Snowflake Competitors and Comparisons

Snowflake is a robust data warehouse platform businesses employ to store and manage large volumes of data effectively. Other key players that thrive in the market are Amazon Redshift, BigQuery, and Hadoop.

It is essential for companies to select the right data warehouse platform that meets their technical and business needs.

In this article, we compare Snowflake with other competitors and analyze their strengths and weaknesses in detail.

Let’s get started.

Table of Contents

• Overview of Data Warehouse Platforms
  
  • What is Snowflake?
  • What is Redshift?
  • What is BigQuery?
  • What is Hadoop?
• Data Warehousing Platforms Comparison
  
  • Snowflake vs. Redshift
  • Snowflake vs. BigQuery
  • Snowflake vs Hadoop
• When to use Snowflake
• When to use Redshift
• When to use Hadoop
• When to use BigQuery
• Frequently asked Questions

Overview of Data Warehouse Platforms

Before comparing Snowflake with its competitors, we will have a basic understanding of Snowflake, BigQuery, Redshift, and Hadoop.

Let’s begin!

What is Snowflake?

• Snowflake is a cloud-based data warehousing platform. It is known as a "data warehouse-as-a-service".
• It can handle both structured and semi-structured data.
• Unlike traditional solutions, Snowflake is a faster, more user-friendly, and more flexible data warehouse.
• It uses a unique, hybrid architecture design that binds the features of both shared-disk and shared-nothing architectures.
• The architecture separates its storage and compute layers. It helps scale up and down resources automatically based on demand.
• You don't need to preprocess data or perform complex transformations when using Snowflake.
• Snowflake allows you to load data in parallel without interfering with existing queries.

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What is Redshift?

• Amazon Redshift is a cloud-based data warehouse service that can be integrated with BI tools to make smarter business decisions.
• You can start the ETL (Extract, Transform, and Load) process with as little as a few hundred gigabytes of data and scale it up to any volume.
• You must launch a set of nodes called the Redshift cluster to build a data warehouse in Redshift.
• You can speed up query performance in Redshift using SQL-based tools regardless of the data size.

What is BigQuery?

• BigQuery is a fully managed, serverless data warehouse that is pivotal in managing large amounts of data.
• It is a Platform-as-a-Service (PaaS) that uses ANSI SQL for querying.
• It is an enterprise-level data warehouse that can process super-fast SQL queries using Google's processing power.
• Its centralized data store hosts all types of data, simplifying the creation of analytics with BI tools.
• BigQuery’s REST API enables easy dashboard creation on Google App Engine.

What is Hadoop?

• Hadoop is an open-source framework that implements distributed processing of large volumes of datasets across clusters of computers.
• It uses simple programming models for distributed processing.
• It allows businesses to scale up from one server to thousands of servers with local computation and storage.
• Businesses can easily handle challenges involving large volumes of data with Hadoop.
• You must add additional servers to the Hadoop cluster to increase storage capacity.

Data Warehousing Platforms Comparison

This section will compare Snowflake with Redshift, BigQuery, and Hadoop against various parameters. It will help you precisely understand where they shine and where they fall short.

Snowflake vs. Redshift 

Here’s a quick comparison table that shows how Snowflake and Redshift differ from each other.

Comparison Parameters	Snowflake	Redshift
Security	Provides many tools and features for data security and compliance, including ACL, RBAC, and MFA.	It offers many features, including access management, cluster encryption, SSL connections, cluster security groups, and sign-in credentials
Scaling	Allows instant scaling for heavy traffic without redistributing data Provides an auto-concurrency feature to set the minimum and maximum cluster sizes.	It provides better scaling but is not as fast as Snowflake. It takes less time to add new nodes to clusters.
Integration	Integrates with tools like Apache Spark, IBM Cognos, Tableau, Qlik, etc.  Offers limited integration with tools like Athena and Glue.	Integrates with AWS services like Athena, Kinesis Data Firehose, CloudWatch, Database Migration Service (DMS), and DynamoDB.
Pricing	Separate payment for computing and storage. It provides better pricing when query usage is minimal. There is no charge when clusters are idle.	Provides attractive discounts on long-term commitments. It charges per hour per node, covering both computing and data storage

 

• Ease of use

According to G2, the business software review company, Snowflake has a better rating than Amazon Redshift for ease of use and setup.

