Snowflake (NoSQL Database) Books (Jun 2026)

Download Books    Download Report    Other Technology Book Lists

1:
Build Pipelines for AI: An Essential Guide to Smarter Data Engineering
By: 
Year Published: 2024

2:
Building the Interoperable Lakehouse: Data Strategies for AI Leaders
By: 
Year Published: 2024

3:
Bulk loading from Amazon S3 using COPY
By: Snowflake
Year Published: 2020

4:
Bulk loading from a local file system using COPY
By: Snowflake
Year Published: 2023

5:
Cassandra: The Definitive Guide
By: Unknown
Year Published: 2010

6:
Create users and grant roles
By: Snowflake
Year Published: 2023

7:
Data Modeling with Snowflake: A practical guide to accelerating Snowflake development using universal data modeling techniques
By: Serge Gershkovich
Year Published: 2022

8:
Data Trends 2026: Manufacturing
By: 
Year Published: 

9:
Database Performance at Scale: A Practical Guide
By: Unknown
Year Published: 2023

10:
Getting Started with NoSQL
By: unknown
Year Published: 2013

11:
Getting Started with Snowflake
By: Snowflake
Year Published: 2020

12:
JSON basics
By: Snowflake
Year Published: 

13:
Learning Snowflake SQL and Scripting
By: Alan Beaulieu
Year Published: 2022

14:
Load and query sample data using SQL
By: Snowflake
Year Published: 2024

15:
Load data from cloud storage (Amazon S3)
By: Snowflake
Year Published: 

16:
Load data from cloud storage (Google Cloud Storage)
By: Snowflake
Year Published: 2023

17:
Load data from cloud storage (Microsoft Azure)
By: Snowflake
Year Published: 

18:
Loading JSON data into a relational table
By: Snowflake
Year Published: 

19:
Loading and unloading Parquet data
By: Snowflake
Year Published: 2022

20:
Making Sense of NoSQL
By: unknown
Year Published: 2014

21:
Next Generation Databases: NoSQL and Big Data
By: Guy Harrison
Year Published: 2015

22:
NoSQL Distilled
By: unknown
Year Published: 2012

23:
NoSQL Distilled
By: Pramod Sadalage and Martin Fowler
Year Published: 2012

24:
NoSQL For Dummies
By: unknown
Year Published: 2015

25:
NoSQL and SQL Data Modeling
By: unknown
Year Published: 

26:
NoSQL for Mere Mortals
By: Dan Sullivan
Year Published: 2015

27:
NoSQL the Ruby Way
By: Durran Jordan
Year Published: 2012

28:
NoSQL with MongoDB in 24 Hours
By: Brad Dayley
Year Published: 2015

29:
Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement
By: Unknown
Year Published: 2012

30:
Snowflake - The Complete Masterclass 2023
By: Unknown
Year Published: 2023

31:
Snowflake - The Definitive Guide: Architecting, Designing, and Deploying on the Snowflake Data Cloud
By: Joyce Kay Avila
Year Published: 2022

32:
Snowflake Data Engineering
By: Maja Ferle
Year Published: 2021

33:
Snowflake Essentials
By: Frank Bell
Year Published: 2021

34:
Snowflake Intelligence for CFOs
By: 
Year Published: 

35:
Snowflake PDF
By: Tom Coffing and Dave Cook
Year Published: 2016

36:
Snowflake Security: Securing Your Snowflake Data Cloud
By: Ben Herzberg and Yoav Cohen
Year Published: 2023

37:
Snowflake and SAP: Powering Enterprise AI with Unified Data and Context
By: 
Year Published: 2024

38:
Snowflake in 20 Minutes
By: Snowflake
Year Published: Unknown

39:
Snowflake the Definitive Guide
By: O'Reilly
Year Published: 2022

40:
The Essential Guide to Transactional, Analytical and Hybrid Data
By: 
Year Published: 2016

41:
The Snowflake Dummies Guide Series
By: Snowflake Inc.
Year Published: 

