cloud-based data warehouse: big query vs. snowflake vs. redshift
Today, business intelligence is essential for businesses to make smarter decisions and operate more efficiently. Data warehouses are at the core of business intelligence because they consolidate data from a wide variety of sources in a single location and make it available for data-driven transformation. But setting up and maintaining traditional data warehouses can be costly. This is why cloud-based data warehouses have become increasingly popular since they can provide real-time data for an affordable price while not requiring users to set up or manage their infrastructure.
When it comes to cloud-based data warehouses, BigQuery, Redshift, and Snowflake are among the best ones. Although these solutions are relatively close, they have marked differences that can influence how suitable each is to a given business use case. In this post, we’ll carry out a comparative analysis of these three cloud-based data warehouses and determine which one best suits a given business use case. First, let’s understand why you should choose cloud-based data warehouses instead of traditional data warehouses.
Why should you choose a cloud-based data warehouse solution?
Cloud-based data warehouse solutions eliminate the operational overhead of setting up data warehouse infrastructure and maintaining it. Apart from that, they also come with the following benefits.
With cloud-based data warehouses, you get to pay only for the resources that you need. When your workloads exceed what you initially anticipated, you can take advantage of their on-demand pricing plans to cater to emerging needs.
- Unlimited resources
Cloud-based data warehouses contain numerous servers and large storage spaces to facilitate data analytics at a scale of your choice.
- Integrations to boost capabilities
These data warehouse solutions typically exist within an ecosystem of other solutions which can extend their capabilities. For instance, BigQuery resides in the Google Cloud Platform which has other solutions such as Vertex AI und die DataPlex. Vertex AI adds machine learning capabilities to BigQuery and Dataplex helps to preserve data integrity.
These solutions have nearly complete availability, hence, they are very reliable. They are less prone to unexpected downtime that can severely hurt an enterprise’s operations.
That being said, we can now do the data warehouse comparison.
Cloud-based data warehouses in comparison – BigQuery vs. Snowflake vs. Redshift
BigQuery is a serverless cloud-based data warehouse that allows users to perform big data analytics of over petabytes of data. It is fully managed and Google handles the infrastructure setup and management for users. Because of its serverless nature, BigQuery avails compute resources on demand and it scales to zero when no queries are being run. With BigQuery, users can perform large-scale data analytics via ANSI SQL, which is designed for this purpose. In addition, BigQuery promotes an agile business model by utilizing columnar storage that allows faster and more efficient data querying. Using simple SQL, users can build machine learning models in BigQuery and incorporate them into their workflows BigQuery Omni allows users to share and analyze data across clouds making it a multi-cloud data platform.
Snowflake offers a cloud-based data warehouse and analytics solution as a Software-as-a-Service (SaaS). It is a scalable and highly flexible solution supported by ANSI SQL similar to BigQuery. It is fully managed but unlike BigQuery, it offers nearly zero administration responsibility to users; everything concerning infrastructure provisioning is handled by Snowflake. Snowflake works with the major cloud platforms to provide its users with a high-performance solution for querying data. Any Snowflake account can be hosted on Google Cloud, AWS, or Azure.
Redshift is Amazon’s cloud-based data warehouse solution for lightning-fast data analytics at a petabyte scale. Just like Snowflake and BigQuery, it has an industry-standard SQL query engine that sits on top of the data warehouse. Redshift is optimized for high performance as a result of parallel processing, columnar storage, impeccable data compression, and query optimization. It is very versatile and users can build data pipelines to bring data from a wide variety of sources to Redshift. It organizes its compute resources as clusters with nodes which greatly contributes to its capability in big data analytics.
Although all the data warehouses above are efficient, highly-performing, and can be used for the analysis of sizeable data, they have major differences in how they do it. This affects the business use cases for which they are suited.
Data warehouse comparison – main differences
The data warehouse architecture typically comprises a compute, client, and storage layer. But this differs in how the compute layer is built to operate across the three cloud-based data warehouses. The compute layer contains clusters with nodes for query processing. Data warehouse comparison by the architecture means how the nodes work and if they share disk space. This separates them into the following:
- Shared disk (Traditional)
- Shared-nothing (Modern)
BigQuery and Redshift
BigQuery and Redshift utilize the modern shared-nothing architecture coupled with Massive Parallel Processing (MPP). This means that the nodes work independently to process data in parallel and they don’t share any disk space. Each node is an independent unit having its storage.
