BigQuery vs Snowflake - Data Warehouse Comparison
In terms of Snowflake vs BigQuery, whether it's batching or streaming, time-series or cross-sectional data, megabytes or petabytes in size, both data warehouses work well to serve even the most complex data analytics, reporting, or prediction data use cases.
Even though these major data warehouse players implement similar principles, there are a few high-level differences worth mentioning, before the final Snowflake vs BigQuery decision is made. The dissimilarities mostly come down to compatibility, pricing and usability.
Although they may seem minor at first glance, for some businesses even the most subtle variance may play a crucial role. Customers should pay most attention to these, because both data warehouses work well in other aspects.
What is a data warehouse?
Data warehouse is a centralized data repository of information that are used for reporting, analysis, and making more informed decisions. Data regularly flows into the warehouse from operational systems, transactional systems, relational databases, and external data sources.
To stay competitive, data and analytics are crucial. Data warehouses store data efficiently and deliver results to users quickly, so they are the ultimate tools for business analysts, data engineers and data scientists, using business intelligence (BI) tools and SQL clients.
Data lake vs data warehouse
Data lakes are highly scalable storage repositories that complement data warehouses. Composed of structured, semi-structured data, and unstructured data formats from different sources, data lakes hold large volumes of raw data in native format until needed for use. Data is stored with a flat architecture and is queried as required.
If your organization needs to collect and store a lot of data, but doesn’t need to process and analyze it all straight away, a data lake is the way to go.
By contrast, data warehouses such as BigQuery and Snowflake process data for advanced querying and analytics. Generally, companies use a combination of a database, data lake, and data warehouse to store and analyze data.
However, any data warehouse solution cannot be used to substitute a relational database, as they are specialized in running analytical queries, not simple CRUD operations and queries.
BigQuery vs Snowflake comparison
Before we compare BigQuery and Snowflake, let’s take a brief look at what each solution offers.
What is Snowflake?
Snowflake is a cloud-based warehousing solution launched in October 2014. This data warehouse consists of three main components:
- Database storage
- Query processing
- Cloud services
The fully-managed Software-as-a-Service (SaaS) architecture is flexible, and can run on any of the popular cloud providers, including AWS, Azure, and Google Cloud Platform (GCP).
The solution decouples storage and compute functions, allowing clients to use and pay for them separately. With no hardware or software to select, install, configure, or manage, Snowflake users don’t have to dedicate manpower and money to set up, maintain, and support in-house servers. Moreover, it’s simple to move data into Snowflake using an extract, transform, and load (ETL) solution.
What is BigQuery?
BigQuery is a petabyte scale, cloud-based data data warehouse launched in May, 2010 and is integrated into the Google Cloud Platform.
Under the hood BigQuery, is the implementation of many different services Google worked on over the years to serve their vast and complex data centers. It’s a combination of Borg (compute), Colossus (distributed storage), Jupiter (the network), and Dremel (execution engine).
The fully-managed and serverless architecture helps customers manage and analyze data at scale via built-in features such as machine learning, business intelligence, and geospatial analysis.
How to choose the right data warehouse?
Choosing the best data warehouse for your needs and project is key. In terms of Snowflake vs BigQuery, there are a host of advantages and disadvantages to each, from high accessibility and design on the pros side, to cost considerations in the list of cons.
The main differences between both data warehouse solutions are:
Ecosystem and integration
Nowadays, it's easy to diverge dramatically from system to system in terms of solution design and overall technology approach. It’s also easy to lose a sense of integration with other technologies and approaches. It’s absolutely crucial to stay abreast of the 3 latest technologies and solutions to keep your system up to date.
Because of the different technologies and solutions, developers spend a lot of time designing proper communication channels from one system to another so they can talk to each other.
To retrieve data in batches, a good data warehouse should be compatible with various data source types like Hadoop, cloud storage like AWS S3, as well as data streaming queue services like Pub/Sub, AWS SQS, or Apache Kafka.
Getting data out is also a big integration factor: The warehouse must provide the data in the streaming and batching. Having rich and easy to use API/SDK helps a lot for data science to retrieve and feed data into the data model.
Additionally, popularity of the warehouse helps when it comes down to connecting analytical tools like Tableau, Microstrategy, Power BI or Google Data Studio. Those popular data insight and analytics tools support a huge variety of warehouses where connection is possible with no coding involved.
Performance (speed & reliability)
It's not only crucial to achieve good quality of information from the data, but also to get it in a timely and consistent manner:
- How quickly a data warehouse can process data to get the information?
