Snowflake vs Other Data Warehouses

Choosing the optimal data warehouse for your business needs can be challenging. Many companies find themselves at a crossroads when choosing a suitable Cloud Data Warehousing Platform.

Snowflake is a robust data warehouse platform used by businesses to store and analyze large volumes of data. Its competitors include Amazon Redshift, BigQuery, Azure Analytics, Databricks, and Hadoop.

This article compares Snowflake with its competitors and examines their strengths and weaknesses in detail. It will help you select the best platform for your data warehouse needs.

Table of Contents

• Snowflake vs Other Data Warehousing Platforms – Comparison
  
  • Snowflake vs Azure Synapse Analytics
  • Snowflake vs BigQuery
  • Snowflake vs Redshift
  • Snowflake vs Databricks
  • Snowflake vs Hadoop
• When to use Snowflake
• When to use BigQuery
• When to use Redshift
• When to use Databricks
• When to use Hadoop
• Conclusion
• FAQs

Snowflake vs Other Data Warehousing Platforms: Comparison

We’ll compare Snowflake with Redshift, BigQuery, Databricks, and Hadoop across various parameters. It will help you precisely understand where they shine and where they fall short.

1. Snowflake vs Azure Synapse Analytics

Let’s begin by comparing Snowflake and Azure Synapse Analytics. Before analyzing, we’ll understand what Snowflake and Azure Synapse Analytics are.

Snowflake Overview

Snowflake is a cloud-based data warehousing solution that supports the storage of both structured and unstructured data. You can store your data and perform various data tasks from a single location.

Snowflake is known as a 'Data Warehouse-as-a-Service' - a service model in which businesses can manage data storage, processing, and analysis. It makes data engineering operations faster, easier, and more flexible.

Let’s explore some more key features of Snowflake.

• Snowflake is a robust relational database system featuring a modern SQL engine.
• It has a multi-cluster, shared-data architecture that separates its storage and compute layers. This architecture helps Snowflake to scale up and down automatically based on demand.
• Snowflake supports micro-partitioning. That’s why it could manage both semi-structured and structured data.
• You don't need to preprocess data or perform complex transformations while using Snowflake.
• It enables speed performance optimization, data security, and secure data sharing.

Microsoft Azure Synapse Analytics Overview

Azure Synapse is Microsoft’s cloud data warehousing service. It is also called Azure SQL Data Warehouse because Synapse incorporates SQL Data Warehouse features and technologies. 

Let’s explore some interesting aspects of the Azure Synapse.

• Azure Synapse uses machine learning (ML) tools to store, manage, and analyze various types of data.
• It supports CSV files and allows for user-controlled source selection.
• It provides a unified workload for all tasks related to data handling, forecasting, and business analytics.
• It enables you to integrate IoT, cloud applications, and event nodes into a single platform for broadcasting.

Snowflake vs Azure – Key Differences

	Snowflake	Azure Synapse
Platform	Snowflake is a Software-as-a-Service (SaaS) solution that can operate on Google Cloud Platform and AWS. It uses an abstract model to isolate the Snowflake database from the core cloud and storage systems.	Azure Synapse is a Platform as a Service (PaaS) model that offers a free Azure Synapse Workstation development platform.
Compute Resources	Snowflake SQL databases are fully isolated from computing resources used for storing or querying.	Azure Synapse requires a dedicated SQL pool to build a robust SQL database suitable for data warehousing.
Flexibility	Snowflake offers high-level flexibility due to its multi-cluster, shared-data architecture. You can use virtual warehouses with near-infinite scalability and parallelism to meet your computing requirements without any downtime.	Synapse supports dedicated SQL pools and serverless SQL. Dedicated pools offer fixed resources, while serverless SQL adjusts automatically based on workloads.
AI Capabilities	Snowflake Cortex and Snowflake ML are some of the AI features of Snowflake.	Azure Synapse uses ML capabilities for data acquisition, modelling, deployment, and scoring.
Administration	Snowflake helps users with near-zero administration. It achieves this through automated grouping, built-in job scheduling, and materialized view management.	Azure Synapse requires additional administration for concurrency monitoring, process management, and tuning.