42:
Tuning the Snowflake Data Cloud: Optimizing Your Data Platform to Minimize Cost and Maximize Performance
By: Andrew Carruthers
Year Published: 2022

Download Books    Download Report    Other Technology Book Lists

Tags: List of Books,Database,

Using DeepSeek With GitHub Copilot

See All on GenAI    « Previously

---

Important Links

DeepSeek API Docs
DeepSeek V4 for Copilot Chat (By: Vizards)
Integrate with GitHub Copilot
Awesome DeepSeek Integrations

*** IMPORTANT ***

Optional: Vision Support

DeepSeek V4 is text-only, but the extension handles images automatically. Drop a screenshot into chat and it proxies through another installed Copilot model (Claude, GPT-4o) to describe the image before sending to DeepSeek. Run DeepSeek: Set Vision Proxy Model to pick which model handles image descriptions.



Help From Gemini

I don't have any other model for vision proxy to use with DeepSeek in GitHub Copilot.
How can I use screenshot with DeepSeek V4 in GitHub Copilot?

Because GitHub Copilot recently integrated DeepSeek V4 (both Pro and Flash models) directly into its ecosystem via the official "DeepSeek V4 for Copilot Chat" extension, there is a built-in architectural catch: DeepSeek V4 is entirely text-only, but Copilot handles screenshots using a transparent proxy system.

If you are using this extension or an OpenAI-compatible third-party bridge, you do not need a separate, external vision proxy model like your own Claude or OpenAI API key. GitHub Copilot uses its own built-in models (like GPT-4o or Claude 3.5 Sonnet) to act as that proxy entirely for free behind the scenes.

If it isn't working or you are getting an error (like unknown variant image_url or the screenshot is simply ignored), use the following steps to properly configure and use screenshots with DeepSeek V4 in Copilot:

The Setup Method

1

Verify Extension Prerequisites

Required version

1.Verify Extension Prerequisites:Required version.

Ensure your VS Code is updated to the latest version (1.116 or later) and your GitHub Copilot extension is active. Open Copilot Chat and ensure you can see DeepSeek V4 Pro or DeepSeek V4 Flash in your model picker.

2

Configure the Built-in Copilot Vision Proxy

Command Palette

2.Configure the Built-in Copilot Vision Proxy:Command Palette.

You don't need an external API key for vision. Open the Command Palette (Cmd+Shift+P on Mac or Ctrl+Shift+P on Windows) and run:

DeepSeek: Set Vision Proxy Model

3

Select an Installed Copilot Model

Choose fallback

3.Select an Installed Copilot Model:Choose fallback.

A dropdown will appear listing the native models your GitHub Copilot subscription already provides (e.g., gpt-4o or claude-3.5-sonnet). Pick one. This does not change your main chat model; it simply tells Copilot which of its native models to use to look at your screenshot.

4

Paste and Reference the Screenshot

In the Chat Window

4.Paste and Reference the Screenshot:In the Chat Window.

Drag and drop your screenshot into the Copilot Chat panel, or copy the image and press Ctrl+V / Cmd+V. Add your text prompt (e.g., "Fix the styling issue in this screenshot") and hit enter.

How it Works Behind the Scenes

When you drop a screenshot into the chat while DeepSeek V4 is active, the workflow bypasses DeepSeek's text-only limitation seamlessly:

1. The Interception: Copilot intercepts the image file before it hits the DeepSeek API.

2. The Description: It routes the image directly to the local native Copilot model you selected in Step 3 (like GPT-4o).

3. The Text Injection: That model generates a highly detailed text description of what is happening in the screenshot (UI layout, error message, code snippet).

4. The DeepSeek Execution: Copilot invisibly appends that text description to your original prompt and forwards it to DeepSeek V4. DeepSeek processes the instructions as if it "saw" the image.

Troubleshooting Tip: If you encounter a 400 Error (unknown variant image_url) when pasting a screenshot, it means your VS Code extension is trying to pass the raw image straight to DeepSeek's text endpoint. Update the DeepSeek V4 for Copilot Chat extension to the newest patch release, which fixes the format translation layer.

