#Bigquery Geospatial

[govindhtech]

Google BigQuery Geospatial: Analyze Location Data In Cloud

Use Google's new geographic datasets to improve BigQuery analysis.

Geospatial Google BigQuery

Today at Google Cloud Next 25, it unveils Earth Engine and Google Maps Platform geospatial analytics datasets and functionalities. BigQuery, Google's data-to-AI technology, integrates them flawlessly. BigQuery users know the value of data-driven insights, and these new features will let you analyse data from additional sources and leverage geographic data to make faster, more informed choices.

Geographical analytics trends and issues

Due to generative AI, hyper-localization, and strong analytical tools, geospatial analytics is increasing quickly. Despite these developments, many sectors underuse geospatial analytics. Finding fresh, accurate, and full data in an analysis-ready format takes time and resources. Second, because various data sources contribute unpredictability, firms struggle with integration and analysis, requiring extensive planning and transformation.

Finally, building geospatial analytics applications requires expertise and consistency.

How new geospatial capabilities solve these problems

The trustworthy geospatial technology Google Maps Platform improves the lives of over 2 billion users with over 10 million websites and apps. Over the past 15 years, Earth Engine has provided data scientists with over 90 petabytes of satellite images and geospatial data.

Customers want greater insights from the vast geographical data to improve business and sustainability choices. We are integrating a few Google Maps Platform datasets and Earth Engine datasets and analytic tools directly into BigQuery for the first time. This means data analysts and decision-makers may now use BigQuery to access and analyse fresh, vast, and global geospatial data.

These new datasets and capabilities allow:

Novel perspectives, trusted tools: Use Google's new global geospatial data without remote sensing or GIS expertise.

Integration of geographical data with your data yields fresh insights.

Easy data access and discovery: No more data digging. Geospatial data may be examined like other BigQuery datasets.

The first integration of analysis-ready pictures and datasets from Earth Engine, Places, and Street View into BigQuery processes allows customers to leverage data clean rooms to extract insights without releasing raw data.

Imagery insights

The first Experimental Imagery Insights dataset for the US, Canada, UK, and Japan speeds up infrastructure asset management using Street View data's global scale, Vertex AI-powered analysis, and BigQuery's capacity.

This combo lets you utilise Street View pictures to quickly recognise and automatically assess infrastructure assets like road signs and utility poles, with the option to add more attribute types.

Street View photos can assist municipal planners estimate annual road sign repair costs by detecting the quantity and location of sign maintenance needs. Data-driven decision-making, process optimisation, and planning and operational efficiency result from integration.

Locations Analysis

Places Insights provides monthly Google Maps data on over 250 million companies and places to help you make smarter business decisions. This Places dataset provides insights beyond POI information like wheelchair accessibility and pricing range. You will learn more about millions of companies and attractions, such as the location of most coffee shops in a zip code.

BigQuery's data clean room can mix Places data with proprietary data to disclose more about certain places. Understand local market dynamics and find the ideal store sites based on complementary companies' locations are typical use cases.

Roads Management Advice

Roads Management Insights helps public and road authorities improve road network efficiency and safety through data-driven traffic management. Past data analysis reveals traffic trends on your road networks, likely slowness causes, and required action. With real-time monitoring, authorities can respond to sudden speed drops, identify the source, and maybe redirect traffic in seconds.

BigQuery Earth Engine

Earth Engine with BigQuery brings Earth Engine's best geographic raster data analytics to BigQuery. This functionality allows SQL users to do comprehensive geospatial analysis of satellite photography datasets without remote sensing expertise.

ST_REGIONSTATS(), a new BigQuery geography function, reads and analyses geospatial raster data in a given region using Earth Engine. You can now access more Earth Engine datasets from BigQuery to Analytics Hub's Earth Engine datasets, making data access and discovery easier.

Google's geospatial analytics datasets in BigQuery can help make business and environmental decisions like optimising infrastructure operations and maintenance, enabling sustainable sourcing with global supply chain transparency, improving road safety and traffic, and more.

[technology-insights]

Best Software for Predictive Analytics in 2025

As we approach 2025, businesses are increasingly relying on predictive analytics software to make smarter, data-driven decisions. These tools analyze vast amounts of data to predict future trends, helping organizations stay ahead of the curve. With data creation expected to reach 180 zettabytes by 2025, leveraging predictive analytics has never been more crucial.

