The Best 5 Snowflake Alternatives for Data Management in 2024

Cloud data platforms like Snowflake have redefined how organizations store, process, and analyze vast amounts of data. With its ability to scale on demand and support for seamless data sharing across different environments, Snowflake has become a popular choice. However, it’s not the only option. Many organizations are exploring Snowflake alternatives to better meet their unique needs for performance, pricing, scalability, and integrations.

Here, we’ll explore the best Snowflake alternatives in 2024, comparing their features, pricing, scalability, and ideal use cases.

An Overview of Snowflake and Its Key Features

Before diving into alternatives, it’s important to understand what makes Snowflake so appealing. Snowflake is a fully managed, cloud-native data platform known for:

• Separation of storage and compute: This architecture allows for independent scaling of resources, making it flexible and cost-efficient.
• Multi-cloud support: Snowflake runs on AWS, Azure, and Google Cloud, giving users freedom of choice.
• Data sharing: Its secure data sharing features enable seamless collaboration across organizations without duplicating data.
• Broad analytics support: Snowflake’s Snowpark supports machine learning and data engineering, while Snowsight enhances data visualization.

Despite its strengths, Snowflake has several key drawbacks that organizations should consider. One major issue is its high costs at scale. Snowflake’s usage-based pricing can become expensive for businesses with large or continuous workloads, especially as query volumes increase. Costs can also accumulate from idle resources unless manually paused. Additionally, limited native support for unstructured data is a challenge, as Snowflake primarily focuses on structured and semi-structured data. This makes it less ideal for companies needing robust support for unstructured data types like images or videos. While Snowflake scales well for large operations, performance issues can arise under high concurrency or complex queries, leading to longer response times and inefficiencies for smaller, ad-hoc tasks. It is for these reasons that many organizations prefer Snowflake alternatives that offer different advantages, such as enhanced GPU-based processing, hybrid solutions, or cost-efficiency at scale.

Other drawbacks include vendor lock-in: because Snowflake is entirely cloud-based, switching platforms or moving to a hybrid setup can be difficult. While it supports multiple clouds like AWS, Azure, and Google Cloud, it lacks on-premises options, limiting flexibility for some organizations. Complex pricing structures can make managing and predicting costs difficult, especially without built-in cost-control mechanisms. 

How to Boost Your Snowflake Performance Without Switching Platforms

For businesses already invested in Snowflake, improving performance without switching platforms is a valid option. Here are a few ways to optimize Snowflake:

1. Data Compression: Ensure that data is stored in compressed formats (like Apache Parquet) to speed up query times and reduce storage costs.
2. Cluster Scaling: Use Snowflake’s auto-scaling features to dynamically adjust resources based on the workload, preventing over-provisioning.
3. Integrating GPU Acceleration: Solutions like SQream Blue can work in tandem with Snowflake, offloading large queries to GPUs for faster processing without leaving the Snowflake ecosystem. By leveraging advanced GPU parallelism, SQream Blue processed 30 TB of data in just 41 minutes, significantly outperforming Snowflake in speed (processing data at twice the speed of Snowflake). This performance enables enterprises to handle massive datasets much faster, delivering results in a fraction of the time​.

SQream Blue also offers exceptional cost efficiency, allowing businesses to save up to 50% on data processing costs compared to Snowflake. The native Snowflake connector makes integration seamless, allowing users to offload complex workloads to SQream without transferring data between systems. And by using SQream’s GPU acceleration, organizations can enhance their Snowflake environment without changing their data stack: accelerating complex queries, speeding up insights, and reducing overall operational costs​​.

Now, let’s review the top alternatives to Snowflake.