Snowflake employs a hybrid shared-nothing and shared disk architecture. It stores data in two ways:
- In a centralized repository where users from any independent compute node can access (shared-disk architecture).
- Locally in the compute nodes of a cluster – where each node stores a portion of the data.
It is worth mentioning that BigQuery and Snowflake separate the storage and compute layers for better flexibility in scaling. For BigQuery, this allows automatic and rapid provisioning of more computer resources to handle large data loads. For Snowflake, this helps to scale storage and compute independently when necessary.
- Data type supported
BigQuery supports structured, semi-structured, and now unstructured data formats. The support for unstructured data was announced during the Google Cloud Next ’22 event. As a result, users can now unify, manage, and govern all types of data using this solution. Its integration with Dataplex ensures that enterprises can integrate trusted data into their operations. The semi-structured data types it supports include JSON and XML.
Snowflake supports both structured and unstructured data in the following formats: JSON, XML, Avro, and Parquet.
Supports structured and unstructured data in XML format. Redshift requires that data be defined by a structured schema. So if you have unstructured data, you’ll have to perform ETL on it.
- Loading of data
All of these cloud data warehouses support Extract Transform Load (ETL) and Extract Load Transform (ETL) data integration methods. Users can transform the data before or during loading.
In addition to supporting ETL/ELT data integration:
- BigQuery – loads data row-by-row using Streaming APIs.
- Snowflake – decides the best way to transform data after loading it.
- Redshift – can work with different Data Streams by using Data Manipulation Language commands like COPY.
All three have varied pricing models which can be suitable for specific situations. For reference here is a simple breakdown.
As far as pricing is concerned, BigQuery offers the most flexible model. It offers a hybrid of on-demand pricing for computing costs and flat-rate pricing for storage costs. Users are charged for the quantity of data returned per query and a flat rate for storage per month. BigQuery halves the storage costs if users subscribe to long-term plans.
Snowflake’s pricing model is a slight variation on the one BigQuery uses, in that instead of users being charged per query, they are charged for execution time. It also offers a flat rate for storage pricing per month but this price nearly doubles if the storage resources are in demand.
Redshift offers a multitude of pricing options. This includes per instance/cluster and on-demand pricing per hour. In addition, users are also charged for bytes scanned while querying against S3 (Spectrum pricing). Also, users can subscribe to pre-paid hourly plans that are heavily discounted (Reserved Instance Pricing).
To facilitate securing sensitive data, BigQuery lets users create policies and check access status at the column level. In addition, BigQuery utilizes Google Cloud’s Data Loss Prevention API to classify sensitive information and de-identify it so that it is meaningless in transit. By using techniques such as date shifting and tokenization, data in transit is encrypted by default. BigQuery is compliant with security standards like FedRAMP, PCI DSS, and so on.
Redshift’s security relies on input from Amazon and the user. Amazon secures the cloud and the user has the responsibility to secure their resources within their cloud. As a user you can secure your cloud using SSL certificates, setting up sign-in policies, etc. Redshift also offers compliance with security standards like SOC 1, 2, 3, ISO, HIPAA BAA, etc.
For Snowflake, users’ data warehouse security depends on the cloud provider’s features. On its own, it controls access management, and just like the others, it provides compliance with security standards like SOC 1 & 2 type 2, HIPAA, HITRUST, and so on.
To Wrap Up
Cloud data warehouses are becoming more popular as data aggregation and analysis tools because of the massive advantages they have over traditional data warehouses. BigQuery, Snowflake, and Redshift are some of the best cloud data warehouses today. These solutions are robust, reliable, and versatile – making them capable of catering to a wide variety of business use cases.
They are also relatively similar, and the choice comes down to the platform in which you want to operate your data. It is worth mentioning that if you want to work with unstructured data, then BigQuery has a competitive advantage over the two. But for general business use cases, each one of them has the capacity to be useful.
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