Snowflake vs Databricks vs AWS Redshift vs Azure Synapse

Selecting the right data warehouse is crucial.

In this blog, we’ll compare and analyze the Data Warehouses that are Snowflake vs. Databricks vs AWS Redshift vs Azure Synapse. We’ll analyze their features, performance, scalability, and suitability for different businesses, helping you make the best choice for your data analytics needs.

We’ll also take a closer look at four Data Warehouses: Snowflake vs Databricks vs AWS Redshift vs Azure Synapse, We’ll explore what makes each of them special and how they can help businesses manage their data effectively.

Topics covered in this blog are:

Cloud Data Warehouse
Database vs. Cloud Data Warehouse
Benefits of Cloud Data Warehouse
Snowflake
Databricks
AWS Redshift
Azure Synapse
Understanding Differences
Key Reasons
When To Use
Want to build a career in Data Engineering?
Use Cases
Pricing
Limitations
Conclusion

Cloud Data Warehouse

A data warehouse integrates data from various sources for quick access. It stores structured and semi-structured data from operational databases and other systems, enabling analysts to use it for business intelligence and analysis.

The market for data warehousing might develop at a compound annual growth rate (CAGR) of 10.7% from 2020 to 2028, reaching $51.18 billion.

Data warehouses can be deployed on the cloud, on-site, or a combination of the two. On-premise setups require physical servers, making scaling costly and challenging. Online data storage is cheaper and offers automatic scaling.

Experience the transformative power of a fully managed, scalable, and adaptable cloud data warehouse. Enjoy pay-as-you-go pricing, seamless integration with other cloud services, reduced operational complexity, and elastic scalability, revolutionizing your data management.

Read more at Introduction To Modern Data Warehouse

When to employ a warehouse for data

There are several applications for a data warehouse. As a single source of truth, it can be used to store historical data in a unified context.

Database vs. Cloud Data Warehouse:

Traditionally, OLTP databases like PostgreSQL suffice for smaller data sets, but cloud-based data warehousing, such as with BigQuery, is now accessible even for modest data volumes thanks to affordable options and free query processing for the first terabyte.

Serverless cloud data warehouses significantly lower the total cost of ownership, streamlining analytics. Moreover, a rich ecosystem of integration tools, observability solutions, and business intelligence offerings further accelerates the analytical processes of popular cloud data warehousing platforms.

Benefits of Cloud Data Warehouse

Scalability: Easily scale your storage and compute resources based on your needs.
Cost-effectiveness: Pay only for the resources you use, with no upfront investment required.
Flexibility: Access data from anywhere with internet connectivity and integrate with various tools and platforms.
Performance: Enjoy high-speed querying and analytics with optimized cloud infrastructure.
Security: Utilize robust security features and compliance certifications provided by cloud providers.

What is Snowflake Service?

With Snowflake, any organization can use the data cloud to mobilize its data. Snowflake provides a consistent data experience across various clouds and locations, regardless of where the data or users reside. The Snowflake Data Cloud powers the companies of thousands of customers in a variety of industries, including 691 of the Forbes Global 2000 (G2K) as of January 31, 2024.

Snowflake, a SaaS-based data platform, seamlessly operates on major cloud service providers like AWS, Microsoft Azure, and Google Cloud Platform. It offers real-time data consumption, sharing, warehousing, engineering, and data science, along with robust security features. Snowflake’s core components include cloud services, query processing, and database storage, providing a comprehensive end-to-end data processing and management solution.

Integrated with GCP, Azure, and AWS, Snowflake offers a fully managed service with flexible use cases and pay-as-you-go pricing.

What is the Databricks Service?

Databricks is a comprehensive solution for data analytics that integrates data science and data engineering throughout the whole machine learning lifecycle, from managing ML configurations to preparing data.

Its numerous and distinctive properties enable businesses to use AI. Meanwhile, users can manage a multi-cloud lakehouse architecture with Databricks SQL vs. Snowflake cloud services. Businesses in the energy and utility, financial services, and advertising and marketing sectors will find the program suitable.