See All on GenAI    « Previously
Tags: Agentic AI,Generative AI,Large Language Models,

GCP (Google Cloud) Books (Jun 2026)

Download Books    Download Report    Other Technology Book Lists

1:
AI for Absolute Beginners: Easy Guide with Clear Explanations and Real-World Applications of Artificial Intelligence to Quickly Gain Confidence—No Tech Skills Required
By: Ava Sinclair
Year Published: 

2:
AI in 10 Minutes - The Only Guide You Don't Need for 2026: Master ChatGPT, Manus, and Any AI Tool with One Simple Prompt—A Hands-On Quick Start for Complete Beginners (Generation AI)
By: Rafi Boghosians
Year Published: 

3:
Cracking the Kubernetes Interview: Discover expert tips and best practices to ace your Kubernetes technical interviews
By: Viktor Vedmich
Year Published: 

4:
Data Science on Google Cloud Platform
By: Lak
Year Published: 2017

5:
Essential GCP Exam Study Tips from Certified Experts
By: Safwan Azeem
Year Published: 

6:
Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph
By: Ben Auffarth
Year Published: 2024

7:
Google Cloud Associate Cloud Engineer Certification and Implementation Guide: Master the deployment, management, and monitoring of Google Cloud solutions
By: Agnieszka Koziorowska
Year Published: 2020

8:
Google Cloud Certified Associate Cloud Engineer Study Guide (Sybex Study Guide)
By: Dan Sullivan
Year Published: 2020

9:
Google Cloud Certified Professional Cloud Architect All-in-One Exam Guide
By: Ghanizada
Year Published: 

10:
Google Cloud Certified Professional Cloud Architect Study Guide
By: Sullivan
Year Published: 

11:
Google Cloud Cookbook
By: Rui and Drew
Year Published: 

12:
Google Cloud Digital Leader Certification Guide: A comprehensive study guide to Google Cloud concepts and technologies
By: Bruno Beraldo Rodrigues
Year Published: 2023

13:
Google Cloud Digital Leader Certification Study Guide: The Ultimate AI-Powered Mastery Guide to Cloud Strategy, Innovation, and Digital Transformation
By: Rayithi, Mohan
Year Published: 

14:
Google Cloud Digital Leader Exam Study Guide (Google Cloud Pro-Certification Series)
By: Patil, Manoj
Year Published: 

15:
Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion
By: Dario Cabianca
Year Published: 2020

16:
Google Cloud Platform All-In-One Guide: Get Familiar with a Portfolio of Cloud-based Services in GCP (English Edition)
By: Praveen Kukreti
Year Published: 2019

17:
Google Cloud Platform in Action
By: JJ Geewax
Year Published: 2015

18:
Google Cloud for Beginners: Build Scalable Applications on GCP Step-by-Step
By: MIGUEL. FARMER
Year Published: 

19:
Google Cloud: GCP: Google Cloud Platform: Learn Google Cloud Platform from the Scratch: The Ultimate Guide for Beginners
By: Adney Ainsley
Year Published: 

20:
Kubernetes Up & Running
By: 
Year Published: 2017

21:
Kubernetes in Action
By: Marko Luksa
Year Published: 2018

22:
Mastering Qlik Sense Modern Cloud Analytics: Unlock advanced analytics and visualization power with Qlik Sense Cloud (English Edition)
By: Steve Rey
Year Published: 

23:
Official Google Cloud Certified Professional Cloud Security Engineer Exam Guide
By: Ankush Chowdhary
Year Published: 2020

24:
Official Google Cloud Certified Professional Data Engineer Study Guide
By: Dan Sullivan
Year Published: 2020

25:
Practical Wisdom for Software Professionals: Lessons Learned as a Software Engineer in the Real World
By: James N Gershfield
Year Published: 

26:
Professional Cloud Architect Google Cloud Certification Guide
By: Clapa & Gerrard
Year Published: 

