Wednesday, October 31, 2012

Have you ever wanted to cut and paste from an existing email while creating a new email?  I reuse my email content all the time.  Well, now thanks to Google Apps constantly updated capabilities, I can more easily compose new emails while accessing content from my previously received emails.

If you use Google Apps you may use this new feature.

Gmail's new compose and reply experience

You can now write messages in a cleaner, simpler experience that puts the focus on your message itself, not all the features around it. Here are some of the highlights:
  • Fast: Compose messages right from your inbox.
  • Simple: Redesigned with a clean, streamlined look.
  • Powerful: Check emails as you're typing, minimize drafts for later, and even compose two messages at once.

Try it out!

Once you click the Compose button, click the "new compose experience" link right next to the Labels button at the top of the message. Until the change is fully launched, you'll be able to choose whether you use the new or current experience.
If you change your mind or if you need to use a feature that isn't available yet, you can switch back to the old experience at any time. Here's how:
  1. Click Compose
  2. At the bottom corner of the message pane, click the More menu icon More options drop-down arrow next to the Discard button.
  3. Select "Switch back to old compose."

Details of the new compose feature may be found at this Google site

Tuesday, October 2, 2012

Google's BigQuery

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.

With Google BigQuery, you can run ad hoc, SQL-like queries against datasets with billions of rows. This can be your own data, or data that someone has shared with you. BigQuery works best for interactive analysis of multi-terabyte datasets, to which you can append fresh data.

Google BigQuery offers these features:


  • Data storage that scales seamlessly to hundreds of terabytes, with no management required

Speed and flexibility
  • Ad hoc queries on multi-terabyte datasets
  • Familiar SQL-like query syntax and intuitive web UI
  • Ability to JOIN enormous fact tables to most lookup tables

Integration and accessibility
  • Integration with Google spreadsheets, letting data analysts drive massive datasets in BigQuery directly from a spreadsheets interface
  • Interactive dashboards easily built with Google AppEngine, and smooth data export to Google Cloud Storage
  • HTTP REST API, a web UI for interactive querying, and command-line interface

Today Google enhanced BigQuery 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

If you need help with Extract - Translate - and Load (ETL), BigQuery design, or generating BigQuery reports send us an email to