Here are the top 5 predictive analytics software of 2025:

Google Cloud BigQuery: Known for its serverless architecture, BigQuery allows businesses to analyze massive datasets quickly, with machine learning integrations for predictive insights. Its real-time analytics and geospatial capabilities make it ideal for businesses needing fast, scalable predictions.

Amazon QuickSight: This AWS-powered tool uses machine learning and natural language processing to forecast trends and identify anomalies. QuickSight’s auto-scaling architecture ensures it performs well even with millions of queries, making it ideal for companies in the AWS ecosystem.

Adobe Analytics: A leader in marketing analytics, Adobe Analytics uses AI-powered insights to predict customer behaviors and trends. It excels in cross-channel tracking and real-time data visualization, offering businesses a 360-degree view of customer journeys.

Tableau: Known for its interactive dashboards, Tableau integrates with Salesforce Einstein Discovery to provide predictive analytics. Its ability to handle complex datasets from diverse sources makes it a top choice for data-driven decision-making.

SAP Analytics Cloud: Combining predictive analytics with business intelligence, SAP Analytics Cloud offers AI-driven forecasting and smart insights. It allows seamless integration with other SAP applications, providing a unified workflow for enterprise-level planning.

These tools empower businesses to harness their data, uncover insights, and make informed decisions, ensuring they are prepared for the future.

[indoorverticalfarmingnews]

Almanac Appoints Dr. Chad W. Jennings as New VP of Product Management

Key Takeaways: New Leadership: Dr. Chad W. Jennings has been appointed as Almanac’s Vice President of Product Management, bringing over 20 years of experience in data analytics. Background in Geospatial Technology: Jennings previously led geospatial advancements at Google Cloud’s BigQuery, enhancing its data capabilities. Agricultural Insight: With a personal background in agriculture, Jennings…

[uswanth123]

SNOWFLAKE BIGQUERY

Snowflake vs. BigQuery: Choosing the Right Cloud Data Warehouse

The cloud data warehouse market is booming and for good reasons. Modern cloud data warehouses offer scalability, performance, and ease of management that traditional on-premises solutions can’t match. Two titans in this space are Snowflake and Google BigQuery. Let’s break down their strengths, weaknesses, and ideal use cases.

Architectural Foundations

Snowflake employs a hybrid architecture with separate storage and compute layers, which allows for independent resource scaling. Snowflake uses “virtual warehouses,” which are clusters of compute nodes, to handle query execution.

BigQuery: Leverages a serverless architecture, meaning users don’t need to worry about managing the computing infrastructure. BigQuery automatically allocates resources behind the scenes, simplifying the user experience.

Performance

Snowflake and BigQuery deliver exceptional performance for complex analytical queries on massive datasets. However, there are nuances:

Snowflake: Potentially offers better fine-tuning. Users can select different virtual warehouse sizes for specific workloads and change them on the fly.

BigQuery: Generally shines in ad-hoc analysis due to its serverless nature and ease of getting started.

Data Types and Functionality

Snowflake: Provides firm support for semi-structured data (JSON, Avro, Parquet, XML), offering flexibility when dealing with data from various sources.

BigQuery: Excels with structured data and has native capabilities for geospatial analysis.

Pricing Models

Snowflake: Primarily usage-based with per-second billing for virtual warehouses. Offers both on-demand and pre-purchased capacity options.

BigQuery provides a usage-based model where you pay for the data processed. It also offers flat-rate pricing options for predictable workloads.

Use Cases

Snowflake

Environments with fluctuating workloads or unpredictable query patterns.

Workloads heavily rely on semi-structured data.

Organizations desiring fine control over compute scaling.

BigQuery

Ad-hoc analysis and rapid exploration of large datasets

Companies integrated with the Google Cloud Platform (GCP) ecosystem.

Workloads requiring geospatial analysis capabilities.

Beyond the Basics

Security: Both platforms offer robust security features, such as data encryption, role-based access control, and support for various compliance standards.

Multi-Cloud Support: Snowflake is available across the top cloud platforms (AWS, Azure, GCP), while BigQuery is native to GCP.

Ecosystem: Snowflake and BigQuery boast well-developed communities, integrations, and a wide range of third-party tools.

Making the Decision

There’s no clear-cut “winner” between Snowflake and BigQuery. The best choice depends on your organization’s specific needs:

Assess your current and future data volume and complexity.

Consider how the pricing models align with your budget and usage patterns.

Evaluate your technical team’s comfort level with managing infrastructure ( Snowflake) vs. a more fully managed solution (BigQuery).

Factor in any existing investments in specific cloud platforms or ecosystems.