Top Snowflake Alternatives for 2024

1. SQream DB

• Overview: SQream DB is a GPU-accelerated database designed to process massive datasets faster and more cost-effectively than traditional CPU-based systems. It offers deep integration with BI tools and supports both on-premises and cloud deployments.
• Key Features:
  • Leverages GPU parallelism to handle terabyte-to-petabyte-scale datasets.
  • Significantly reduces processing times for large and complex queries.
  • Integrates seamlessly with existing data ecosystems like Hadoop, Spark, and Snowflake.
• Scalability: Ideal for scaling workloads that involve large volumes of data, without requiring hundreds of CPU servers.
• Pricing: SQream DB offers a lower total cost of ownership with a typical reduction in cost of up to 50% compared to Snowflake. .

2. Databricks

• Overview: Originally focused on big data and AI, Databricks has evolved into a unified lakehouse platform.
• Key Features:
  • Built on Apache Spark, Databricks can handle complex data science tasks.
  • Strong support for machine learning workflows through its MLflow integration.
  • Capable of handling both structured and unstructured data.
• Scalability: The lakehouse model allows Databricks to scale flexibly across data lakes and warehouses.
• Pricing: Databricks uses a pay-as-you-go model, and while compute costs are competitive, human resource costs for managing the platform can be higher.

3. Amazon Redshift

• Overview: Amazon Redshift is a fully managed, cloud-based data warehouse service that integrates tightly with AWS.
• Key Features:
  • Supports massive parallel processing (MPP) for high-performance analytics.
  • Auto-scaling and concurrency scaling for unpredictable workloads.
  • Strong integration with AWS services for seamless cloud workflows.
• Scalability: Scales across large data environments but may struggle with concurrency at very high volumes.
• Pricing: Amazon Redshift offers on-demand pricing with a cost model that scales based on query volumes and storage.

4. Google BigQuery

• Overview: Google BigQuery is a serverless, highly scalable, multi-cloud data warehouse designed for speed and simplicity in running analytics on large datasets.
• Key Features:
  • Offers integrated machine learning capabilities with BigQuery ML.
• Scalability: Scalable, ideal for businesses with large and dynamic data volumes.
• Pricing: BigQuery’s pricing is based on the amount of data processed by queries, making it economical for some but potentially expensive for heavy users.

5. Microsoft Azure Synapse Analytics

• Overview: Synapse is a cloud-based data warehouse and analytics service that combines big data and data warehousing capabilities.
• Key Features:
  • Integrates tightly with other Microsoft services like Power BI and Azure Machine Learning.
  • Supports both on-demand and provisioned resource models.
  • Scalability: Scales across large data sets with support for hybrid cloud setups.
• Pricing: Synapse pricing can vary significantly based on provisioning.

Pricing Comparison: Snowflake vs. Alternatives

Platform	Approximate Cost (TB-scale Workloads)	Scalability	Integration Capabilities
Snowflake	$53.25 (30TB workload)*	High	Multi-cloud, BI tools
SQream DB	$23.94 – $26.94 (30TB workload)*	Very High	BI tools, Hadoop, Spark, common open-source workflow management and orchestration tools (Apache Airflow, Dgaster, Prefect), industry-standard ODBC, JDBC, and Python connectors.
Databricks	$76.49 (30TB workload)*	High	Apache Spark, MLflow
Redshift	Usage-based, flexible	High	AWS ecosystem
BigQuery	Usage-based, flexible	Very High	GCP and multi-cloud
Synapse	Varies by usage and provisioning	High	Microsoft ecosystem

*Per benchmarks from the nonprofit Transaction Processing Performance Council (TPC) which developed TPC Express Big Bench (or TPCx-BB) as a benchmark for objectively comparing Big Data Analytics System (BDAS) solutions. 

Key Considerations When Choosing a Snowflake Alternative

When evaluating alternatives, consider the following factors:

• Performance: Are your workloads compute-intensive, and do they require faster query processing? GPU-accelerated platforms like SQream DB offer significant speed advantages.
• Pricing: Pay attention to both operational and long-term costs. Snowflake’s per-credit pricing can add up quickly, while SQream offers better cost efficiency at scale.
• Integration: Make sure the platform integrates well with your existing tools, including ETL processes and BI platforms.
• Scalability: Look for platforms that can grow with your data needs without introducing bottlenecks or excessive costs.