Read more at Azure Databricks Architecture Overview

It excels not only in various industries but also in the public sector, telecom, healthcare, and life sciences.

What is AWS Redshift?

Amazon offers a cloud-based data storage service called AWS Redshift. Petabytes of structured and semi-structured data from your operational database, data lake, and data warehouse may be queried using SQL.

A competitor to Snowflake, Redshift offers seamless integration with AWS, allowing query results to be saved in open formats to S3. With multiple data import options and easy setup akin to other AWS services, Redshift ensures data security through encryption.

Its flexible deployment options ensure fast query performance regardless of data size, and compatibility with SQL-based tools simplifies analysis. Users can easily set up a Redshift cluster, upload data, and start analyzing.

What is Azure Synapse?

Microsoft provides Azure Synapse, a PaaS-based cloud data warehousing solution. Combining enterprise data warehousing, data integration, and big data analytics, it is an endless analytics service. Additionally, Synapse is integrated with Power BI, Azure Machine Learning, and Azure Data Share. The next iteration of Azure SQL Data Warehouse, Azure Synapse Analytics, allows you to query data at scale on your terms with serverless or dedicated choices.

If you’re looking for a distributed, enterprise-grade, PaaS-based cloud data platform, go with Azure Synapse.
Additionally, it offers additional advantages over traditional SQL because of its T-SQL dialects, such as dedicated SQL, Apache Spark, and serverless SQL pools. With a variety of pricing options, it offers excellent value for money as well.

Azure Synapse Analytics has several ETL, modeling, analytics, and machine learning connectors, making it particularly suitable for businesses that employ Microsoft technologies. Additionally, it provides data pipeline management, code-free visualization, and BI tools, in addition to relational and non-relational data warehousing.

Read more at: Azure Synapse Analytics

Understanding Differences:

Features	Snowflake	Databricks	Amazon Redshift	Azure Synapse
Architecture	Snowflake’s cloud-based architecture integrates a SQL query engine with three main components: cloud services, query processing, and database storage.	Databricks enables collaboration among data scientists, engineers, and analysts on a single platform.	Amazon Redshift, from AWS, is a fully managed cloud data warehouse, designed for fast query performance and scalability, even at petabyte scale.	Azure Synapse uses a scale-out architecture to distribute computational processing among multiple nodes. It separates computation and storage, allowing users to scale computing independently of stored data.
Scalability	Offers instant elasticity to scale computing and storage independently based on workload demands.	Scales horizontally to handle large volumes of data and process tasks efficiently.	Provides scalable clusters to accommodate varying workloads and data sizes.	Offers on-demand scalability for both data warehousing and big data analytics workloads.
Integration	Integrates seamlessly with various BI tools, ETL pipelines, and data lakes.	Integrates well with other Azure services and supports various data sources and formats.	Integrates with the broader AWS ecosystem and supports connections from popular analytics tools.	Provides tight integration with other Azure services such as Power BI and Azure Machine Learning.
Performance	Offers high performance with optimized query processing and automatic scaling.	Utilizes in-memory processing and distributed computing for fast data processing.	Optimized for fast query execution and parallel processing of large datasets.	Provides fast query performance and optimization across both data warehousing and big data workloads.
Ease of Use	User-friendly interface with easy setup and management, suitable for users with varying technical skills.	Provides collaborative workspace and notebooks for data scientists and analysts.	Offers a familiar SQL interface and management console for easy administration.	Unified platform with intuitive tools for data integration, preparation, and analysis.

Key Reasons

According to a Statista-reported poll, 83% of US transportation and warehousing companies used WMS between 2015 and 2021.

Why Snowflake?

Allow both semi-structured and fully-structured data formats (JSON, Parquet, XML, ORC, and so forth).
Snowflake is a fully managed, cloud-deployed DWH that requires very little setup.

Read more at: Databricks vs Snowflake

Why Databricks?