27:
THE AI WORKSHOP: Your Complete Beginner’s Guide to AI Prompts: An A-Z Guide to AI Prompt Engineering for Life, Work, and Business- NO CODING REQUIRED (THE AI WORKSHOP by Milo Foster)
By: Milo Foster
Year Published: 

28:
The Kubernetes Book
By: Nigel Poulton
Year Published: 2017

29:
The Only Official Google Cloud Study Guide
By: 
Year Published: 2018

30:
The Quick & Easy Guide to AI for Absolute Beginners: 5-Minute Lessons on What AI Is, How It Works, and How to Start Using It — No Coding or Tech Skills Needed (Generative AI Made Simple)
By: John V. Sullivan
Year Published: 

31:
Ultimate Snowflake Architecture for Cloud Data Warehousing: Architect, Manage, Secure, and Optimize Your Data Infrastructure Using Snowflake for ...
By: Ganesh Bharathan
Year Published: 

32:
VMware Certified Professional - Data Center Virtualization (VCP-DCV) Certification Guide: Master data center virtualization with expert VMware vSphere guidance (English Edition)
By: Dinesh Shaw
Year Published: 2020

33:
Visualizing Google Cloud
By: Priyanka
Year Published: 

Download Books    Download Report    Other Technology Book Lists

Tags: List of Books,Cloud,

The Angry Man And The Buddha

All Buddhist Stories    « Previously    Next »

One day, a man arrived at the park where the Buddha was teaching, his face flushed with rage. He belonged to a different group and felt threatened by the Buddha’s growing influence. Before the gathered crowd, the man began shouting insults, calling the Buddha a fraud, a hypocrite, and a fool.

The Buddha did not interrupt. He sat calmly, listening with deep patience and absolute serenity. When the man finally ran out of breath and stopped his tirade, the Buddha smiled gently and asked him a question.

"My friend, if you buy a gift for someone, and that person chooses not to accept it, to whom does the gift belong?"

The man, surprised by the calm response, replied, "Well, it belongs to me, of course. I bought it."

The Buddha nodded. "That is correct. In the same way, I do not accept your anger, your insults, or your verbal abuse. They remain yours to keep. You can carry them home with you."

He then explained further: "If a person became angry with you, and you insulted them back, you would be accepting their gift. But if you remain calm, the anger returns to the person who brought it. Anger only hurts the one who holds onto it, like grasping a hot coal with the intent of throwing it at someone else; you are the one who gets burned."

The man stood in stunned silence. The weight of his own hostility collapsed under the Buddha's peaceful clarity. Realizing the truth of the words, his anger dissolved into shame and admiration. He bowed deeply before the Buddha, asking for forgiveness and to become one of his followers.

The Takeaway: This teaching illustrates the concept of Kshanti (patience or forbearance). It reminds us that we cannot control how others treat us, but we have absolute control over whether we accept their negativity or let it go.

All Buddhist Stories    « Previously    Next »

The Moon Cannot Be Stolen

All Buddhist Stories    « Previously    Next »

"The Moon Cannot Be Stolen" is one of the most famous Zen stories, capturing the essence of compassion and non-attachment. It centers on Ryokan, a 18th-century Zen master who lived a famously simple life in a lonely hut at the foot of a mountain.

One evening, while Ryokan was away, a thief snuck into his hut. He searched everywhere but quickly realized there was absolutely nothing to steal—Ryokan owned no money, no precious items, and barely enough food to survive.

Just as the thief was about to leave empty-handed, Ryokan returned and caught him in the act. Instead of becoming angry or frightened, the gentle master looked at the thief with deep sympathy.

"You have walked a long way across the mountain to visit me," Ryokan said softly. "You should not return empty-handed. Please, take my clothes as a gift."

The thief was utterly bewildered. Before he could protest, Ryokan stripped off his own robes and pressed them into the man's hands. Stunned and confused by this strange monk's overwhelming kindness, the thief grabbed the clothes and fled into the dark night.