Remember: The beauty of the cloud is that you can often experiment with Snowflake and BigQuery. Consider proofs of concept or use free trial periods to test them in real-world scenarios with your data.

You can find more information about  Snowflake  in this  Snowflake

 

Conclusion:

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[tipsgcp]

BigQuery for data analytics in GCP

GCP Taining and Certification, Google BigQuery is a fully managed, serverless data warehousing and analytics platform offered by Google Cloud Platform (GCP). It enables organizations to analyze large datasets quickly and efficiently. Here's an overview in 250 words:

1. Scalable Data Warehousing:

BigQuery can handle petabytes of data, providing a scalable solution for data warehousing and analytics.

2. Serverless and Managed:

It's serverless, meaning users don't need to manage infrastructure, and Google takes care of performance optimization and scaling automatically.

3. SQL Query Language:

BigQuery uses standard SQL for querying data, making it accessible to users familiar with SQL.

4. Real-time Analysis:

It supports real-time analysis with streaming data ingestion, enabling immediate insights from live data sources.

5. Integration with GCP Services:

BigQuery seamlessly integrates with other GCP services like Cloud Storage, Dataflow, and Dataprep, allowing data import, transformation, and visualization.

6. Data Security and Governance:

It provides robust security features, including fine-grained access control, encryption at rest and in transit, and audit logging.

7. Cost-Effective Pricing:

Users are billed for the amount of data processed by queries and storage used. BigQuery's pricing model is cost-effective, especially for on-demand, ad-hoc querying.

8. Machine Learning Integration:

It offers integration with Google's AI and machine learning tools, allowing data scientists to train models on BigQuery data.

9. Geospatial Analytics:

BigQuery supports geospatial data types and functions for location-based analysis.

10. Data Export:

Users can export query results to various formats or directly into other GCP services for further analysis or visualization.

11. Data Studio Integration:

Connect BigQuery with Google Data Studio for creating interactive, customizable reports and dashboards.

BigQuery is widely used for various data analytics tasks, including business intelligence, data exploration, machine learning, and real-time data analysis. Its simplicity, scalability, and integration with the broader GCP ecosystem make it a powerful tool for deriving insights from large and complex datasets.

[hackernewsrobot]

Faster Geospatial Enrichment: PostgreSQL vs. ClickHouse vs. BigQuery

https://tech.marksblogg.com/faster-geospatial-enrichment.html Comments

[glenmenlow]

Google and Unilever teams up to fight deforestation with cloud computing

Google is building a more holistic view of the forests, water cycles, and biodiversity that intersect Unilever’s supply chain thus raising sustainable sourcing standards for suppliers and bringing Unilever closer to its goal of ending deforestation and regenerating nature

Google Cloud and Unilever have announced that they will advance sustainable business practices together using technology to expand the use of data for eco-friendly decision making. As an initial step in this partnership, the two companies are collaborating on the first commercial application of Google Cloud and Google Earth Engine for sustainable commodity sourcing.

By combining the power of cloud computing with satellite imagery and AI, the two companies are building a more holistic view of the forests, water cycles, and biodiversity that intersect Unilever’s supply chain — raising sustainable sourcing standards for suppliers and bringing Unilever closer to its goal of ending deforestation and regenerating nature.

The tech company and Unilever will work with a broad range of technology partners to build a centralised command centre. This will provide a more complete picture of the ecosystems connected to Unilever’s supply chain and create a better mechanism for detecting deforestation—leading to greater accountability — whilst simultaneously prioritising critical areas of forest and habitats in need of protection.

Unilever, which owns 400+ brands and whose products are used by 2.5 billion people every day, has made sustainability an intrinsic part of its business. The company’s sustainable sourcing initiative, which is initially focused on sustainable palm oil, will be extended to other commodities in the future, directly supporting Unilever’s existing work with other technology partners to achieve a deforestation-free supply chain by 2023.

Google Cloud’s planetary-scale geospatial platform, including Google Earth Engine, Google Cloud Storage and BigQuery, combines accurate satellite imagery, with the ability to store and make sense of large amounts of complex data. Unilever will use the platform to obtain insights into any impact on sourcing to the environment and local communities, allowing Unilever and its suppliers to take action wherever and whenever it is needed.

Simplifying complex datasets is critical to increasing transparency within supply chains and enabling collaboration across public and private partners. Google Earth Engine is used for planetary-scale image analysis by academic and public institutions, as well as civil society organisations. This first commercial application with Unilever will enable further innovation that can be shared with sourcing partners of all types on a common platform.