• Market trends

PeerSpot reports that Snowflake ranked first among the cloud data warehousing platforms, whereas Amazon Redshift ranked fifth.

• Pros of Snowflake
  • It has a multicluster shared data architecture that works on any cloud.
  • It is a highly secure data warehousing platform that complies with regulatory guidelines.
  • It uses virtual warehouses for caching data.
  • It uses micro-partitions for data storage, which takes less time for query processing.
• Cons of Snowflake
  • Snowflake doesn’t support on-premises deployment.
  • The user community is small.
• Advantages of Redshift:
  • It delivers outputs on large datasets with speed.
  • It provides the freedom to choose ETL, SQL, or BI tools.
  • It enables data encryption to level up security.
  • It supports automating repetitive tasks.
• Limitations of Redshift:
  • Redshift delivers poor results when it is used as a transactional database.
  • Loading data properly is essential for performance. Loading data in small batches will slow down execution.
  • You need to configure WLM properly. Otherwise, high-priority queues will be left behind low-priority ones.

Snowflake vs. BigQuery

The comparison table below shows the key differences between Snowflake and BigQuery.  Let’s take a look.

Comparison Parameters	Snowflake	BigQuery
Architecture	Uses a hybrid shared-disk and shared-nothing database architecture.	Adopts serverless architecture
Performance	Enables automatic performance tuning and workload monitoring to improve query performance.	Processes even petabytes of data in minutes.
Scalability	Scales resources up and down independently.	Provides resources automatically on an as-needed basis to handle large data workloads.
Security	It uses AES encryption, customer-managed keys, and federated user access via Microsoft ADFS, Okta, and SAML 2.0.	Allows federated user access via Microsoft Active Directory. It supports MFA and OAuth 2 for authorized account access.
Pricing	Separate payment for computing and storage.	Adopts a Query-based pricing model Charges for the amount of data returned for queries. Less expensive than Snowflake storage.

 

• Ease of Use

According to G2, Snowflake has gained an ease-of-use rating of 9.0. 

On the other hand, BigQuery earns just a little less than Snowflake, with a rating of 8.7. The image below shows the same.

• Market Share

PeerSpot reports that Snowflake is ranked first among the cloud data warehousing platforms with an average rating of 8.4. 

On the other hand, BigQuery ranks fourth in the category with an 8.2 rating. The image below shows the same.

• Advantages of BigQuery:
  • It provides operational simplicity, seamless scalability, and cost-effectiveness.
  • It is a fully managed and highly available platform.
  • It offers low storage costs for large data sets.
  • It excels in processing massive data volumes.
• Limitations of BigQuery:
  • It works only with Google Cloud infrastructure.
  • Its serverless architecture provides less flexibility.

Well! We hope you have clearly understood the key differences between Snowflake and BigQuery.

The Top 40+ Best BigQuery Interview Questions & Answers 2025 article can help you understand key concepts and prepare for interviews.

Snowflake vs. Hadoop

Next, we will compare Snowflake and Hadoop on multiple aspects to differentiate their capabilities.

Comparison Parameters	Snowflake	Hadoop
ACID compliance	Handles many read-consistent readings at the same time. Allows ACID-compliant changes	Doesn’t support ACID compliance. Excellent tool for processing ad-hoc queries
Performance	Virtual warehouses allow the separation of workloads and query processing.	The MapReduce model handles batch processing. Apache Spark handles stream processing.
Data Storage	It uses variable-length micro-partitions to store data Handles both small and large sets of data with ease	Divides data into pre-defined blocks duplicated across nodes. Stores the dataset on a single node for small data files under 1 GB.
Scalability	Scale data loads up or down in seconds.	Hadoop is difficult to scale. Allows expansion of a Hadoop cluster by adding more nodes, You can increase the cluster size, but not decrease it.
Security	Implements both two-factor and federation authentication, as well as Single Sign-On. Allows policies to be set to restrict access to specific resources.	Uses service-level authorization to verify client permissions Includes third-party vendor standards, such as LDAP Supports both traditional file permissions and ACLs
Pricing	Pricing is estimated for storage space and query processing time separately. The price per query is cheaper than that of Hadoop.	It is expensive to deploy, configure, and maintain. You must pay a high Total Cost of Ownership (TCO) for the hardware. It requires significant disk space and computing power.