Summary of Comparison

Features	Snowflake	Azure Synapse
Pricing	You will obtain the cost per credit depending on the number and capacity of the warehouses.	There are no upfront expenses and no cancellation fees. You need to pay for only what you consume.
Scalability	Depending on the workload, both manual and automatic processes can be managed.	It's simple to scale up or down. Scaling is automated.
Security	Always-on-Encryption is used.	Transparent Data Encryption (TDE) helps defend against unauthorised activity.
Architecture	Separates computing, space, and cloud services to maximize efficiency.	Synapse SQL uses a scale-out design to distribute data processing across multiple nodes.
Data Integration	ELT/ETL is used for data integration.	Spark and SQL engines are tightly coupled.
Sharing	Multifunctional cloud data warehouses collaborate on reports and tasks.	Provides complete insight into the data-sharing partnerships. Transfer information to and from Azure in any format.
Backup and Recovery	This is accomplished through the usage of virtual warehousing and queries from a clone.	Backups are performed automatically.

2. Snowflake vs BigQuery

Before jumping into the comparison between Snowflake and BigQuery, let’s take a glance at the BigQuery platform.

BigQuery Overview

BigQuery is a fully managed, serverless data warehouse that supports scalable analysis of petabytes of data. It is a Platform as a Service (PaaS) that supports ANSI SQL queries.

BigQuery’s REST API enables businesses to build App Engine-based dashboards and mobile front-ends easily. Its centralised data store stores all types of data, simplifying analytics with BI tools.

• It provides operational simplicity, seamless scalability, and cost-effectiveness.
• It excels at processing massive volumes of data.
• It charges low storage costs for large data sets.

Snowflake vs BigQuery Comparison

Market Share:

According to PeerSpot, Snowflake ranks tenth among cloud data warehousing platforms, with an average score of 8.4.

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

Pricing:

Snowflake uses a time-based pricing model for computing resources, charging customers based on execution time.

On the other hand, BigQuery uses a query-based pricing model for compute resources. Customers are charged for the amount of data returned for their queries. BigQuery storage is slightly less expensive per terabyte than Snowflake storage.

Architecture:

Snowflake’s architecture is a hybrid of traditional shared-disk and shared-nothing database architectures. Snowflake uses a central data repository for persisted data, accessible from all Compute nodes.

Also, Snowflake processes queries using MPP (massively parallel processing) compute clusters. Each node in the cluster stores a portion of the entire data set locally.

When it comes to BigQuery, the platform manages all resources and automates scalability and availability, so administrators don’t have to allocate CPU or storage.

Ease of Use:

Both Snowflake and BigQuery fall on the “user-friendly” side of the spectrum when it comes to the question of ease of use.

On G2, Snowflake has an average ease-of-use rating of 9.0, while BigQuery has a rating of 8.2.

Scalability:

Snowflake allows users to scale their compute and storage resources independently. It includes automatic performance tuning and workload monitoring features.

As a serverless solution, BigQuery automatically provisions additional compute resources on an as-needed basis to handle large data workloads. This makes it easy to process even petabytes of data in just a few minutes.

Security:

For authentication, Snowflake supports federated user access via Okta, Microsoft Active Directory Federation Services (ADFS), and most SAML 2.0-compliant vendors. Snowflake offers granular permissions for schemas, views, procedures, tables, and other objects, but not individual columns.

On the other hand, BigQuery allows federated user access via Microsoft Active Directory. It only offers permissions on datasets, and not on individual tables, views, or columns.

Maintenance and Management:

Both Snowflake and BigQuery have low maintenance. In Snowflake, queries are tuned and optimised in the background while you work, and the size and power of your instance are automatically scaled based on changing needs.

In BigQuery, users will hardly even be aware of these considerations, since everything will run in the background.

Snowflake and BigQuery Summary of Comparison

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

Comparing 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 Okta, Microsoft ADFS, and SAML 2.0.	Allows federated user access via Microsoft Active Directory.
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

Well! We hope you now have a clear understanding of the key differences between Snowflake and BigQuery.

3. Snowflake vs Redshift

Before diving into learning Snowflake and Redshift comparison, let’s go through the key features of Redshift.

Redshift Overview

Amazon Redshift is a cloud-based data warehouse service that you can integrate with BI tools to make smarter business decisions. You can start the ETL (Extract, Transform, Load) process with a few hundred gigabytes of data and scale it up as needed.

You must launch a Redshift cluster to build a data warehouse. After that, you can upload datasets to run the data analysis queries. You can speed up query performance in Redshift using SQL-based tools regardless of the data size.

Let’s learn some more details about Redshift.

• It delivers results on large datasets quickly.
• It supports automating repetitive tasks.
• It enables data encryption to level up security.

Snowflake vs Redshift: Which is the Best Data Warehousing Tool?

Here's a quick overview of how these two solutions differ from each other:

Scaling:

Snowflake allows instant scaling under high demand without redistributing data or interrupting users. Auto-concurrency lets users set the minimum and maximum cluster sizes.

