Top 9 Snowflake Alternatives In 2025

‍Data warehousing is crucial for organizations to manage and analyze large volumes of data efficiently. Snowflake, a popular cloud-based data warehousing solution, has gained significant traction in recent years due to its scalability and ease of use. However, several Snowflake alternatives offer similar functionalities and features. This article will explore some of the top Snowflake alternatives available.

Traditional methods of storing and managing data, such as on-premise databases and data warehouses, are becoming increasingly outdated in the rapidly evolving technical landscape. These legacy systems are often expensive to maintain, require a large amount of physical storage space, and are challenging to scale as data volumes grow. Moreover, traditional data storage solutions are typically not designed to handle the massive amounts of unstructured data that organizations are now generating.

On the other hand, cloud-based data storage solutions offer a wide range of benefits that drive organizations to make the switch. Cloud storage is flexible, scalable, and cost-effective, allowing organizations to pay for only the needed storage and scale up or down as required. Cloud-based solutions like Snowflake and its alternatives also offer improved security and accessibility, enabling organizations to access their data anywhere.

What Is Snowflake?

Snowflake is a cloud-based data warehousing platform that allows users to store and analyze large volumes of data in a scalable and efficient manner. It separates storage and computing, which enables users to scale up or down based on their specific needs.

Snowflake supports various data formats and provides advanced data processing and analytics features. Overall, Snowflake is known for its performance, ease of use, and flexibility, making it a popular choice among businesses of all sizes.

Features Of Snowflake

Snowflake has a bucket full of features to help you streamline your data management and enhance productivity.

Some of the major features of Snowflake are:

• Storage Optimization: It combines organized, semi-structured, and unstructured data into one central location for unrestricted access to all the required data. It lets you handle your data with many features, like time travel, automated micro-partitioning, optimized compression, and ACID compliance. You can work with data already on-premises or in open table formats (in private preview) by deploying a variety of architectural patterns, such as data lakes, data warehouses, data meshes, and others.
• Elastic Multi-Cluster Compute: It utilizes a single engine to power interactive apps, feature engineering, large-scale analytics, complicated data pipelines, and more. It scales quickly and affordably to support almost any number of users and workloads simultaneously without sacrificing performance. Snowflake offers comprehensive SQL support and the Snowpark development framework for Python, Java, and Scala, among other versatile, extendable, and potent ways to program with data.
• High Server Capacity: Conventional data storage methods usually necessitate a large server and corresponding hardware investment. Snowflake data warehouses provide increased capacity without the need for extra hardware. Because the technology is entirely cloud-based, you may scale it up or down to apply it to the desired level.

Drawbacks of Snowflake

While Snowflake offers many benefits, there are some potential drawbacks to consider. You can choose Snowflake alternatives which addresses the drawbacks.

For instance, Snowflake's pricing model is based on the amount of data processed, which can lead to unexpected costs for organizations with fluctuating workloads.

Additionally, Snowflake may not be the best fit for organizations with very specific or unique requirements.

Here are some drawbacks of Snowflake: 

• No support for unstructured data: Snowflake handles both structured and semi-structured data, but does not support for unstructured data.
• On-demand Pricing: Pay-as-you-go pricing is undoubtedly one of Snowflake's strong points, although the service can cost more than its alternatives, such as Amazon Redshift. This is because your usage habits highly influence Snowflake pricing.
• Difficult-to-use GUI: Getting acclimated to the new GUI is still challenging. The interface used to be much cleaner and more straightforward, but as capabilities increase, you will occasionally find a more complicated interface. It takes some time to get familiar with its UI. 

9 Best Snowflake Alternatives

There are many Snowflake alternatives available in the market and to cut down your research. We have listed 9 of the best Snowflake alternatives below, with one bonus entry.

1. Amazon RedShift

‍Redshift is a managed data warehouse service provided by Amazon Web Services (AWS) that boasts columnar storage on a high-speed disc, massively parallel query execution, and quick query performance through machine learning. It is one of the Snowflake alternatives, completely scalable, and easily connected with other AWS services. 

• Best-price performance: With a fully managed, AI-powered, massively parallel processing (MPP) data warehouse designed for performance, scale, and availability, you may get up to 6x better pricing performance than any other cloud data warehouse.
• Combine all your data using zero-ETL: With a zero-ETL strategy for integrated analytics that requires no code or low code, you can effortlessly access or ingest data from your databases, data warehouses, streaming data, and data lakes.

Customer Rating 

• G2 - 4.3/5

2. BigQuery

BigQuery is a fully managed, multi-engine, multi-format, multi-cloud data analytics platform that is AI-ready. It helps you extract the most value from your data. BigQuery, the central component of Google's data cloud, makes it easier to integrate data, scale analytics economically and securely, and share rich data experiences with integrated business intelligence. This Snowflake alternative allows you to train and deploy machine learning models using a straightforward SQL interface. 