Compatible with Bitbucket and Github
10x quicker than other ETLs

Why AWS Redshift?

Options for data encryption, access control, network isolation, etc.
Columnar storage improves performance by reducing disc I/O.

Read more at: Snowflake vs Redshift

Why Azure Synapse?

The analytics process can be streamlined by using its capabilities for data ingestion, preparation, management, exploration, and visualization.
To ensure data safety and regulatory compliance, it offers strong security features like data encryption, access controls, and compliance certifications.

When to use:

Snowflake

Companies that wish to leverage Snowflake’s distinct architecture—separate computation and storage—to improve data warehouse performance should do so. Concurrency with queries and users is almost infinite using this method.
For workloads requiring low latency and smaller data volumes, Snowflake is also perfect.

Databricks

When complicated data conversions, analytics, and machine learning activities are needed, Databricks is the best option.
Data scientists and analysts can work together easily on data exploration, experimentation, and model creation with Databricks’ collaborative workspace featuring notebooks.

Read more at Mastering Databricks

Amazon Redshift

If you’re looking for a data warehouse that can handle petabyte-scale data sets quickly and with an excellent price-performance ratio, choose Amazon Redshift.
If you use AWS products and want to make use of the powerful data analytics and machine learning capabilities of the platform, Redshift is especially suitable.

Azure Synapse

If you’re looking for a distributed, enterprise-grade, PaaS-based cloud data platform, go with Azure Synapse.
Azure Synapse Analytics has several ETL, modeling, analytics, and machine learning connectors, making it particularly suitable for businesses that employ Microsoft technologies

Want to build a career in Data Engineering?

Data warehouse expertise is vital for a successful data engineering career. It enables you to design and optimize data storage solutions for efficient processing and analysis. Proficiency in data warehouses unlocks opportunities to build scalable pipelines, drive business intelligence, and deliver actionable insights, enhancing your value in data engineering.

Use Cases:

snowflake:

Retail Analytics: Snowflake enables retailers to analyze supply chain, inventory, sales trends, and consumer behavior, optimizing processes and identifying market trends for improved customer understanding and streamlined operations.
Analytics for the healthcare industry: Snowflake stores and analyzes large healthcare data sets, including clinical trials, medical imaging, and patient records, supporting research, enhancing patient care, and optimizing resource utilization for healthcare organizations.

Databricks:

Predictive maintenance:

Databricks enables predictive maintenance in manufacturing, utilities, and transportation sectors by analyzing sensor data and maintenance records to prevent downtime and optimize schedules.
Fraud Detection: Databricks detects fraud in banking, finance, and e-commerce by analyzing transactional data, and swiftly identifying anomalies and fraudulent activities with machine learning algorithms to mitigate risks and losses.

AWS Redshift:

Financial Analytics: AWS Redshift efficiently analyzes large datasets of market data, financial transactions, and risk management, empowering financial institutions with tools for fraud detection, portfolio analysis, risk modeling, and compliance reporting.
Ad-Tech Analytics: AWS Redshift is commonly utilized in the advertising technology sector to analyze advertising campaigns, user behavior, and performance indicators. Ad agencies, publishers, and marketers leverage it to target audience segments, optimize campaigns, and maximize ROI.

Azure Synapse:

Supply Chain Optimization: Azure Synapse optimizes supply chains by analyzing supplier performance, transportation routes, and inventory levels, helping organizations enhance efficiency, reduce costs, and streamline processes.
Analytics for the Energy Sector: Azure Synapse analyzes large datasets in the energy sector, including production data and consumption trends, enabling energy firms to estimate demand, monitor equipment health, and optimize production.

Pricing

Snowflake:

Snowflake Usage ( Each unit is 1 cent of usage): $0.01 / unit

Databricks:

The amount of computational resources you use determines how much Databricks costs you. Databricks offers this pay-as-you-go option with per-second invoicing.
You can use Databricks Community Edition (completely open-source) if you want to use it for free with some restricted functionality, including training your data staff. If you want to completely test out Databricks, you may do so for free during a 14-day trial.

