RYAN MATSUMOTO: Cloud Bigtable is Google Cloud's NoSQL
database that's optimized for use cases
with high numbers of reads and writes
per second at low latency.
In previous episodes, we learned about Bigtable speed, scale,
and reliability, as well as how it's
being used for use cases like personalization and fraud
detection.
In this episode, we're going to learn more about
how Bigtable can be used in the ad tech industry.
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Have you ever browsed one of your favorite websites
and seen an advertisement that just happens
to align with your interests?
The advertising technology, or ad tech industry,
is all about the systems, software, and tools
that advertisers use to deliver digital ads
to potential customers.
The goal of ad tech is to help both publishers and advertisers
optimize their online ad campaigns.
Publishers, which could include popular websites
and mobile apps, want to monetize their digital space
by serving ads to their users.
Advertisers on the other hand, want
to optimize the ROI of ad campaigns
for their products by efficiently reaching
target customers.
One of the most popular ad tech models
is the open exchange, where automated real-time bidding
options are used to decide which ad appears in front of a user
as they load a page.
Another important part of ad tech is using the power of data
to help digital advertisers reach the right audiences.
Some examples of data that might be helpful
include demographics, referring sites, search queries,
behavior on the site, and interaction with ad content.
When you go on a publisher app or website,
the publisher might send information
to an ad exchange that will look you up
by identification or by cohort and characteristics.
Different ad tech companies might
offer different solutions, but the pattern of looking up
the user is similar.
Sounds like a good case for a key value store, no?
Bigtable is a great database solution for ad tech companies
because of several key business critical challenges they face.
First, ad tech companies need to make sure
that they can serve ads as fast as possible
when a user opens a page.
Delivering ads faster translates to a better user experience,
making potential customers more likely to click on ads
and improve ROI for advertisers.
In practice, this means that many ad tech companies
need database requests to have latency
on the order of single digit milliseconds.
Bigtable is a great option for this use case
since it's designed to support large numbers of reads
and writes per second at low latency.
Ad tech companies also need a database that can scale.
They might have billions of ad requests per day and millions
of requests per second.
This kind of extremely high throughput
might overwhelm many on-premise or traditional databases.
Luckily, Bigtable is designed for this exact use case.
It supports up to millions of read
and write requests per second and can store up
to petabytes of data.
Throughput can be adjusted dynamically
by adding or removing Bigtable nodes,
each of which provides up to 10,000
additional queries per second as well as additional storage
capacity.
Ad tech companies also want to focus on their core technology
like the machine learning algorithms
used to optimize ad placement, rather than worry
about how to manage their underlying database
infrastructure.
As a fully managed database service,
Bigtable makes it easy for development teams
to focus on innovation rather than reinventing the wheel.
Bigtable replicates data across multiple Google data
centers all around the world to maintain high availability
and low latency for users everywhere.
This makes it easy for ad tech companies
to seamlessly expand to new markets
without having to worry about managing
the data centers themselves.
Bigtable also handles backups.
So ad tech companies can easily recover
from application-level data corruption, as well as
human error.
Ad tech companies also find Bigtable useful
because of how easy it is to integrate with other Google
Cloud products.
Some companies have used Memorystore from Memcached
to cache frequently queried data.
This can help get latency down to lower than one millisecond,
and also help optimize Bigtable costs.
Other companies have also extensively used
BigQuery, Google Cloud's large scale data warehouse
for storage and analysis.
While Bigtable can handle very high throughput at low latency,
BigQuery can be better for large scale data analysis.
In the ad tech space, BigQuery can
be helpful for data scientists who
may want to compile analytics and dashboards to help
advertisers and publishers meet their goals.
Powerful and complex queries that
may take several minutes to complete with other databases
may take just seconds with BigQuery.
And finally, ad tech companies rely heavily
on machine learning to take the data that's available
and use it to better connect advertisers
and potential customers.
Vertex AI is Google Cloud's unified machine
learning platform.
It makes it easy for businesses to build, deploy, and scale
ML models on Google's infrastructure.
As you can see, Bigtable's speed, scalability,
and reliability make it a great fit for the unique challenges
of ad tech.
If you're curious to learn more, take a look at the links below.
Thanks for joining us today, and take a look
at the other episodes in the series
to learn more about Bigtable.