- How often tables are updated with fresh data?
- How much consistency remains after data processing?
These are important factors for making the right decisions at the right time.
With distributed systems it’s easy to get duplicates or missing data due to processing being divided into multiple machines. Each of these machines keeps its own state, and in case they apply the same data transformation or calculation on a different chunk of data, it becomes very important how these workers are managed by the master node.
In other words, how those workers are managed will largely dictate how much throughput and consistency you see in the data.
Database features
Modern warehouses are usually rich in features, allowing for smarter work and queries. For example, Snowflake provides an easy to read and understandable execution plan – a directed acyclic graph that explains the order of operations taken during, before, and after SQL execution and data retrieval. That helps optimize cost and performance of queries that are run repeatedly and frequently.
With BigQuery, a valuable feature is that before execution of any query, the number of bytes are scanned, so the person can roughly estimate how much time a query will run and its cost before running it. That prevents costs rising and speeds up work by making sure the query takes the appropriate amount of time to calculate for the right purpose.
Maintenance
Another important aspect to consider is the cost and time of maintaining the warehouse. The more administrative, provisioning, and operating jobs that can be automated to avoid human error, the better.
With BigQuery, there’s absolutely no maintenance for the running of queries by the user. Everything is done by Google, by making sure there is enough compute power and resources to run the query. On the other hand, Snowflake gives users the option to create a virtual warehouse of an appropriate size to optimize costs and performance, that's highly dependent on business needs.
In some cases, and to save money, there’s no need to retrieve data in a few seconds and it’s fine to wait a few minutes. However, with the freedom to create and choose a virtual warehouse comes extra maintenance complexity, meaning you need to make sure virtual warehouses are available for appropriate jobs.
Security
The way the data is retrieved is extremely important. It’s important to look at how a data warehouse connects to the data sources:
- Is it over the public internet or can it be done over a private secure network?
- Is the connection encrypted?
- Is access to the warehouse interface available via the public internet or only through a virtual private network accessible only by VPN?
Important security aspects are hashing and encryption functionality, built in modern warehouses at data level. These help secure highly sensitive data like personal information, and are different from encryption at the network and storage level.
Costs
Both services are well-designed and work very well with a huge variety of projects.
In general, BigQuery is easier to start with for small companies, because it’s hugely simple to set up and there’s lots of public data available from the start. Moreover, BigQueryML machine learning makes predictions and simple data science discoveries even easier for teams who are comfortable using SQL syntax.
Additionally, companies using other Google products find integration straightforward. BigQuery also has a range of tools and optimizations for huge enterprises to operate their big data needs.
Meanwhile, Snowflake is great for those who want to avoid vendor lock-in and keep their data separate from big cloud providers. Snowflake simplifies processes, making it easy to implement, and you only pay for what you use. Therefore, small companies won’t have any issues with Snowflake.
Huge enterprise-grade projects are also well-catered for, because Snowflake’s performance is exceptional. Moreover, given the options to control and customize compute costs and performance, it’s possible to optimize overall costs.
Pros of Snowflake
By answering three key questions, the pros of Snowflake are clear:
| Key questions | Advantages |
| How can it help your business? | Support for different cloud providers (high accessibility) |
| What problems can you avoid? |
|
| How can it help your users? |
|
Cons of Snowflake
What are the negative aspects of Snowflake? Here are some of them:
- Users must be careful with time travel options, because these can build up costs very easily.
- Users must set and choose a virtual warehouse before doing most queries. Only metadata queries are excluded from that obligation.
- Minimum time interval available for scheduling tasks is one minute.
Pros of BigQuery
By posing the same three questions as above, BigQuery’s advantages are evident:
| Key questions | Advantages |
| How can it help your business? |
|
| What problems can you avoid? |
|
| How can it help your users? | Query validation and consumption estimation prior to execution |
Cons of BigQuery
However, as all tools, BigQuery is not free of some disadvantages, like:
- Limitations regarding data export
- Lack of compute customization for query processing to optimize costs
- Extra costs for data transfer services for scheduling queries
- Minimum time interval available for scheduling tasks is 15 minutes.
Snowflake vs BigQuery
Virtual data warehousing is the future. In a nutshell, both data warehouse solutions have a lot in common. The differences between them can be a deciding factor for business.
We believe that Snowflake it's a very promising toolset but regardless of your choice between BigQuery and Snowflake, our team can support your business with a dedicated solution based on any of the two data warehouses and other data science services.
