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BigQuery vs Redshift: Which Data Warehouse For Embedded Analytics?

August 17, 2023

Mieke Houbrechts

Which data warehouse is best for blazingly fast dashboards, embedded in your SaaS platform? Read our detailed comparison of BigQuery vs Redshift.

Your SaaS product generates a ton of data, which can uncover many hidden insights for your customers. Think of a recruitment platform that shows HR managers where most applicants drop off. Or a marketing platform that shows digital marketers their best-performing campaigns in one glance.

But to build interactive, blazingly fast dashboards, you’ll first need a good data infrastructure. Which data warehouse is best for embedded analytics? In this article, we’ll compare two popular alternatives: Redshift vs BigQuery.

What is Amazon Redshift?

Amazon Redshift is a popular data warehouse service. It is fully managed, running exclusively on Amazon Web Services (AWS). Businesses use Redshift to store large amounts of data in a structured way. Whether you’re collecting sales transactions, customer information, SaaS platform usage statistics or all of the above, all of that data can be stored in Redshift.

Redshift integrates with many data analysis tools, so businesses can easily visualize data and make sense of it. Its architecture - which we’ll discuss in more detail later - is perfect for handling large datasets in reporting and analytics use cases. 

What is Google BigQuery?

Google BigQuery is a fully-managed cloud data warehouse, running exclusively on Google Cloud Platform (GCP). Using SQL-like queries, businesses use BigQuery to store and analyze massive volumes of data quickly and cost-effectively.

Similarly to Redshift, BigQuery is great for high-performance analytics and data exploration without managing complex infrastructure. This makes it a popular choice for business intelligence and customer-facing analytics.

Although they may seem alike at first, BigQuery and Redshift have plenty of differences to prefer one over the other. Let’s have a closer look.

BigQuery vs Redshift - Architecture

Although BigQuery and Redshift are both managed data warehouses, their architecture is different. Let’s look at the fundamental differences, so you can get a clearer picture of which is better for you.

Data warehouse management

In terms of data warehouse management, both BigQuery and Redshift are fully managed, meaning you don’t have to worry about:

  • Hardware and software provisioning
  • Creating backups
  • Software patches and updates
  • Usage and performance monitoring
  • Security features like encryption or identity and access management (Cloud IAM/AWS IAM)

Having a fully managed solution comes with many advantages, but with BigQuery and Redshift, you will be tied to their respective cloud platforms. If a cloud-agnostic provider is important to you, other tools like Snowflake are a better fit.

It’s worth noting that with Redshift, there are still a few aspects you will need to configure manually compared to BigQuery. Let’s have a look at why that is.

Data warehouse architecture of Amazon Redshift
Source

Serverless vs cluster-based architecture

The biggest difference between BigQuery and Redshift is how they handle managing resources. BigQuery has a serverless architecture, which means you don’t have to worry about adding more resources if your data grows - or reducing it when you need less. If you have unpredictable workloads and the volume of data changes constantly, BigQuery will save you a lot of time and headaches. As you execute more (or less) queries, it will allocate resources automatically.

Redshift, on the other hand, needs a little more manual management. It uses something called Redshift clusters, which lets you decide on the type and amount of nodes you need. Because you configure and manage the cluster's capacity yourself, you have much more control over your resources. If you want to control performance, and you don’t want your costs to skyrocket unexpectedly, Redshift is a better choice for you.

However, aside from its general cluster-based architecture, Redshift also launched a serverless offering. If you want to stay on an AWS stack, but you don't want to manage resources manually, it's worth checking it out.

Diagram of Google BigQuery architecture
Source

Storage and computing

How your data warehouse stores and processes your data will have a big impact on speed, efficiency, and the final price tag.

Unlike PostgreSQL and other typical relational databases, BigQuery and Redshift both use columnar storage. It means they store data in columns, rather than rows, which leads to much faster and more efficient analytical queries.

Redshift takes the upper hand because it offers additional compression techniques, which you can fully control for even better performance. However, this comes with a learning curve. If ease of use is more important to you than full control, you are better off with BigQuery’s automatic compression.

Although data storage is similar, the way BigQuery and Redshift handle storage and computing together is fundamentally different. While BigQuery separates them, Redshift traditionally couples them.

For that reason, the most flexible and cost-effective solution is BigQuery. If you have large amounts of data, but your workloads vary from heavy to lighter, you can easily scale the two independently and avoid paying for compute resources you won’t use.

If you want more control and predictable performance, Redshift is better. Storage and computing are tightly integrated, so you can optimize them together. If you often use the same specific data types and query patterns, this can come in handy. It is, however, less flexible and sometimes more expensive. 

