GCP - BigQuery
Summary
TLDRThis 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
🗂️ 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.
🛠️ 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.
📊 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.
🌐 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.
🏅 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.
Mindmap
Keywords
💡Relational Databases
💡ACID
💡Cloud SQL
💡Cloud Spanner
💡NoSQL
💡Bigtable
💡Data Warehouse
💡BigQuery
💡Serverless Architecture
💡Data Transfer Services
💡BI Engine
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
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
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