Sasha  Lee

Sasha Lee

1621371000

Introduction to Google BigQuery

It is incredible to see how much businesses rely on data today. 80% of business operations are running in the cloud, and almost 100% of business-related data and documents are now stored digitally. In the 1960s, money made the world go around but in today’s markets, “Information is the oil of the 21st century, and analytics is the combustion engine.” (Peter Sondergaard, 2011)

Data helps businesses gain a better understanding of processes, improve resource usage, and reduce waste; in essence, data is a significant driver to boosting business efficiency and profitability.

This reliance on data isn’t without challenges though. A business can have large data warehouses and no efficient way of processing the data in them. There is also the challenge of sorting valuable data from noise, primarily when you collect data from public sources. Amassing data is meaningless without the tools and means to process, analyze, and act on it. So the important questions are: How can you make this process painless and how do you become a successful data-driven company? The answer to both lies in Google BigQuery.

So, What Is Google BigQuery?

Without the right tools, data collection and processing are not only challenging but costly. To store its first 1TB of data, a business needs to invest heavily in a large and reliable server cluster capable of running calculations and managing multiple storage nodes.

Today, that problem is gone thanks to services like Google Cloud Platform (GCP). GCP doesn’t just make establishing data warehouses easy but the platform also makes collecting and processing large volumes of data affordable. This is referred to as data democratization; cloud services from providers like Google making enterprise-grade data processing accessible to even small home businesses.

A key component to Google’s ecosystem is Google BigQuery. Google BigQuery is a data warehouse suite that handles data processing through SQL commands, all without requiring you to create a cloud computing instance specifically for processing data.

What BigQuery does is leveraging data stored in buckets and databases through simple ANSI SQL. We know how taxing SQL queries can be when complex, but that is no longer something you need to worry about with BigQuery. BigQuery can run complex SQL queries at incredible speeds, even when you have petabytes of data to analyze.

The secret behind BigQuery’s efficiency and speed is its serverless nature. You are not spooling up a separate BigQuery instance to process data. Google decouples cloud architecture from data management, meaning Google is responsible for maintaining availability and security. Instead, you pay only for the storage you use.

Being serverless and decupled are not the only features offered by Google Big Query. The service also comes with a long list of capabilities.

  • Standard SQL queries for all your data needs. If you are already familiar with SQL or you’ve used frameworks like MS SQL and MySQL before, you will have no trouble adapting to Google BigQuery. There is no steep learning curve to deal with.
  • High availability by nature. Google designs its services to be highly available, and BigQuery is no different. Besides persistent data storage, you also have your data accessible from the nearest nodes without paying extra for CDN services.
  • Granular cost control. With Google BigQuery, storage and computing cost components are separate cost components. You can fine-tune every part of your data infrastructure without jumping through hoops. Even better, you can transparently control costs.
  • Advanced access management. Google BigQuery integrates well with other Google services, so you still get features such as access control and enhanced security with the service. The centralized control dashboard gives you all the features you need to manage access to different segments of your data.
  • Backup and restore automation. Data security features offered by Google BigQuery also include data protection in the form of resilient backups. BigQuery has an intuitive versioning feature that automatically keeps versions of your data for up to seven days, so you can always revert to older versions or undo changes at any point.
  • ML and AI integrations. Of course, in-depth data analysis becomes more powerful when machine learning and artificial intelligence are parts of the equation. You get that with BigQuery ML. You have the option to integrate AI Platform or even TensorFlow to strengthen data analysis routines further.
  • Native multi-cloud support. Despite the deep integration with GCP and Google’s services, Google BigQuery has native support for multi-cloud infrastructure. The solution for multi-cloud integration is BigQuery Omni. It may still be in its early phase, but you have the option to manage multiple cloud-based data infrastructures from BigQuery.
  • Integrated natural language processing. If the availability of suites like TensorFlow is not what you want, you will undoubtedly appreciate Data QnA, which is BigQuery’s own natural language processing unit. The suite is an instance of Analyza, which is already very popular among data scientists. It can be utilized in use cases like chatbots and business intelligence too.
  • Multiple data ingestion methods. Of course, a good data warehouse is nothing without efficient data ingestion pipelines. This is one of the parts where BigQuery shines. Free Data Transfer Service or DTS handles ingestion. It works out of the box with services like Salesforce and other cloud business solutions, plus it works at scale from the beginning.

The list of features goes on with things like access to Google Cloud Public Datasets, detailed logging and monitoring, a built-in alert system, and so much more. Google is trying to put BigQuery in the center of all business data storage and analysis needs. From the feature sets that we’ve seen so far, Google is doing an excellent job at it.

#blog #analytics #bigquery #business data #data

What is GEEK

Buddha Community

Introduction to Google BigQuery

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

BigQuery : Petabyte Scale Data warehouse In GCP

In GCP , BigQuery is serverless way of doing petabyte scale analytics. This blog explains about BigQuery data warehouse solution on GCP.

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Introduction

BigQuery is a data warehouse that is built for the cloud. Its google proprietary data warehouse solution on Google Cloud Platform.

BigQuery is Serverless that means as a customer we don’t have to configure/manage any servers & storage.It will be done behind the scene by google. as a customer, our job is to upload the data and query that means which just focus on business rather than thinking about infrastructure.

BigQuery is not a transactional database like Mysql or Oracle. BigQuery is designed for analytical workloads.

For Example, Query like below is called an analytical query because its purpose is to analyze the data and provide some calculative results like count, max, min, avg, etc.

Here we trying to find titles and total_views for each Wikipedia page.

SELECT title,

count(views) as total_views
FROM
`bigquery-public-data.wikipedia.pageviews_2020`
WHERE
DATE(datehour) = “2020–04–18”
GROUP BY
title
ORDER BY
total_views
DESC;

Analytical queries are very useful in reporting and business intelligence because it provides insights from data based on which Business side can make the tactical decision for the company.

Architecture

Being Serverless we actually don’t need to know about underlying architecture but in knowing it would be helpful for us to optimize our query, cost & performance in some scenarios.

BigQuery is built on top of Google Dremel technology which is used inside google since 2006 in many services in production. (Please refer reference section for the paper)

Dremel is google’s interactive ad-hoc query system which is designed to query read-only data. BigQuery uses Dremel for its execution engine.

Apart from Dremel BigQuery uses Google’s innovative tech like Borg, Colossus File Syste, Jupyter network, and Capacitor.

#introduction-to-bigquery #bigquery-for-beginners #gcp-data-warehousing #data-warehouse #google-bigquery #data analysis

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

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Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

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Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

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On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

Chaz  Homenick

Chaz Homenick

1622443244

Introduction to Google BigQuery

Learn the basics of Google BigQuery in this introduction as well as ideal use cases and best practices.

It is incredible to see how much businesses rely on data today. 80% of business operations are running in the cloud, and almost 100% of business-related data and documents are now stored digitally. In the 1960s, money made the world go around but in today’s markets, “Information is the oil of the 21st century, and analytics is the combustion engine.” (Peter Sondergaard, 2011)

Data helps businesses gain a better understanding of processes, improve resource usage, and reduce waste; in essence, data is a significant driver to boosting business efficiency and profitability.

This reliance on data isn’t without challenges though. A business can have large data warehouses and no efficient way of processing the data in them. There is also the challenge of sorting valuable data from noise, primarily when you collect data from public sources. Amassing data is meaningless without the tools and means to process, analyze, and act on it. So the important questions are: How can you make this process painless and how do you become a successful data-driven company? The answer to both lies in Google BigQuery.

#bigquery #big-data #google