 

• Ease of use

According to G2, Snowflake outperforms Hadoop in ease of use and setup. The image below depicts the same.

• Market trends

According to PeerSpot, Apache Hadoop ranks seventh among cloud data warehousing platforms. The image below depicts the same.

• Advantages of Hadoop
  • It provides good scalability by using the distributed computing model.
  • Since it is an open-source framework, it becomes a low-cost option.
  • It offers exceptional system resilience and fault tolerance.
  • It provides enhanced flexibility in data storage since it doesn’t require preprocessing.
• Limitations of Hadoop
  • The MapReduce ecosystem is large and has a steep learning curve.
  • It faces issues in handling large datasets.
  • It doesn’t provide robust tools for data management, quality and standardization.

This section might have helped you precisely understand Snowflake and Hadoop's differences.

For a foundational understanding, the Hadoop Tutorial covers what Hadoop is, its features, core components, installation, operation modes, and use cases

When to use Snowflake

Snowflake is the best choice when you need to:

• Use both structured and semi-structured data
• Compress data automatically and reduce storage costs
• Scale data up and down seamlessly
• Enable automated encryption to secure your data
• Avoid installation, configuration, and frequent updates

When to use Redshift

You can use Redshift when you need to:

• Use a cost-efficient solution for enterprise-level implementations
• Perform real-time analytics to make data-driven, informed decisions
• Build robust dashboards and generate reports automatically
• Process structured, semi-structured and unstructured data to gain insights
• Perform operations on data in S3  and save the results in S3

When to use Hadoop

You can use Hadoop when you need to:

• Perform production data processing and make analytics with big data
• Store diverse datasets and enable parallel data processing
• Find a low-cost storage option for transactions, click streams and machine data
• Store and analyze CRM data
• Store data without preprocessing, like data lakes

When to use BigQuery

Leveraging BigQuery is a top choice when you need to:

• Run complex analytical queries over larger datasets
• Reduce load on relational databases
• Store columnar data while maintaining privacy
• Use queries for the data that changes frequently
• Create a single source of truth for massive datasets
• Build data visualizations with Tableau, Looker Studio, and Catchr

Summary:

• Snowflake is a cloud-native multicluster shared data architecture.
• It scales compute and storage resources independently, providing high flexibility and elasticity.
• Amazon Redshift uses MPP architecture for data warehousing and analytical workloads.
• It stores data in columnar format, increasing query performance.
• BigQuery provides a serverless architecture to manage heaps of data without managing physical resources.
• BigQuery’s SQL ML models help generate predictive insights by training models on queries.
• Hadoop provides distributed storage and processing by breaking large datasets into blocks.
• The MapReduce programming model enables parallel processing of distributed data.

Frequently asked Questions

1. Is Snowflake better than Hadoop? 

Ans: No, both tools have their own strengths and weaknesses. Hadoop is an excellent choice for a data lake, an immutable repository of raw business data. On the other hand, Snowflake is a powerful data warehouse platform that provides high-level flexibility and scalability.

2. Which data warehouse is better for real-time analytics and is easy to set up?

Ans: Snowflake and BigQuery are the best choices for real-time analytics because of their elasticity and managed services.

3. Is Google BigQuery difficult to learn?

Ans: No, you can learn BigQuery easily if you understand SQL concepts, data structure, data loading, and streaming methods well.

Conclusion

Let’s sum it up! We hope this blog has provided insights into Snowflake, Redshift, BigQuery, and Hadoop. You have understood that each data warehouse solution has its own positive sides and downsides.

Now, you can pick the perfect one based on your technical and business goals and harness your data's full potential.

If you need further clarification about the platforms, you can step into MindMajix. Both beginners and experienced learners can take MindMajix courses to stay ahead in this competitive landscape.