A new cover, title, and co-author; and lots of new content

Professional Android 4th Edition is now available, and will start shipping today (September 25th) from Amazon!

You can order Professional Android 4E in paperback from Amazon or The Book Repository, or your local bookseller. Or for your electronic reading pleasure, Kindle US (and UK) or Google Play Books.

This edition was written using Android Studio 3.1 and targeting API Level 27 (with some coverage of API 28 changes). It introduces Kotlin, but the code samples are written using Java syntax (we plan to make them available in Kotlin on Github as well — stay tuned!).

As always, it covers both…

Reto Meier and Colt McAnlis present Build Out (Episode 2)

Designing a global metaverse

In the second episode of Build Out, Colt McAnlis and Reto Meier were given the challenge of designing a global metaverse.

Take a look at the video to see what they came up with, then continue reading to see how you can learn from their explorations to build your own solution!

TL;DW: What they designed

Both solutions describe a design to generate a 3D environment that users experience using a virtual reality headset, using various levels of cloud compute and storage to provide virtual Earth data to the client, and calculate changes to the world environment as users interact with it.

Reto’s solution

Reto Meier and Colt McAnlis present Build Out (Episode 1)

Designing an autonomous, learning smart garden

In the first episode of Build Out, Colt and Reto — tasked with designing the architecture for a “Smart Garden” — supplied two very different concepts, that nevertheless featured many overlapping elements. Take a look at the video to see what they came up with, then continue reading to see how you can learn from their explorations to build your very own Smart Garden.

TL;DW: What they built

Both solutions aim to optimize plant care using sensors, weather forecasts, and machine learning. Watering and fertilizing routines for the plants are updated regularly to guarantee the best growth, health, and fruit yield possible.

Colt’s solution

Crime Distribution in San Francisco

Map all the SFPD incidents since 2010 and the Tenderloin stands out as a place you probably want to be careful if you’re visiting The City.

I wondered how different types of crime are distributed around the City by the Bay — so I fired up BigQuery and used the SFPD Incidents public dataset to investigate.

Using the NOAA GHCN and GSOD datasets

The analysis in today’s Today I Learned with BigQuery was performed by @savio_lawrence using BigQuery, Tableau, and the NOAA GHCN and GSOD datasets to see what observations we can make about changes in the average temperatures recorded at weather observation stations around the world.

Before we dive-in, note that while interesting, this analysis by itself is insufficient to draw conclusions for something as complex as climate change or global warming.

Scientific organizations and researchers work hard to account for the challenges we’ll highlight throughout the analysis, as well as combining datasets like these with remote sensing observations to obtain global…

San Francisco. Fog City. The City by the Bay. No matter what you call it, those 47mi² are home to over 800,000 people about whom we can draw outrageous conclusions using the new San Francisco public dataset in BigQuery.

Thanks to the City and County of San Francisco’s SF OpenData project and Bay Area Bike Share, Google BigQuery’s Public Datasets now includes San Francisco public data, including:

The New York City public dataset contains all the 311 complaints made since 2010. Let’s see how the city has improved — and gotten worse — over the past 6 years.

The graph below highlights the 311 complaints, with significant call volume, with the biggest increase / decrease between 2010 and 2016.

Importing data for analysis with Google BigQuery

Originally published on the Google Cloud Big Data and Machine Learning blog

The NYC 311 dataset in BigQuery indicates a dramatic increase in complaints about rats, starting in 2013.

Using Google BigQuery to explore how weather affects NYC

Originally published on the Google Cloud Big Data and Machine Learning blog

With over 150GB of New York City public data, parsing it all for patterns and insights is a challenge. One solution? Combine it with another 30GB of weather data, and use the CORR function to find correlations for you.

Correlation doesn’t imply causation, but it can help you identify patterns worth exploring. By finding the highest correlations between weather variables and the NYC datasets, I’m going to try and answer a number of weather-related questions about the city:

  • Does the temperature affect Citi Bike and taxi rides?

Originally published on the Google Cloud Big Data and Machine Learning blog

Google BigQuery lets you process your first terabyte of data, every month, at no cost. This allowance is automatically applied to each project, so if you’re already using BigQuery you’re already taking advantage.

If you’re new to BigQuery, and you’re interested in playing with their public datasets, you can use the free tier without even needing to provide your credit-card details. ’Tis but the work of a moment.

As shown in the video, start by navigating to the BigQuery Web UI. You’ll need to sign-in using a…

Reto Meier

Developer Advocate @ Google, software engineer, and author of “Professional Android” series from Wrox. All opinions are my own.

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