“This collaboration with Google Cloud will take us to the next level in sustainable sourcing,” said Dave Ingram, Unilever’s Chief Procurement Officer. “We will now be able to process and combine complex sets of data like never before. The combination of these sustainability insights with our commercial sourcing information is a significant step-change in transparency, which is crucial to better protect and regenerate nature.”

“At Google, we strive to build sustainability into everything that we do. Unilever has been an industry leader in environmental sustainability for many years, and we’re excited to be on this journey with them,” said Rob Enslin, President, Google Cloud. “Together, we’re demonstrating how technology can be a powerful tool in aiding businesses who strive to protect the Earth’s resources. It will require collective action to drive meaningful change, and we are committed to doing our part.”

The article Google and Unilever teams up to fight deforestation with cloud computing appeared first on World Branding Forum.

from WordPress https://glenmenlow.wordpress.com/2020/09/23/google-and-unilever-teams-up-to-fight-deforestation-with-cloud-computing/ via IFTTT

[govindhtech]

AI-Powered Google Cloud Project Transforms Climate Action

The AI-Powered Project by Google Cloud to Accelerate Climate Action

The need for greater aspirations and swift action becomes clear as COP28 draws to a close. An alarming conclusion from the UN’s new research is that this century’s emissions commitments are likely to cause global warming of almost three degrees Celsius. A spike in worldwide disasters, such as fires, floods, and the loss of arable land, is predicted by this concerning trend. The implications of these promises failing are much more serious than the enormous stakes.

Google’s Particular Contribution to Climate Change

Google has the potential to make an impact at this crucial point. Particularly in wealthy, high-emission countries, the UN emphasizes the importance of expedited low-carbon reforms. Google has a unique potential to make a substantial contribution here.

One of the most important issues that the world is now experiencing, in opinion at Google, is climate change. To protect the Paris Agreement and confront the impending risks posed by climate change, they are steadfast in they resolve.

Promises Fulfilled by Deeds

Beyond words, google dedication is genuine. By 2030, Google wants all of its activities and value chain to be net-zero emission sources. This challenging objective is strengthened by google pledge to depend entirely on carbon-free energy on all grids in which they operate. Billionaires and users who use Google’s numerous goods and services on a daily basis stand to gain greatly from the wide-ranging consequences of this.

Google partners and customers are also beneficiaries of this unshakable dedication. Google Cloud sees cloud and AI as essential tools to cut carbon footprints and promote green growth, having recognized the value of these technologies.

Three Key Points of Attention

Google’s initiatives are focused on three main areas: improving the climate tech ecosystem, using geospatial analytics for climate resilience, and supporting the accessibility of climate transition data, all of which are in line with the objectives put forward by the COP28 chair.

1.Improving Climate Transition Information Accessibility Data on climate change, such as vital details about greenhouse gas (GHG) emissions and emission reduction goals of businesses, are currently not consistently reported. Complicating the problem are obstacles to obtaining this data. Ever aware of this vacuum, Google is actively contributing to the development of the Net Zero Public Data Utility, which will be unveiled at COP28.

As a global archive for data pertaining to climate transition in the private sector, the Net-Zero Data Public Utility (NZDPU) was established. Leading this endeavor, Michael Bloomberg makes sure that all stakeholders have unrestricted access to the data. The design and construction of the NZDPU proof of concept will be handled by Google, Insomniac Design Inc., and CyBourn Inc.

2.Maintaining the Ecosystem of Climate Tech Google is dedicated to fostering the emerging community of businesses and entrepreneurs in the climate tech space that are using cloud and artificial intelligence to create meaningful change. This project heavily relies on the Google Cloud Ready – Sustainability (GCR-S) assessment program. Using Google Cloud, forty climate tech partners designated as GCR-S are now developing solutions aimed at cutting carbon emissions, improving value chain sustainability, storing climate data, and recognizing climate threats for heightened resilience.

3.Making Climate Resilience Through the Use of Geospatial Analytics Google is focusing on the advantages of geospatial analytics in building resilience, while also acknowledging the physical effects of climate change. Organizations who want to know how climate change is affecting their supply chains and infrastructure can do so by utilizing geographic analytics. Making use of BigQuery, Vertex AI, and Google Earth Engine, Google Cloud works with partners to shed light on long-term chronic climate problems as well as immediate threats.