BigQuery vs Redshift - Performance

BigQuery and Redshift are both quite fast and efficient for loading data. But they handle performance in different ways, for which you may prefer one over the other. Let’s look at some of the key differences.

Query execution and scalability

Both BigQuery and Redshift can deal with larger query volumes efficiently. Although they scale differently, they share the goal of optimizing performance. For example, a recruiting solution needs to be able to handle thousands of data points at once.

BigQuery uses Google’s Dremel technology, which is optimized for interactive queries over massive datasets. It uses automatic scaling, which means it will dynamically decide how many and which resources to allocate, based on the volume and complexity of queries.

If speed is essential, and you don’t want to manage infrastructure or pre-allocate resources, BigQuery is a good choice.

Redshift uses Massively Parallel Processing (MPP), which is a more traditional query approach. It will run queries across multiple nodes and cores, so you can process queries in parallel. It also uses concurrency scaling to deal with larger workloads. When you have more or heavier queries than your main cluster can handle, it will temporarily add capacity on-the-fly.

If you have datasets with complex joins across many large data sets, Redshift caters better to more complex querying tasks.

Performance optimization

Similar to its query execution, BigQuery handles performance optimization automatically. This means you don’t need to do manual indexing, while Redshift will need some manual tuning. For example, to accelerate queries, you’ll need to:

  • choose distribution keys (= which data sits in which node)
  • choose sort keys (= how to sort data in a table)
  • do ‘vacuuming’ (= reorganizing your data as data gets added or deleted)

This again proves BigQuery is the best choice for painless setup, while Redshift wins in terms of flexibility and control over performance.

It’s worth noting both data warehousing tools have caching in place to improve query performance. You can use benchmarks to help you decide which solution will have the best performance, but it will  ultimately depend on your specific requirements.

Example of performance benchmark tests of different data warehousesr
Example of a performance benchmarking test

BigQuery vs Redshift - Data types and integration

BigQuery and Redshift are perfectly suited for handling structured data with defined schemas. Both can handle semi-structured data as well, although BigQuery does it better out of the box. You can store and query JSON files and other nested formats.

Redshift can also query JSON and Avro files if you use specific functions, but you may be better off using the Redshift Spectrum feature for handling semi-structured data. It lets you query data directly in your Amazon S3 data lake without loading it into the main cluster.

For unstructured data, you’ll probably want to use an ETL tool on top of both data warehouses. Luckily, they offer powerful APIs to hook up your data warehouse to any data source, ETL tool, or BI platform for data visualization and embedded analytics.

Both data warehouses use SQL queries, which will feel familiar to any data engineer or developer. If you’re an avid user of PostgreSQL, Redshift will be an easy transition since it uses a variant of PostgreSQL as its query interface.

In terms of integration, your choice will depend on your existing ecosystem of tools. BigQuery integrates best with other Google Cloud services, while Redshift works seamlessly with all AWS services.

Google BigQuery vs AWS Redshift - Pricing model

Although price alone shouldn’t determine your choice of a data warehousing tool, it’s definitely worth considering when comparing options.

Google BigQuery offers an on-demand pricing model. You pay as you go for the amount of data processed during queries. This model is great if your workloads vary a lot. For example, if you’re running a SaaS platform for managing events or festivals, the summer months will be much busier than winter months.

If your workloads are predictable and you run a lot of queries consistently, Redshift might be cheaper than being charged per query. You pay for the capacity of your cluster, so as long as you stay within your node’s limits, you can run as many queries as you want.

Although BigQuery pricing is more flexible, Redshift’s flat-rate pricing is more predictable upfront. Both have their advantages and disadvantages, depending on your use case.

Which cloud data warehouse is right for you?

Redshift and BigQuery are both solid providers for SaaS companies who are building data analytics features. If you want to offer engaging charts and dashboards to your product users, both are great for fast loading and a smooth user experience. Which one is better depends on your use case.

Google BigQuery stands out for its ease of use. Scaling and managing petabytes of data becomes effortless with their many automations. If you want fast, on-demand queries without a hefty price tag, BigQuery is the best choice for you.

Amazon Redshift is the master when it comes to controlling data processing, scaling and performance. If you have a steady, high volume of queries, their node-based architecture is perfect.

Now that you know which data warehouse is best for you, you can start with the fun stuff: turning cloud-based data into interactive dashboards full of insights! If you need a good visualization tool that fits seamlessly into your SaaS product, look no further than Luzmo. It has out-of-the-box connectors to BigQuery and Redshift, so you can immediately start building dashboards for your product users in days, not months.

Our team of analytics experts will gladly show you a demo, and advise you on the best data infrastructure for your use case. Book a free consultation today!

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