On the contrary, Redshift can scale, but not as instantaneously as Snowflake. It may take anywhere from minutes to hours to add new nodes to the cluster. Therefore, we can say that Snowflake has an advantage over Redshift.

Integration and Performance:

Redshift is a natural choice for organisations that are already working with AWS or using AWS services such as Athena and CloudWatch. It can also be integrated seamlessly.

On the other hand, Snowflake may introduce some challenges when integrating with tools like Athena and Glue. However, it provides easy integration with tools like Apache Spark, Qlik, IBM Cognos, and Tableau.

Pricing:

Snowflake charges separately for compute and storage, whereas Redshift bundles them together. Snowflake clusters incur no charges when there is no query load and shut down automatically when idle.

Redshift charges per hour per node, covering both compute and storage. With Redshift, you can calculate the monthly price by multiplying the price per hour by the size of the cluster and the number of hours in a month.

Moving data to the Warehouse:

Redshift uses the COPY command to load data, whereas Snowflake uses the COPY INTO command for loading data. Snowflake allows users to use multiple clouds and third-party storage services.

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.

4. Snowflake vs Databricks

Before navigating through the differences between Snowflake and Databricks, we’ll cover what Databricks is.

Databricks Introduction

Databricks is a market-leading cloud-based test automation platform for processing and transforming large amounts of data. It allows you to analyze data using machine learning algorithms.

Let’s check out some more about Databricks.

• Although Databricks is Spark-based, it also supports popular programming languages such as R, Python, and SQL.
• Databricks is secured through the Azure Active Directory architecture, which enables integration with the full Azure stack, including Data Lake Storage.
• It can also be used for smaller projects and improvements, and serves as a one-stop solution for analytics tasks.
• It connects to a variety of other resources, such as CSV files, SQL servers, and JSON files.

Snowflake vs Databricks: Which is better?

Data management:

Snowflake enables you to load and store structured and semi-structured files directly into the data warehouse.

On the other hand, Databricks supports all data types in their native formats. Moreover, Databricks may be used as an ETL tool to organize complex data.

Versatility:

Snowflake excels in SQL-based data analysis. Dealing with Snowflake data in scientific computing use cases requires reliance on their partner ecosystem.

Databricks supports high-performance SQL queries for Data Analytics. You can execute SQL queries at the high rates reserved solely for Database queries by using Databricks Delta Engine.

Pricing:

Snowflake provides four editions: basic, premium, professional, and enterprise.

On the other hand, Databricks offers three business pricing tiers for its subscribers: data science, business intelligence, and corporate plans.

5. Snowflake vs Hadoop

Here's a quick rundown of two such technologies – Apache Hadoop and Snowflake – to help business owners figure out which is the best fit for their specific needs.

Apache Hadoop Introduction

Hadoop is another data warehousing solution that operates on expensive MPP appliances. It is an open-source framework that uses simple programming models to enable the distributed processing of large data sets across clusters of computers.

You can expand Hadoop from a single computer system to thousands of computers using the MapReduce programming model. You just need to add extra servers to your Hadoop cluster to increase storage capacity.

Let’s get into some more points about Apache Hadoop.

• Hadoop can retrieve data from an RDBMS, store it on the cluster using HDFS, and clean and prepare it for analysis using MPP processing techniques.
• It has a shared-nothing architecture, which means it is a cluster of independent servers.
• It offers exceptional system resilience and fault tolerance.
• It provides good scalability through a distributed computing model.
• It provides enhanced flexibility in data storage since it doesn’t require preprocessing.

Snowflake vs Hadoop: A Quick Comparison

Now that you have a general understanding of both technologies, we can compare Snowflake and Hadoop on many aspects to determine their capabilities.

Features:

Snowflake can handle many concurrent read-consistent reads. It also allows for ACID-compliant changes.

On the contrary, Hadoop doesn't support ACID compliance, so it writes immutable files that can't be updated. You must read a file in and write it out with the changes made. That’s because Hadoop isn't an ideal tool for processing ad hoc queries.

Performance:

Snowflake's virtual warehouses are its most appealing feature. This allows you to separate or categorise workloads and query processing based on your needs.

Hadoop continuously collects data from many sources, regardless of the data type and stores it in a distributed environment. MapReduce handles Hadoop's batch processing, whereas Apache Spark handles stream processing.

Data Storage:

Snowflake uses variable-length micro-partitions to store data. It can handle small data sets as well as terabytes of data with ease.