SpatiaFi was introduced on Google Cloud, for example, by Climate Engine, a Google Cloud Ready – Sustainability partner. This program, in partnership with Deloitte, helped UK bank NatWest make sense of critical climate-related data points for its agricultural clients by working with satellite imagery.

Google contributed to the UN Race to Resilience in 2023 as a parallel endeavor.  This was investigated at a design sprint held during NY Climate Week. More progress is anticipated in 2024 thanks to the Race to Resilience team’s continued partnership with Google.

In summary, Google is leading the charge in accelerating climate action with its Commitment to Climate Action COP28. Beyond words, google dedication is real. google goal is to help companies measure, optimize, and reimagine their operations for a low-carbon economy by utilizing AI and Google Cloud technology. Enabling AI to help the future generation of climate tech entrepreneurs, Google sees itself as a key player in these developing data platforms as they approach the dawn of a new age.

Read more on Govindhtech.com

[endlesssuppliesnl]

Supporting Geospatial Analytics With GCP Public Datasets (Cloud Next '19)

Finding the perfect dataset to supplement your existing workloads can be really difficult. It requires you to know where to look, how to access the data, and how often it's updated. This process can be challenging and time consuming. The Google Cloud Platform Public Datasets Program can help you focus on your analysis and insights instead of data discovery and onboarding, using BigQuery. This session will provide users with an overview of the Google Cloud Platform Public Datasets Program and the datasets that support geospatial analytics. In addition, this session will demonstrate how users can connect public datasets with their own data to leverage BigQuery's GIS support to expand their analysis without expanding your data discovery efforts. Google Cloud IoT → https://bit.ly/2TZaKOZ Watch more: Next '19 Data Analytics Sessions here → https://bit.ly/Next19DataAnalytics Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the G Suite Channel → https://bit.ly/G-Suite1 Speaker(s): Shane Glass, Felipe Hoffa, Clare Hegg, Kian Mirshahi Session ID: DA208 product:BigQuery,Cloud Pub/Sub,Cloud Dataproc; fullname:Samrat Saha; http://bit.ly/2IeiXNR G Suite April 10, 2019 at 09:15PM https://ift.tt/eA8V8J

[android-for-life]

"Cloud Covered: What was new with Google Cloud in February"

February is a time for chocolate and candy hearts, but you know what's really sweet? Less email spam and apps that work faster. Those are just two of our updates from Google Cloud last month. Read on for what was new and popular last month on the Google Cloud blog.

Gmail: now with even less spam.

Gmail already blocks 99.9 percent of spam email for users, and a new application of our machine learning framework TensorFlow is now helping to block 100 million more spam messages every day. TensorFlow does this by detecting new types of spam messages by identifying potentially suspicious patterns in large data sets more efficiently than humans can. So this use of machine learning means it’s easier to stop new types of spam, which are constantly emerging.  

You can move to and from the cloud with a hybrid platform.

Working in the cloud means that businesses won’t run their back-end computers (known as servers) at their own location anymore, but will instead use a cloud provider to run them in the cloud. Hybrid cloud is the concept of a business running some of their servers with a cloud provider, in a cloud provider’s data center, and keeping other servers in their own data centers. Last month’s hybrid cloud announcement brought news that this is now a lot easier for businesses using Google Cloud Platform (GCP) to do. The Cloud Services Platform (CSP) launch means that developers can build and, IT teams can run, their applications both in the cloud and in their own data center with a consistent experience.

A database could be the secret sauce that helps build apps faster.

Lots of us—or maybe most of us—depend pretty heavily on our phone applications to book transportation, find restaurants, order food and do lots of other everyday tasks. There’s a lot under the hood that makes those apps work well and stay updated all the time. We announced last month that the Cloud Firestore database is generally available, and described some of the ways to use it for  building mobile, web, and IoT apps. One media company used Cloud Firestore to build a real-time media feed so users would see the latest news updated across all their devices.

We introduced a new sandbox (but not the messy kind).

The idea of a sandbox in the tech world isn’t that different from the kind you see at playgrounds: It’s essentially a contained area to explore and play, using software development tools instead of shovels and buckets. There’s a new type of sandbox for IT students, developers and other experimenters to use Google Cloud’s BigQuery without having to enter credit card information.  BigQuery is a Google Cloud product that lets companies ask questions of their collected data, such as to track business trends over time, or to explore publicly available data sets, such as NOAA weather data, to include in their own applications. BigQuery sandbox users get the same compute power as paying users, and just like paying users can use new capabilities like BigQuery Machine Learning and BigQuery Geospatial Information Systems.

It’s easier to explore cryptocurrencies and blockchains.

