SPEAKER: More and more companies are looking to unlock business
insights from their data.
But it can be hard to scalably ingest, store, and analyze
that data as it rapidly grows.
Google's enterprise data warehouse, BigQuery,
was designed to make large-scale data analysis
accessible to everyone.
In this series, we'll show you how
BigQuery can help you get valuable insights
from your data with ease.
Ask questions and get answers on "BigQuery Spotlight."
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If you're a developer, data analyst,
or just about anyone else, you're
probably working with data.
If your business has small amounts of data,
you might be able to store it in a spreadsheet.
But as your amount of data grows to gigabytes,
terabytes, or even petabytes, you
start to need a more efficient system like a data warehouse.
That's because all that data isn't very useful unless you
have a way to analyze it.
Traditionally, larger sets of data
mean longer times between asking your questions
and getting answers.
Have you ever needed to wait hours or days for an analytics
report to be run?
BigQuery was designed to handle massive amounts of data,
such as log data from thousands of retail systems or IoT data
from millions of vehicle sensors across the globe.
It's a fully managed and serverless data warehouse
which empowers you to focus on analytics instead of managing
infrastructure.
By design, BigQuery helps you avoid the data silo problem
which happens when you have individual teams
in your company having their own independent data marts.
This can create significant friction between analyzing data
across teams and cause challenges
with data version control.
Thanks to the integration with Google Cloud's native identity
and access management, you can assign read or write
permissions to specific users, groups, or projects,
and keep your sensitive data secure,
all while still collaborating across teams.
Working with data in BigQuery involves three primary parts--
storage, ingestion, and querying.
Google handles running everything else.
BigQuery is a fully managed service,
which means you don't need to set up or install anything.
And you don't require a database administrator.
You can simply log into your Google Cloud project
from a browser and get started.
First, let's talk about BigQuery's storage.
Data is stored in a structured table, which
means that you can use standard SQL for easy querying and data
analysis.
For example, let's say that you have
some data that represents the sales for each of your stores
in the last year.
You could probably use a smaller database for that.
But what if you have thousands of stores?
And what if you want revenue broken up by product SKU
or by region per time period?
BigQuery is perfect for big data because we
manage all that storage and the scaling operations
automatically for you.
Of course, storing the data doesn't
matter if you can't get into BigQuery in the first place.
Thankfully, there are lots of ways
to do that, as BigQuery is integrated
with the rest of the data analytics platform from Google.
You can upload data from Cloud Storage and just streaming
data from Cloud Dataflow, build an ETL pipeline using
Cloud data fusion, import data from a variety of file formats,
or use a combination of all of these.
We'll go over these and more in future episodes
so that you can decide which setup
is right for your data sets.
Once your data is in BigQuery, you're
ready to start answering those questions.
BigQuery supports the same Structured Query Language,
or SQL, that you may be familiar with if you
worked with ANSI-compliant relational databases
in the past.
We'll go over multiple ways to work with data in BigQuery
so that you can choose what's best for your use case.
Plus, you'll be able to share access with other users,
so they can derive insights from your data, too.
Want to skip straight to analyzing some data?
You can bypass the ingestion and storage steps
by analyzing the BigQuery public data sets.
These are third-party data sets which have been made public
for anyone to query against.
Google handles all the storage so that you
can focus on figuring out answers to questions,
like, how does the weather in New York City
affect demand for taxicabs?
In this series, we'll walk you through the play-by-play, some
of the most common tasks in BigQuery,
and share some best practices to help you up your game.
But if you want to get started right now, click on the link
below, and start with the BigQuery sandbox.
The sandbox gives you a free environment to trial BigQuery.
And you can get started by analyzing public data
sets immediately.
Look out for our next episode of "BigQuery Spotlight."
And remember, stay curious.