GCP - BigQuery

Summary

This script offers an in-depth look at Google's BigQuery, a fully managed, serverless data warehouse solution designed for analytical use cases. It highlights BigQuery's ability to handle large-scale data analysis with its separation of storage and compute, allowing for scalability and cost-effectiveness. The script also covers various data ingestion methods, unique features like machine learning integration, and compares BigQuery with other cloud data warehouse solutions, emphasizing its serverless advantage and ease of use.

Takeaways

• 😀 Relational databases are characterized by ACID properties, supporting atomicity, consistency, isolation, and durability.
• 🔗 Cloud SQL is a managed SQL variant that offers vertical scaling, while Cloud Spanner provides horizontal scaling along with Cloud SQL's features.
• 📚 NoSQL databases offer flexible schemas and come in various types such as wide column, key-value pair, document, and case-based databases.
• 🌐 Bigtable is a wide column database with an HBase interface, making it suitable for big data projects.
• 📊 For analytical and business intelligence use cases, data warehouses like BigQuery are essential for ingesting and analyzing data from various sources.
• 🛠️ BigQuery is Google Cloud's serverless, petabyte-scale, and cost-effective analytics data warehouse designed for OLAP use cases.
• 🌐 BigQuery's architecture decouples storage and compute, allowing independent scaling and providing flexibility and cost control.
• 💾 Storage in BigQuery is managed by Colossus, Google's global storage system optimized for reading large amounts of structured data.
• 🔍 BigQuery's compute is powered by Dremel, which executes SQL queries and manages the execution tree, mixers, and slots for processing power.
• 📈 BigQuery offers unique features like multi-cloud capabilities with BigQuery Omni, built-in machine learning with BigQuery ML, and integration with BI tools through BI Engine.
• 🌍 BigQuery provides public datasets, allowing users to query up to one terabyte of data per month at no cost from a repository of over 200 high-demand datasets.

Q & A

What are the key features of relational databases?

Relational databases support ACID properties which include atomicity, consistency, isolation, and durability. They also support relational hierarchy.

What are the differences between Cloud SQL and Cloud Spanner in Google Cloud?

Cloud SQL is a managed SQL variant that provides vertical scaling, while Cloud Spanner offers everything Cloud SQL provides plus horizontal scaling.

What types of NoSQL databases are mentioned in the script?

The script mentions wide column, key-value pair, and document-based NoSQL databases.

Which Google Cloud services are used for NoSQL databases?

Bigtable, which is a wide column database, and Memorystore, which is a managed in-memory data store, are mentioned as Google Cloud services for NoSQL databases.

What is the purpose of a data warehouse in the context of the script?

A data warehouse is used for business intelligence use cases where all data is ingested at one place for reporting tools to provide actionable insights. It supports analysis on both batch and real-time data.

What is BigQuery and how does it differ from traditional data warehouses?

BigQuery is Google Cloud's fully managed, serverless, and petabyte-scale analytics data warehouse designed for OLAP use cases. It differs from traditional data warehouses by decoupling storage and compute, allowing independent scaling and offering a serverless architecture.

How does BigQuery's architecture support its serverless nature?

BigQuery's architecture decouples storage and compute, connected via a petabit network, allowing it to scale independently on demand without the need for managing any infrastructure.

What are the components of BigQuery's architecture?

BigQuery's architecture includes Dremel for compute, Colossus for storage, Jupiter for the petabit network, and Borg for orchestration.

How does BigQuery handle data ingestion?

BigQuery allows data ingestion through streaming, batch loading, or bulk data uploads. Data can be accessed via SQL compliant clients, REST API, web UI, CLI, and client libraries in multiple languages.

What are some unique features of BigQuery?

BigQuery offers multi-cloud capabilities with BigQuery Omni, built-in machine learning with BigQuery ML, integration with Vertex AI, BI Engine for accelerating BI workloads, connected sheets for analyzing data in Google Sheets, geospatial data types, federation to process external data sources, and access to public datasets.

How does BigQuery compare to other cloud data warehouse solutions like AWS Redshift and Snowflake?

BigQuery is a true serverless solution with no need to manage nodes or infrastructure, offering on-demand or flat-rate pricing based on slots, and native AI/ML support with Google Cloud services.

Outlines

00:00

🗂️ Introduction to Databases and BigQuery

This paragraph introduces the concept of databases, focusing on relational databases and their ACID properties. It differentiates between Cloud SQL and Cloud Spanner for relational data, and Bigtable and Memory Store for NoSQL data. The paragraph then transitions to analytical use cases, emphasizing the need for a data warehouse and business intelligence solutions like BigQuery. BigQuery is described as a fully managed, serverless data warehouse that scales to petabyte levels and is cost-effective, supporting OLAP operations and various analytical features.

