Do you really need a cloud data warehouse in today’s data management landscape? The honest answer is yes. These days, data warehouses are relatively inexpensive way to get a holistic look at all the data behind running your business. But without a data warehouse, analyzing the true impact of your ad campaigns, sales team, product usage, customer lifecycle, shipping process, and more becomes very difficult to manage. Choosing one does not have to be overwhelming or expensive. Many data warehouses offer 30- and 60-day free trials, and some can be a permanent solution for free cloud computing if working with small datasets.

If you’re not sure which database is right for you, and you’re just getting started, Google BigQuery is a good entry point into the world of warehousing data. In this blog and video, we’ll show you how to get it set up, then give you an easy way to automate loading your data into it. That way you can spend far less time manipulating data and far more time analyzing it. But first, let’s take a look at different data warehouses, so you can get a sense of how they compare.

Data Warehouse Comparisons

  Google BigQuery Amazon Redshift Snowflake Microsoft Azure
Architecture Shared-nothing MPP Shared-nothing MPP Hybrid (shared-disk and shared-nothing) Shared-disk MPP
Server management Serverless More self-managed More serverless More self-managed
Deployment Cloud-based Cloud-based Cloud-based Cloud-based
Data loading ETL/ELT, data streaming ETL/ELT, data streaming ETL/ELT, data streaming ETL/ELT, data streaming
Data backup/recovery Yes Yes Yes Yes
Performance Good Good High High
Pricing Flat rate, on-demand On-demand, managed storage On-demand, pre-purchase Compute charge, storage charge
Scalability Horizonal & vertical Horizonal & vertical Horizonal & vertical Horizonal & vertical
Security High High High High
Integrations Google Workplace, data integration, BI and AI tools AWS ecosystem, data integration, BI and analytics tools Data integrations, BI and analytics tools Microsoft software, data integration, BI and ML tools
Implementation User-friendly, need knowledge of SQL commands and ETL Knowing PostgreSQL or similar RDMSs facilitates deployment Intuitive, simple to use, requires solid SQL and DW architecture knowledge Easy to use, requires SQL and Spark use experience
Best for those who: Have varied workloads Process large datasets Need easy deployment and configuration Need enterprise DWHs

What is Google BigQuery?

BigQuery is a cloud-based, serverless data warehouse that can automate the data management process for you. They have a pay-as-you-go pricing structure, but the first 10 GB of storage and 1 TB of querying are free every month. So, if you only have a small amount of data to store and analyze, it could technically be free for you.

Free is great! But so is a fully supported platform that runs on the Google cloud infrastructure. BigQuery offers a virtual private cloud environment (VPC) and shared resource pooling to minimize the work and risk involved in computing your data.

How Does Google BigQuery Work?

Before walking through how to set it up, it’s important to understand how BigQuery works. BigQuery runs on a columnar data structure, which means your data will be stored by column. It serializes all your data together based on each specific field you need to track. So, it will always be easy to find the exact piece of data you are looking for and query it. And, being Google, it integrates all Google-based platforms out of the box.

BigQuery can do a whole host of amazing things. It offers machine learning baked directly into the platform using simple standard SQL to build predictive analytics. You can finally narrow down the true lifetime value of a customer — and predict when they might leave you or are ready to make a big purchase.

Within BigQuery, data is immediately available for querying and publishing to BI tools. You can instantly integrate with BigQuery BI Engine for querying and Looker Studio for data visualization. Or you can natively integrate with a number of other data visualization and business intelligence tools. Sound good? Ready to get started? Here’s how to set it up.

Google BigQuery Setup Tutorial

In this video, we explain how to set up BigQuery. Don’t worry — it’s quick and easy.

Next Step, Loading Data (Stitch Makes it Easy)

Once you have BigQuery (or another data warehouse) in place, the next hurdle to maneuver is getting your data out of your platforms and loaded into that warehouse to prepare for analysis. You can fiddle with coding transformations to handle the dozens of completely disparate APIs and land them into your cloud solution after hours and hours of engineering time. Or you can choose to move data from a selection of over 140 sources to BigQuery or another data lake or data warehouse in just a few minutes using Stitch.

What is Stitch?

Stitch is a fully managed ETL pipeline that can rapidly onboard data from sources like Hubspot, LinkedIn Ads, MongoDB, MySQL, Oracle, SurveyMonkey, and Zapier and many more to destinations like Snowflake, Amazon Redshift, Azure, Amazon S3, BigQuery, Panoply, PostgreSQL. With Stitch, you can push your data stack and analytics to the next level.

You’ll spend less time manually exporting and importing data, developing ETL, and managing your data pipeline, and have more time to analyze data. Stitch takes care of scheduling, maintenance, and adjusting to ever-changing data source APIs.

BigQuery + Stitch Make it Easy

BigQuery is one of the simplest platforms out there for data management. You get secure, cost-effective data storage and analysis all in one place. When partnered with Stitch, importing data from your most used data sources becomes a pain-free process. Stitch allows you to focus on actually analyzing your data in BigQuery, so you can maximize the business impact of your data.

Get started with Stitch today.

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