Ryokan sat down completely naked on the porch of his empty hut, shivering slightly in the crisp night air. He looked up at the sky, where the full moon was shining brilliantly, casting a beautiful, serene light over the landscape.

He smiled to himself and whispered, "Poor fellow. I wish I could have given him this beautiful moon."

The Meaning Behind the Story

This simple tale highlights two core Buddhist philosophies:

• Non-Attachment (Anatta / Suññatā): Ryokan possessed nothing of material value, but more importantly, he had no emotional attachment to physical things. Because he viewed his robes as temporary loans from the universe, losing them caused him zero suffering.

• The True Source of Wealth: The thief represents a mind trapped by greed, chasing fleeting, material things that can easily be taken away. Ryokan, conversely, possessed a wealth that could never be depleted: a mind capable of appreciating the permanent, universal beauty of the moon.

All Buddhist Stories    « Previously    Next »

What is a Class Factory?

All Questions From This Chapter    « Previously

explain this text from a book in layman terms (it's completely bouncing off of me):

The default class factory is type. When you write

class Dog:
    pass

Python roughly does:

Dog = type('Dog', (), {})

---

This is one of those Python lines that looks mysterious until you realize it's just showing what Python does behind the scenes.

Let's start with the code you normally write:

Python
class Dog:
    pass

Most people think this creates a class using some special Python magic.

What actually happens is that Python asks another object called type to create the class.

Behind the scenes, Python roughly does:

Python
Dog = type('Dog', (), {})

Let's decode each piece.

---

Step 1: What is type?

You've probably used it like this:

Python
x = 5
print(type(x))

Output:

Python
<class 'int'>

So it seems like type() tells you the type of an object.

But here's the twist:

Python
print(type(int))

Output:

Python
<class 'type'>

Even classes themselves are objects.

And the thing that creates classes is type.

Think of it like this:

Dog object  <-- created by -- type
Cat object  <-- created by -- type
Person object <-- created by -- type

type is the "class factory."

---

Step 2: The first argument

Python
type('Dog', (), {})

First argument:

Python
'Dog'

This is simply the class name.

Equivalent to:

Python
class Dog:
    pass

---

Step 3: The second argument

Python
()

This is a tuple containing parent classes.

Example:

Python
class Dog:
    pass

has no explicit parent class, so:

Python
()

means "no parent classes specified."

Another example:

Python
class Dog(Animal):
    pass

would roughly become:

Python
Dog = type('Dog', (Animal,), {})

Notice the tuple contains Animal.

---

Step 4: The third argument

Python
{}

This dictionary contains everything inside the class body.

Example:

Python
class Dog:
    species = "Canine"

    def bark(self):
        print("Woof")

becomes approximately:

Python
Dog = type(
    'Dog',
    (),
    {
        'species': 'Canine',
        'bark': bark
    }
)

The dictionary is basically:

attribute name -> value
method name    -> function

---

Visualizing the process

When Python sees:

Python
class Dog:
    pass

it internally thinks something like:

1. Create an empty dictionary {}
2. Put class variables and methods into it
3. Call type()
4. Store the result in Dog

So:

Python
class Dog:
    pass

is roughly:

Python
Dog = type('Dog', (), {})

---

Proof

These two are essentially equivalent:

Normal syntax

Python
class Dog:
    def bark(self):
        print("Woof")

Using type

Python
def bark(self):
    print("Woof")

Dog = type(
    'Dog',
    (),
    {'bark': bark}
)

Both work:

Python
d = Dog()
d.bark()

Output:

Python
Woof

---

The big idea

There are objects, classes, and something that creates classes.

Think of a car factory:

Factory ---> creates Cars

In Python:

type ---> creates Classes
Class ---> creates Objects

Example:

Python
Dog = type('Dog', (), {})

type
  ↓
Dog class
  ↓
Dog()
  ↓
dog object

This idea becomes important later when learning metaclasses, because a metaclass is simply a custom replacement for type that can control how classes themselves are created.

All Questions From This Chapter    « Previously