05:02

🛠️ BigQuery Architecture and Usage

The speaker delves into BigQuery's architecture, highlighting its serverless nature where storage and compute are decoupled. BigQuery's ability to ingest data through streaming or batch loads and access it via various interfaces like SQL clients, REST API, and CLI is discussed. The architecture's flexibility and cost control are emphasized, contrasting traditional data warehouse solutions. The paragraph also covers BigQuery's underlying technology stack, including Dremel for compute, Colossus for storage, Jupiter for networking, and Borg for orchestration, and the importance of efficient query writing to manage costs.

10:02

📊 BigQuery Data Ingestion and Query Execution

This section explains how data is loaded into BigQuery, either by creating a new table or modifying an existing one. It outlines the process of using gsutil to upload data to Cloud Storage and then transferring it to BigQuery. The paragraph also mentions the user-friendly interface of BigQuery, where SQL queries can be executed to analyze large datasets quickly. The efficiency of BigQuery in reading only the necessary columns for a query is highlighted, along with the ease of use and the lack of operational overhead for users.

15:03

🌐 BigQuery Features and Integrations

The paragraph discusses the unique features of BigQuery, including multi-cloud capabilities with BigQuery Omni, built-in machine learning with BigQuery ML and integration with Vertex AI, and the BI Engine for accelerating BI workloads. It also covers the ability to analyze large datasets in Google Sheets through Connected Sheets, support for geospatial data types, and federation capabilities to process external data sources. The availability of public datasets in BigQuery is mentioned, allowing users to query up to one terabyte of data per month for free.

20:06

🏅 BigQuery as a Cloud Data Warehouse Solution

The final paragraph compares BigQuery with other cloud data warehouse solutions like AWS Redshift, SQL Data Warehouse, and Snowflake. It emphasizes BigQuery's serverless design, which eliminates the need for upfront investment and operational management. BigQuery's flexible pricing model based on slots, reduction in operational expenses, and native AI/ML support are highlighted as reasons why BigQuery stands out as a clear winner among cloud data warehouse solutions.

Keywords

💡Relational Databases

Relational databases are a type of database that stores and retrieves data based on the relational model. They use tables, rows, and columns to organize data and relationships between tables are defined through keys. In the video, relational databases are discussed in the context of their ACID properties, which stand for Atomicity, Consistency, Isolation, and Durability, ensuring reliable data handling.

💡ACID

ACID is a set of properties that guarantee that database transactions are processed reliably. The acronym stands for Atomicity, Consistency, Isolation, and Durability. The video emphasizes that relational databases support ACID, which is crucial for maintaining data integrity and ensuring that transactions are processed accurately and completely.

💡Cloud SQL

Cloud SQL is a fully managed database service provided by Google Cloud. It supports MySQL, PostgreSQL, and SQL Server. The video mentions Cloud SQL as a managed SQL variant that offers vertical scaling, meaning it can increase the capacity of a single server to handle more workload.

💡Cloud Spanner

Cloud Spanner is a fully managed, horizontally scalable, relational database service by Google Cloud. Unlike Cloud SQL, which offers vertical scaling, Cloud Spanner provides horizontal scaling, allowing it to distribute the database across multiple servers, thus scaling out to handle more significant loads and larger datasets.

💡NoSQL

NoSQL databases are non-relational databases that are more flexible with their data storage and retrieval. They can handle a variety of data models, including key-value, document, wide column, and graph. The video discusses NoSQL in the context of its flexible schema and suitability for different types of data storage needs.

💡Bigtable

Bigtable is a fully managed, scalable NoSQL database service by Google Cloud that is optimized for large analytical and operational workloads. It is mentioned in the video as a wide-column database with an HBase interface, making it suitable for big data projects.

💡Data Warehouse

A data warehouse is a system used for reporting and data analysis. It centralizes data from one or more disparate sources, providing a single point of access for business intelligence tools. The video discusses the need for data warehouses in the context of ingesting all data for reporting and analysis.

💡BigQuery

BigQuery is Google Cloud's fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. The video describes BigQuery as a solution for OLAP (Online Analytical Processing) use cases, providing built-in machine learning, geospatial analysis, and business intelligence capabilities.

💡Serverless Architecture

Serverless architecture refers to a computing model where the cloud provider dynamically manages the allocation and provisioning of servers. In the context of BigQuery, as mentioned in the video, this means that there is no need for users to manage any infrastructure, allowing them to focus on querying and analyzing data without worrying about operational overhead.