AWS Redshift:

With Amazon Redshift’s on-demand pricing, You will be charged for the duration that the cluster is operational at an hourly rate determined by the kind and quantity of nodes you have selected for your cluster.

For as little as $3 per hour, you can begin utilizing Amazon Redshift Serverless. You will only be charged for the computational capacity that your data warehouse uses when it is in use.

Azure Synapse:

When you pre-purchase Azure Synapse Analytics Commit Units (SCUs), you may save up to 28% compared to pay-as-you-go costs. These SCUs can be used over the next 12 months on any publicly accessible Azure Synapse product, except storage.

Your pre-purchased SCUs will be deducted from your Azure Synapse consumption at the retail price of each product until they are used up or until the 12-month period expires

Limitations

Snowflake	Databricks	AWS Redshift	Azure Synapse
Due to the managed nature of the infrastructure, there is limited control over it.	Relative to other platforms, there is limited support for certain data sources and formats.	Longer loading times for data than other rivals, particularly with bigger datasets.	Complexity in workflow and pipeline management and optimization, particularly for large-scale deployments.
A few complex SQL operations and functions have restrictions as compared to traditional databases.	The learning curve is a little bit higher for people who aren’t familiar with Apache Spark or distributed computing.	Compared to platforms like Databricks, there is less support for machine learning and advanced analytics.	Compared to specialized tools like Databricks, there is limited support for several complex analytics functions.
Reliance on internet access to perform cloud-based functions.	High computation requirements for complicated tasks might lead to an increase in cost considerations.	There are situations where scaling up or down a cluster can cause outages or performance deterioration.	To fully utilize its integrated data warehousing and big data analytics capabilities, further skills might be needed.

Conclusion

Understanding the differences and capabilities of data warehousing platforms like Snowflake, Databricks, AWS Redshift, and Azure Synapse is crucial for businesses and aspiring data engineers alike. This blog provides insights into their features, benefits, and real-world applications, enabling informed decision-making for organizations and career planning for individuals. It equips readers with the knowledge to navigate the data engineering landscape effectively and pursue successful career opportunities.

Frequently Asked Questions

Q1- How do Snowflake and AWS Redshift differ as cloud data warehouses?

When comparing Snowflake and AWS Redshift as cloud data warehouses, Snowflake offers a fully managed service with independent scaling for storage and compute, ideal for ease of use and flexibility. It suits businesses seeking a hassle-free, pay-as-you-go model. AWS Redshift, integrated within the AWS ecosystem, provides advanced customization and control over infrastructure, making it a better choice for users with specific performance needs and technical expertise. The decision depends on factors like ease of use, customization requirements, and budget.

Q2- What are the core features of Snowflake, Databricks, and AWS Redshift?

Snowflake, Databricks, AWS Redshift, and Azure Synapse excel in distinct areas. Snowflake offers a user-friendly, cloud-based SQL engine with robust database features, scalability, and seamless integrations. Databricks enables collaboration with interactive notebooks, task scheduling, and advanced runtime support for data science and engineering. AWS Redshift is a fully managed warehouse with column-oriented databases, MPP architecture, and strong security features. Azure Synapse provides scale-out architecture, on-demand scalability, and tight integration with Azure services, making it ideal for big data analytics and warehousing. Each platform is tailored to specific needs, from ease of use to advanced processing.

Q3- How does AWS Redshift function as a cloud-based data storage solution?.

AWS Redshift is a cloud-based data storage service that enables powerful SQL querying of petabytes of structured and semi-structured data from various sources, including data lakes and warehouses. Seamlessly integrated with the AWS ecosystem, Redshift supports flexible deployment, fast query performance, data encryption, and compatibility with SQL-based tools. Its user-friendly setup and multiple import options make it accessible, allowing users to quickly analyze data and save query results to Amazon S3 in open formats.

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