Pros and Cons of Each Snowflake Alternative

• SQream DB:
  • Pros: Superior performance for large datasets, GPU acceleration.
  • Cons: While a single GPU server used by SQream Blue is more expensive than a CPU server, the GPU has far better cost-performance at scale. 
• Databricks:
  • Pros: Strong for AI and machine learning workflows, supports both structured and unstructured data.
  • Cons: Higher complexity and human resource requirements.
• Redshift:
  • Pros: Tight AWS integration, high concurrency scaling.
  • Cons: Can struggle with high-volume concurrent queries, and be more complex for use cases outside the AWS environment.
• BigQuery:
  • Pros: Serverless architecture.
  • Cons: Costs can be unpredictable for heavy workloads.
• Synapse:
  • Pros: Strong Microsoft ecosystem integration, hybrid cloud flexibility.
  • Cons: Can be complex to configure for high-performance workloads.

FAQ

Q: Which Snowflake alternatives provide better scalability? 

A: SQream DB, Google BigQuery, and Amazon Redshift all provide high scalability, with SQream DB excelling in environments with massive datasets due to its GPU acceleration.

Q: How does Snowflake’s performance compare to other cloud data platforms? 

A: Snowflake performs well in general analytics, but for high-performance and cost-effective analytics on large datasets, platforms like SQream DB offer significantly faster processing and better ROI.

Q: Are there open-source alternatives to Snowflake? 

A: Yes, some organizations may consider open-source solutions like Apache Druid or Presto, though these often require more management and maintenance.

Meet SQream – Industry-Leading GPU Accelerated Data Processing

If you’re seeking high-performance, cost-effective data analytics at scale, SQream provides an excellent alternative or complement to Snowflake. With GPU acceleration, SQream delivers twice the speed at half the cost, enabling businesses to handle terabyte-to-petabyte scale data effortlessly.

SQream Blue is a cutting-edge, cloud-based SQL data lakehouse that revolutionizes how organizations handle massive datasets. Leveraging a unique multi-level GPU parallelizing technology, SQream Blue accelerates data processing and complex queries with unparalleled speed and efficiency. Dynamically balancing workloads between GPUs and CPUs, the solution ensures that resources are allocated optimally, providing rapid results even for the most demanding queries.

What sets SQream apart is its ability to streamline data processing without needing to move or ingest data. Instead, SQream Blue accesses data directly from open-standard formats (e.g., Apache Parquet) stored in low-cost cloud environments. This approach eliminates the overhead of duplicating data, preserving a single source of truth while maintaining full data privacy and ownership. With Apache Parquet’s column-oriented structure, SQream also reduces unnecessary reads, further optimizing performance and cutting costs.

SQream Blue’s native Snowflake connector enables seamless integration, offloading large and complex workloads to SQream’s GPU-accelerated engine. This allows businesses to continue leveraging Snowflake’s robust data warehousing capabilities while accelerating analytics and reducing processing costs. With SQream, Snowflake users can unlock deeper insights faster, and handle even larger datasets without needing to change their data stack. This synergy provides faster time-to-insights, greater scalability, and massive cost savings – a combination that maximizes ROI while future-proofing your data infrastructure.

To learn more, get in touch with the SQream team to discuss your data management needs. 

Summary: Snowflake Alternatives in 2024

We reviewed the top Snowflake alternatives, including SQream DB, Databricks, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics. 

For organizations already using Snowflake, there are ways to boost performance without switching platforms, such as using SQream Blue. SQream Blue enhances Snowflake environments by offloading large and complex workloads to GPUs, resulting in faster data processing and better cost-efficiency.

Ultimately, when selecting the best Snowflake alternative, organizations must consider factors such as performance, pricing, integration with existing tools, and scalability. By leveraging solutions like SQream, businesses can unlock faster insights, reduce costs, and position themselves for long-term success​​​.