💡Data Transfer Services

Data Transfer Services in BigQuery allow for the automated transfer of data from various SaaS applications and partners into BigQuery. The video explains how these services can be used to ingest data from sources like Google Maps, YouTube, and other SaaS products into BigQuery for analysis.

💡BI Engine

BI Engine is an in-memory analysis service in BigQuery that accelerates BI workloads. It enables sub-second query response times and high concurrency for popular BI tools. The video discusses how BI Engine can be used to integrate with BI tools like Tableau for faster data analysis.

Highlights

Relational databases support ACID properties: atomicity, consistency, isolation, and durability.

Cloud SQL is a managed SQL variant with vertical scaling, while Cloud Spanner offers horizontal scaling.

NoSQL databases offer flexible schemas and include wide column, key-value pair, and document-based databases.

Bigtable is a wide column database with an HBase interface, suitable for big data projects.

Memorystore is a managed in-memory data store, and Filestore is a document NoSQL used for mobile and web clients.

Data warehouses and business intelligence use cases require the ability to ingest and analyze data from various sources.

BigQuery is Google Cloud's fully managed, serverless data warehouse for OLAP use cases.

BigQuery's architecture decouples storage and compute, allowing independent scaling.

Dremel, part of BigQuery's architecture, is a multi-tenant service for executing SQL queries.

Colossus is Google's global storage system, optimized for reading large amounts of structured data.

BigQuery's pricing is based on the number of slots used, which determines processing power.

BigQuery can be accessed through various methods including GCP console, command line, REST APIs, and client libraries.

Data can be loaded into BigQuery from Cloud Storage using gsutil or bq command, or directly through the web UI.

BigQuery's SQL interface is intuitive, providing query results and statistics on data processed and time taken.

BigQuery ML allows running machine learning models using SQL dialect, simplifying AI integration.

BI Engine accelerates BI workloads by providing sub-second query response times for popular BI tools.

Connected Sheets enables analysis of BigQuery data in Google Sheets without SQL knowledge.

BigQuery GIS supports geospatial analysis, combining BigQuery's serverless architecture with location intelligence.

BigQuery can federate external data sources, processing data in various formats without moving it into BigQuery.

Public datasets in BigQuery offer over 200 high-demand datasets from different industries for free querying up to 1 TB per month.

BigQuery is a clear winner in cloud data warehouse solutions due to its serverless design and native AI/ML support.

Transcripts

hi

let's revisit what we have learned so

far

when it comes to databases we have

relational databases

they have acid support it's atomic in

nature consistency isolation and it

provides you durability

and it also supports relational

hierarchy

when it comes to relational we have two

options cloud sql and cloud spanner

cloud sql user which is a managed sql

variant

it provides vertical scaling

whereas cloud is panel provides

everything

cloud sql provides plus it provides

horizontal scaling

on the nosql side

nosql flexible schemas wide column key

value pair document databases case-based

databases different options

we looked at bigtable which is a wide

column db

uh it also has hbase interface so it's a

good adoption for a lot of big data

projects

memory store which is a managed radish

in-memory data store file store which is

a document nosql and then it's also used

for mobile and web client

now that leaves us to

analytical

kind of use cases so there is a

requirement for building a data

warehouse

and business intelligence use cases

where you want to ingest all of the data

at one place so that all of the

reporting tools can connect to that data

and

give you actionable insights

also

you want to do analysis on batch as well

as real time data

you want to create a store

where

you can sync everything source from

everywhere so it's got nothing to do

with the specific structure of data

nothing to do with

again specific way to define that data

but

any data in any format

you should be able to sync it in

and make it available for others to use

it

you need a place where you can run pi

reports machine learning uh machine

learning models so typically um in the

on-prem world we used to have a lot of

solutions for data warehousing

but uh on cloud

there are some specific offerings from

different cloud

[Music]