• Control all kinds of data and open formats: BigQuery can manage all cloud-based data types, including structured and unstructured, with granular access constraints. With its support for open table formats, you can use an integrated data platform while still using open-source technologies. 
• Real-time analytics: BigQuery can ingest streaming data, make it instantly queryable, and integrate it with other streaming technologies such as Dataflow. The in-memory analysis service BigQuery BI Engine provides high concurrency and sub-second query response times. Materialized views can speed up query performance and cut expenses in BigQuery. 

Customer Rating 

• G2 - 4.5/5

3. Teradata

Teradata is one of the most comprehensive cloud analytics and AI data platforms available. It provides unified data, reliable AI, and quicker innovation so that you and your clients can make smarter and more certain decisions. Some of the best businesses in the world, in different significant industries, rely on Teradata to enhance customer experiences, boost corporate performance, and completely integrate data throughout the organization. 

Customer Rating 

• G2 - 4.3/5

4. Cloudera

With Cloudera's hybrid data platform, you can leverage the capabilities of both public and private clouds. This is one of the Snowflake alternatives for those needing crystal-clear visibility into their data operations and centralized management. With the platform, you can have unparalleled freedom to select any cloud, data, and analytics, and you can benefit from optimized speed and a quicker time to value. Moreover, Cloudera supports you at every stage of the data lifecycle, from data distribution to machine learning and data science.

• Organize data flow with ultimate adaptability: It helps to transfer projects or data between clouds or from on-premises to any cloud with an automated and secure workload transfer. 
• Make the most of your cloud computing investments: It is one of the world's first hybrid data platforms designed for cost-effectiveness and interoperability. It helps in working with the utmost flexibility across multi-cloud and on-premises environments.
• Update your data stack: Its open data lakehouse, which is driven by Apache Iceberg, has a scalable open architecture that prioritizes security, performance, and efficiency.

Customer Rating 

• G2 - 4.0/5

5. Oracle

Oracle Database is one of the world's most popular convergent, multi-model database management systems. Under Oracle Database products and services, you can buy cost-effective, high-performance versions of in-memory, NoSQL, and MySQL databases. Enabling fusion onto database clouds can help you lower IT costs and deliver quality services. With Oracle Autonomous Database, users may streamline relational database environments and lessen management tasks. 

• Utilize a single database for all workloads and data types: Oracle's converged database relieves application developers of laborious transformations and redundant data.
• Make application development simpler: With the most comprehensive database platform that includes both application and data services, you can simplify the development and deployment of enterprise applications. You can create cloud-native, multitenant databases, Kubernetes, CI/CD, and low-code SaaS applications.
• Obtain horizontal scalability: It facilitates the creation of multi-model databases that are distributed globally, linearly scalable, and compliant with data sovereignty regulations while accommodating applications that need high availability and low latency. 

Customer Rating 

• G2 - 4.3/5

6. SAP HANA Cloud

SAP HANA Cloud is the latest database-as-a-service (DBaaS) driving the next wave of intelligent data applications. It incorporates cutting-edge machine learning and prediction tools based on contemporary data science, giving it a competitive advantage. Its strong in-memory performance ensures that data processing is done effectively. SAP HANA Cloud streamlines data management and links to other data sources by safely storing enormous volumes of data with its integrated multitier storage and managing different types on a single copy in its native multi-model database. 

• Scalability, compliance, and security: It manages technical tasks to provide you peace of mind, such as data protection, system maintenance, backup and disaster recovery, and best-in-class uptimes.  

Customer Rating 

• G2 - 4.3/5

7. Vertica

Based on a massively scalable architecture, Vertica is the unified analytics platform. It offers various analytical operations, including pattern matching, geographic analysis, event and time series analysis, and integrated machine learning. With Vertica, data analytics teams can effortlessly integrate these potent features into extensive and rigorous analytical tasks for anticipatory business understanding.

Vertica connects data in cloud object storage and HDFS without requiring any data migration, and it offers a uniform analytics platform across major public clouds and on-premises data centers. Vertica is one of the Snowflake alternatives that offers the capability to separate computing and storage to help you spin up storage and computing resources as needed and then spin them down afterward to save money.

• Analytical Database: Designed for the most demanding data analytics use cases, Vertica is an ACID-compliant, columnar, relational database that complies with ANSI-Standard SQL.
• Install anywhere: Vertica is a software-only, infrastructure-independent solution that can be used on-premises or in any cloud, containerized, private, or hybrid cloud environment. 
• Vertica Accelerator: With Vertica as a SaaS service, you can access a single, high-performance platform for advanced analytics and machine learning in your AWS cloud account.

