quotas from 1000 jobs per day to 1000 jobs per table per day, and boosted the file size limit from 4GB to 100GB 
• Faster data exports from BigQuery to Google Cloud Storage, by enabling large tables to be exported as multiple files in parallel 
• Permanently save common queries in the BigQuery interface 

To learn more about how Google BigQuery can help you gain insights from your data in the cloud, click here to sign up.

Posted by Ju-kay Kwek, Product Manager, Google BigQuery

Big Data can be a challenge for businesses and developers. There is so much information available today that it can be difficult to gain insights and make business decisions based on that data. Last month, Google BigQuery integrated several partner solutions, making it easier to import data from other cloud and on-premise solutions and visualize your data with rich interactive dashboards. Today, we’re giving you new ways to work with your data by adding two new features to BigQuery.

Batch Queries

While BigQuery specializes in getting insights quickly, we understand that there are important, non-interactive queries, such as nightly reports, that businesses also need to run. Now, you can designate a query as a batch query and it will complete within a few hours.If you’re using BigQuery via our standard self-service model, you pay 2 cents per GB processed for batch queries and 3.5 cents per GB processed for interactive queries.

BigQuery Connector for Excel

Analysts and executives use spreadsheets to explore large data sets. Last year, we launched the ability for BigQuery users to execute queries inside Google spreadsheets using the Google Apps Script integration. With the new BigQuery Connector for Excel, we’re now making it simpler to execute BigQuery queries using Microsoft^® Excel. This connector takes advantage of Excel’s standard web query feature to eliminate the extra work of manually importing data and running queries directly within Excel. For instructions on how to download and use the connector, see the BigQuery Connector for Excel page.

If you haven’t gotten started with Google BigQuery yet, you can sign up here.

[Microsoft and Excel are registered trademarks of Microsoft Corporation.]

Posted by Ju-kay Kwek and Momchil Filev, Google BigQuery Team

(Cross-posted from the Google Developers Blog.)

Last month we announced the public launch of Google BigQuery, which enables developers and businesses to gain real-time business insights from massive amounts of data without any hardware or software investments.

Since then, we’ve added new features to Google BigQuery every week. For example, our most recent release includes support for running up to 20 concurrent queries, depending on the volume of data. This enables developers to build visually interactive dashboards on Google BigQuery.

Today, we’re highlighting two data visualization providers, QlikView and Bime, who are using Google BigQuery’s latest features to build dashboards with snappier and richer experiences.

QlikView

QlikView, one of the leaders in the Business Intelligence market, has developed a dashboard that visualizes the birth-record data for all babies born to mothers of different ages and races. With the help of BigQuery, QlikView can crunch millions of rows of data in seconds to answer questions like, “What's the average age of a mother in New York vs. in Texas?"

Bime

Bime, a cloud-based Business Intelligence provider based in France, is another early adopter of Google BigQuery. They’ve built a slick UI on top of the Google BigQuery platform that allows users to slice and dice 432 million rows of business data. For example, you can adjust a few simple parameters to see the sales distribution across products or regions on a map.

This is just a snapshot of how developers can use Google BigQuery to build interactive visual dashboards using a browser and without the hassle of managing SQL. Sign up and share your BigQuery use cases via our developer feedback form or on the Google Enterprise Google+ page.