platforms for their cloud data

warehouses

in google or within google the option is

bigquery so let's let's try to

understand this space now

so what is bigquery bigquery is google

cloud's fully managed by fully managed

what it means that it's serverless is

completely serverless you don't have to

manage any infrastructure behind it it's

a petabyte to scale

and cost effective analytics data

warehouse

if you ca if you understand the oltp or

olap

space

it's meant for olap kind of use cases

analytical use cases

that helps you manage and analyze data

with built-in features like machine

learning geospatial analysis and

business intelligence

now let's look at

the architecture of bigquery

so bigquery's serverless architecture

decouples

storage and compute

so you can see this is the storage bit

of it this is the compute bit of it

and they are connected via a petabit

network

you can ingest data

as in stream you can

load batch or bulk data

and these are the different ways you can

access this

uh through sql compliant clients rest

api web ui or cli and then you have

client libraries in in about seven seven

languages

now

the decoupling of storage and compute

actually allows bigquery to scale

independently on demand

this

structure offers both immense

flexibility

and cost control for customers

because they don't need to keep their

expensive computer resources up and

running all the time

and this is very different

from a traditional node based cloud data

warehouse solutions or even

on premises

mpp based systems this approach allows

customers

to bring in any size of their data into

data warehouse and start analyzing their

data

without worrying about

database operations and system

engineering

now let's

dig

a bit deeper into

uh

bigquery architecture so under the hood

uh bigquery employs a

vast set of multi-tenant services driven

by low-level google infrastructure

technology like dremel colossus jupiter

and borg so if you look at this

architecture

compute is dremel so dremel is compute

it's a large multi-tenant cluster that

executes sql queries so all of the sql

queries they get executed at this level

dremel turns sql into sort of an

execution tree

the

leaves of the tree are called slots

and to do the heavy lifting of reading

data from storage and any necessary

computation

the branches

of this tree are called mixers

which perform the aggregation

now any time

um when when you're looking to

purchase bigquery or use it for your

organization it's basically these slots

is how

you know your pricing comes into picture

so based on the different

number of slots that you buy

and you use those slots

is uh basically this whole execution uh

tree and

and the leaves

on that tree so

which in turns is about the processing

power of it then storage is colossus

clausus which is the google's global

storage system

it leverages the columnar storage format

and compression algorithm

to store data

and it's optimized for reading large

amount of structured data

then you have

jupiter in between which is

the petabytes network and that connects

dremel and colossus which in the sense

is compute and storage network

and then bigquery is orchestrated by

borg

which is google's precursor to

kubernetes so before google built

kubernetes

it was using borg

and the mixers and

the slots they are all run by blog which

allocates hardware resources now the

most important control that you can have

on bigquery is basically how you write a

query because the costing is determined

by the amount of data it's processing

so

you need to be very diligent in terms of

writing the specific queries

so

you can limit the amount of data

processed so select star form is

definitely

not a good option for

bigquery

with that let's uh

look at how you can use bigquery

so

bigquery can be accessed in multiple

ways using the gcp console

command line tool bq

by by using rest apis and then you can

use the client library such as java.net

or python

now while

loading data into

um

big

query you can either create a new table

or append to or overwrite an existing

table so this is a typical structure

how you will look at loading data so

here is your data you will use the

gsutil command to put that data into

cloud storage

and then

from there you can use bq tool to pull

that data into bigquery

similarly you can do that from web ui

console you from web ui console you can

load this data directly

and then you'll you can use bigquery

apis to actually query that data

and that queried result can be used

outside

if you look at the interface so it's a

very intuitive interface so you have a

query editor like in fact you can think

of any sql client you write a sql query

uh it will give you result

and at the same time it will give you

that

what was the time elapsed and what was

the amount of data that it

processed

so

as evident in in this particular example

it takes less than two seconds to

analyze uh in this particular case 28

gigabytes of data and and return the

results

bigquery engine is actually very smart

to read only the columns required to

execute the query

now

the best thing about bigquery is this is

what you use you bring your data and

then you write queries

and you know

you you get your results you don't have

to worry about where this query is

running how it is running

what sort of compute required to run

this query or any of the operational

overhead you just bring in your data and

you execute your query

with that

let's uh look at what are the different

ways you can actually bring data into

into query so

there are many different ways uh

for file csv or json or every kind of

data the

process we looked at you pull that data

into cloud

storage using gsutil command

or any of the client libraries and then

from cloud storage

you will push it to bigquery using bq

command

then

you can use

the data transfer services so bigquery

has a data transfer services to transfer

data from sas applications so you can

use uh sas

dds connectors to pull data from google

maps youtube and uh

there's a long list of sas products

marketing products a lot of products

from where

using the connector you can ingest data

into bigquery or you can use the partner

dts connectors as well

then apart from that

data fusion is google's etl tool