Customer Rating 

• G2 - 4.3/5

8. Azure Synapse Analytics

A corporate analytics solution called Azure Synapse Analytics speeds up the time to provide insight for big data platforms and data warehouses. It combines the most advanced features of Apache Spark big data technologies, Azure Data Explorer for log and time series analytics, and SQL technologies used in enterprise data warehousing.

• Enjoy infinite scale and do data queries at your convenience: You can acquire knowledge from all your data, including big data analytics platforms, operational databases, data lakes, and warehouses. You can choose a language to query both relational and non-relational data. 
• Get vital insights from all of your data: Integrating Power BI and Azure Machine Learning can increase the number of insights you can uncover from all of your data. It also gives practitioners easy access to all of their intelligent apps to apply machine learning models without moving any data, helping significantly cut the time it takes to develop BI and machine learning applications.
• Create analytics solutions with a cohesive approach: You can use Synapse Studio's workspace for tasks including big data, AI, enterprise data warehousing, data preparation, data management, and data exploration. Data engineers can manage data pipelines with a visual interface that doesn't require code. Database administrators can automate Query optimization. Also, Data scientists can quickly construct Proofs of concept.

Customer Rating 

• G2 - 4.4/5

9. Databricks

To assist you in bringing AI to the world, Databricks applies AI to your data. Comcast, Grammarly, and many Fortune 500 global organizations depend on Databricks' Data Intelligence Platform to democratize and unify data, analytics, and artificial intelligence. The original developers of Apache SparkTM, Delta Lake, and MLflow created Databricks in 2013, drawing inspiration from academia and the open-source community. Databricks powers a Data Intelligence Engine that recognizes the distinct value of a company's data by fusing generative AI with the unification advantages of a lakehouse.

• Cut down expenses: It assists in simplifying complexity and increasing efficiency by harmonizing your governance, AI, and data strategies.
• Democratize Insights: Provide your employees with the resources they require to leverage natural languages to extract insights from your data.

Customer Rating 

• G2 - 4.5/5

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Bonus complementary technology: Lucidity

Data Warehousing can get expensive. Technologies that reduce the cost of Data Warehousing and other cloud storage elements can be a great complement to your Data Warehousing solution. One such complementary technology is Lucidity. Lucidity is the industry’s first multi-cloud storage management company, helping enterprises around the globe save up to 70% on their cloud bill and reclaim 100s of hours of DevOps effort with zero changes to their code. Lucidity brings automation, cost savings, and clarity to cloud storage across AWS, Azure, and Google Cloud. Some of the largest enterprises in the world leverage Lucidity to autonomously rightsize cloud block storage on an ongoing basis, get unified visibility into key storage metrics, see data-driven recommendations for disk tiering, and make one-click, downtime-free changes. Lucidity enables IT, Cloud, and DevOps teams to optimize storage management and deliver world-class performance without inefficient manual work and cost overruns. Lucidity complements data warehousing in environments where the data warehouse or analytic platform runs on customer-managed compute instances with directly attached block storage like AWS EC2/EBS or Azure VMs with managed disks.

The ground-breaking block storage AutoScaler from Lucidity, the first of its kind in the cloud-native space, runs your block storage infrastructure on autopilot for AWS, Azure, and GCP, including EBS, managed disks, and persistent disks. Because your storage dynamically expands and shrinks as needed, the risk of overpaying is eliminated. With the help of this automation, you can maintain control over your storage costs without dealing with the hassles that come with DevOps administration. 

Lucidity AutoScaler

Now, Lucidity doesn’t stop at data monitoring only; it offers holistic cloud cost optimization through cloud cost automation. Under this, it optimizes storage resources by automating the provision to eradicate the possibility of underprovisioning or overprovisioning. Once the idle/underutilized resources or over-provisioning issues are identified, Lucidity’s auto-scaler offers the given advantages:

• No Downtime: This auto-scaler smooths out data or resource management with zero downtime as it automatically expands or shrinks resources. Our NoOps approach also reduces delays and hurdles in the storage management process. 
• Smooth Shrinkage and Expansion: As mentioned above, it supports live shrinkage and expansion, unlike many other tools, and ensures storage availability regardless of fluctuations. 
• Storage Cost Efficiency: Lucidity helps to overcome overprosioning and reduces wastage of space. This results in a remarkable savings of approx. 70% on storage costs.  

To check or to take the estimate what you are going to save, you can use our ROI calculator for a transparent saving visibility. You can estimate your potential savings with Lucidity’s ROI calculator. All you need to do is put the essential details, like disk spend and growth rate, and you are ready to have your saving visuals. 

Lucidity makes data storage management and automation simple, cost-effective, and smooth. To check it, book the demo.  

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