On the topic of publicly available data sets, we released six new cryptocurrency blockchain data sets last month. What does that mean, you might ask? The blockchain itself is a list of records, and a cryptocurrency is a type of, well, currency that’s exchanged online and secured by cryptography. Bitcoin may be the most well-known example of a cryptocurrency based on blockchain, but there are others as well. Adding these six new blockchain data sets mean that BigQuery users can explore and analyze data to understand how these cryptocurrencies really work, and integrate them into other financial data management systems.

That’s a wrap for February. Make sure to check out our upcoming Google Cloud Next ‘19 conference to read lots more about cloud.

Source : The Official Google Blog via Source information

[theresawelchy]

Data Notes: Analyzing Ethereum Classic via Google BigQuery

Welcome to Kaggle Data Notes!

Cryptocurrency, climate change, and the price of avocados: Enjoy these new, intriguing, and overlooked datasets and kernels

  1. Analyzing Ethereum Classic via Google BigQuery (Link)

2. Machine Learning for Geospatial Data (Link)

3. What Does a CNN See? (Link)

4. Price of Avocados || Time Series Forecasting (Link)

5. Visualization & EDA on World Happiness Report (Link)

6. Climate Change Forecast - SARIMA Model (Link)

7. Nuclear Power Plant Locations (Link)

8. Dataset: Top Spotify Tracks of 2018 (Link)

9. Dataset: Youth Risk Behavior Surveillance System (Link)

10. Dataset: Python Developers Survey (Link)

    Technique of the week

​

Do you know how to explain the internal workings of machine learning models?  Earn bragging rights (plus a $2000 award!) if you explain the output of a ML model and are chosen as Kaggle’s Kernel Author of the Month!

Copyright © 2019 Kaggle, All rights reserved.

No Free Hunch published first on No Free Hunch

[fouldata]

pycon talks directory

PyCon JP:

Jupyter with BigQuery

Geospatial data and visualization

text analysis with Python

PySpark

Blockchain for Pythonistas

Ways to avoid overfitting models

PyCon 2017:

Python in the Serverless era

Probabilistic programming 

Finding model parameters with random walks

Bayesian statistical analysis with Python

Async Python, Python 3.6

Building Stream processing applications

Lightning talks 

Writing a C Python extension in 2017

Cython as a game changer for efficiency

Optimizing pandas for speed and efficiency

Jake Vanderplas

Oscar predictions

What's new in Python 3.6

Static types for Python

Writing Functional Python

Dask

Leveraging serverless arch to build better data pipelines

Deploy Python web app in 2017

I installed Python 3.6 on Windows and I liked it

Intro to Bayesian Machine learning with PyMC3 and Edward

Parallel data analysis

scipy.spatial

Applied modern cryptography in Python

Best testing practices in Data Science

Hands-on Intro to Python for New Programmers

Time Series Analysis

Introduction to Statistical Modeling with Python

Complexity Science

Exploratory data analysis in python

Using Functional Programming for efficient Data Processing and Analysis Link

Python and Flask

EuroPython 2017:

Scientic computing using Cython

Overcoming Cognitive bias

Graph databases

Python and Angular

Large scale data extraction, structuring and matching using Python and Spark

Cloud native python in Kubernets

Dockerized pytests

Type annotations in Python 3

PyPy JIT

Faster python

Airbnb, data visualization that includes geographic locations

Introduction to Nonparametric Bayesian Models

Booking.com deep learning model predictions

When Django is too bloated - specialized web applications 

Big data analysis at the MPCDF

README docs

Realtime Distributed computing at scale in pure python: Storm and * Streamparse

Python on Windows, like a boss

Bitcoin and Blockchain for Pythoneers

Isreal Pycon 2017

Building a chatbot using Python

[craigbrownphd-blog-blog]

#ICYMI: Google takes BigQuery to new geographies, brings geospatial capabilities into beta https://goo.gl/C5iSnx

[craigbrownphd-blog-blog]

Google takes BigQuery to new geographies, brings geospatial capabilities into beta https://goo.gl/wj2xPn

[geone-ws]

The MapScaping #Podcast: Google BigQuery #GIS – #Geospatial in the Cloud

https://mapscaping.com/blogs/the-mapscaping-podcast/google-bigquery-gis-geospatial-in-the-cloud

[geone-ws]

Puppies & BigQuery: Analyzing #Geospatial Data

https://medium.com/@mentin/puppies-bigquery-analyzing-geospatial-data-9934e0021b