uh

any database that it supports a plug-in

or a connector you can use those

connectors uh from a lot of different

kind of databases to pull direct data

directly into bigquery

then from sap point of view you have sap

data services that you can use to ingest

data directly into bigquery and then

apart from that you have the partner

integration with lot of marketplace etl

tools like informatica fight tran or

confluent which you can use to push data

into

bigquery

now what are the other

some of the very unique features of

bigquery

bigquery provides

multi-cloud capabilities in the sense of

bigquery omni which is in preview it

allows you to analyze data across clouds

using standard sql and without leaving

bigquery's familiar interface

then

it has built-in machine learning and ai

integration so besides bringing machine

learning to data with bigquery ml

integration with vertex ai

which is again the manage platform for

entire machine learning life cycle and

tensorflow enables you to train and

execute powerful models on structured

data in minutes just with just sql so

this is very important feature that

you can run machine learning model

from

sql dialect

bi engine so

to accelerate bi workloads so anytime

you have data warehouse its uh

bi tools will be integrating with that

data

like uh

tableau so

you can turn on bi engine it's an

in-memory analysis service to achieve

sub-second query response time and high

concurrency for popular bi tools so any

bi tool which uses odbc or jdbc

connection you can hook that into

bigquery

uh through bi engine

connected sheets it allows users to

analyze billions of rows of live

bigquery data in google sheets

without

knowing sql so it's a very handy tool

for business users to play around with

data

geospatial data types so bigquery gis it

combines the serverless architecture of

bigquery with native support for

geospatial analysis so you can

augment your analytical workflows with

location intelligence

federation federation is very important

bigquery can process external data

sources in objective storage like cloud

storage

for different file formats like par k

orc

transactional databases like bigtable

cloud sql or spreadsheets in in your

google drive

all this can be done without moving the

data to bigquery

the last one is public data sets

and this is very this is a very useful

feature google cloud's uh

you know public data set repository

offers a powerful

data repository of more than 200 high

demand public data sets from different

industries

and these data sets are available

for you

to import into or

attach into your

bigquery projects and you can

straightaway start querying that

and you can query up to one terabyte of

data per month at no cost

now with this let's let's quickly take a

look at uh

bigquery uh

console

this is a very familiar interface of

bigquery

you have sql workspace and the data

transfer methods and you can also

schedule queries sql workspace is

is pretty much

the project that you create

and

the

200 plus

uh public data sets that that's

available to you it's very easy to load

data into

into your project you can just click on

add data and add and you can follow

through that in the lab section

now when we come on to this site the sql

query is like any sql compliant standard

query you run this query and it will

give you the results the same time it

will give you all the stats

that this query processed

these many megabytes uh

how long did it take for the query to

execute and then you can save the

results and explore the data out of the

box itself

so with that it's very easy to bring

data into bigquery

and that's it

you don't have to worry about any

infrastructure management or

worrying about how the queries are run

you can start writing your query

and

start getting the results and apart from

that as we talked about it has some

amazing and unique features

to make your cloud data warehouse

much more than

what a typical analytical uh data

warehouse solution will do

let's look at uh perspective on all of

the cloud data warehouse solutions

that's available in the market

so

when

we look at aws red shift

red shift is based on concept of nodes

which are again virtual nodes

but you need to deploy configure and

manage them

so there is leader node and then you

have compute nodes

when you come to seo side as your sql

data warehouse or the

synapse analytics

is again a cloud-based but you have

control node

and then you have compute nodes

what it does is that it leverages a

mpp architecture

to process polybase t sql queries

then you have snowflake

which is a managed data warehouse as a

service

that can be deployed on aws or azure

infrastructure

snowflake also separates

compute and storage resources and makes

use of an mpp architecture behind the

scenes

but

at the time of deployment

you have to select a pre-configured

virtual data warehouses in in various

sizes

like

small

to

medium

to large and extra largest so it's kind

of a teaser sizing

and it also provides you a separate

virtual warehouse for ingesting the data

now when we look at the

google

bigquery

nothing you have to manage

as long as you can bring in your data

you bring in your data and then you just

start writing queries so you don't have

to worry about

any of the underlying infrastructure

or any operation

or anything to do with node or node

configuration so google bigquery is

truly a serverless

cloud data warehouse solution which

gives you

analytical capability

and more

so to put it into perspective why

bigquery is a clear winner when it comes

to cloud data warehouse solutions is

elimination of upfront investment and

planning

bigquery serverless design is built

monthly with flexible on demand or flat

rate pricing which is based on slots

it gives you reduction in operational

expenses

it eliminates the need to manage virtual

enterprise data by house nodes as well

as the need to monitor troubleshoot

updates

tune

or any

plan for growth

it scales up or down as needed to meet

the changing

needs of your data

it

also reduces the time spent on

like etl management or new schema

modifications

and it provides you native aiml

support

with native integration with most of its

google's cloud services

so i hope this was useful thank you