Hadoop divides data into predefined blocks, which are duplicated across three nodes. For small data files under 1GB, where the complete dataset is normally stored on a single node, this is not a good solution.

Scalability:

Snowflake can scale from a small data warehouse to a massive one in a matter of seconds, and vice versa.

On the contrary, Hadoop is difficult to scale. Users can expand a Hadoop cluster by adding more nodes, but the cluster size cannot be decreased.

Ease of use:

Snowflake is a database that requires no maintenance. It is a fully managed system.

In Hadoop, you must perform cluster maintenance activities, such as patching and frequent updates. Hadoop is difficult to use, which requires highly skilled data engineers with expertise in Linux systems.

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.

Costs:

In Snowflake, you pay for the storage space used and the time spent querying data. You can set Snowflake's virtual data warehouses to pause while you're not using them. As a result, Snowflake's per-query price is much lower than Hadoop's.

Hadoop was considered to be inexpensive. You'll have to pay a high total cost of ownership (TCO) for the hardware. Hadoop's storage processing is disk-based, so it requires significant disk space and computing power.

Data Processing:

Snowflake features batch and stream processing, allowing it to serve as both a data lake and a data warehouse. Snowflake provides excellent support for low-latency queries through virtual warehouses.

Hadoop is a solution for batch processing massive static datasets. Hadoop, on the other hand, cannot run interactive jobs or perform analytics. This is due to batch processing's inability to respond to changing business needs in real time.

Security:

Snowflake keeps your data secure. Snowflake supports two-factor authentication, federation authentication, and single sign-on. User roles are used for authentication. Policies can be set up to restrict access to specific client addresses.

Hadoop uses service-level authorization to verify that clients have the necessary permissions to submit jobs. It also includes third-party vendor standards, such as LDAP.

Use Cases:

Snowflake offers individual virtual warehouses and excellent service for real-time statistical analysis. It supports query optimization, low-latency queries, real-time data ingestion, and JSON.

Hadoop's HDFS file system is better suited for enterprise-class data lakes or big data repositories that demand high availability and quick access.

Snowflake and Hadoop: A Glance

Next, we will summarize the comparison between Snowflake and Hadoop on multiple aspects.

Comparison Parameter	Snowflake	Hadoop
ACID Compliance	Handles many read-consistent reads simultaneously. 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 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 separately for storage space and query processing time. The price per query is lower than Hadoop's.	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.

Well! This cloud data warehousing platform comparison should help you better understand the differences between Snowflake and its competitors in-depth.

When to use Snowflake?

Snowflake is the best choice when you need to:

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

When to use BigQuery?

Leveraging BigQuery is a top choice when you need to:

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

When to use Redshift?

You can use Redshift when you need to:

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

When to use Hadoop?

You can use Hadoop when you need to:

• Perform production data processing and perform analytics on big data
• Store and analyze CRM data
• Store diverse datasets and enable parallel data processing
• Find a low-cost storage option for transactions, clickstreams and machine data
• Store data without preprocessing, like data lakes.

When to use Databricks?

You can employ Databricks when you need to:

• Deal with unstructured data such as audio, image, or text
• Build complex ETL pipelines
• Work with high-performance Apache Spark
• Handle both batch and real-time data streams
• Perform calculations on data.

Conclusion

Let’s sum it up! We hope this blog has provided interesting and helpful insights into Snowflake, Redshift, BigQuery, and Hadoop. So now you know both sides of the coin, and you are free to pick whichever suits your requirements.

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

Frequently Asked Questions

1. What are the key Snowflake competitors and alternatives?

According to Gartner Peer Insights, AWS, Google BigQuery, Cloudera, Teradata, and Databricks are among Snowflake's competitors.    

2. What is the difference between a data warehouse and a data lake?

You can store a large amount of processed, structured data in a data warehouse. On the other hand, you can store a large amount of structured and unstructured data in a data lake.    

3. Is Snowflake better than Hadoop?

Both Snowflake and Hadoop have advantages and disadvantages. We cannot say one is better than another.

Snowflake is an excellent data warehousing platform because it supports real-time data ingestion and JSON. Although it is more expensive to use, it is easier to deploy and maintain than Hadoop.

Hadoop is an excellent choice for a data lake, which has an immutable repository of raw business data.

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

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

5. Is Google BigQuery difficult to learn?

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

6. Is Snowflake better than Redshift?

Both platforms excel in offering data warehousing solutions. Snowflake integrates seamlessly with AWS, whereas Redshift integrates with non-AWS tools such as Spark, IBM Cognos, and Tableau.