Top 10 Dremio Alternatives for Data Analytics in 2025

Looking for Dremio alternatives for your data analytics needs?

This article reviews top 10 competitors, comparing features, strengths and use cases.

Overview of Dremio Platform

Dremio is a unified lakehouse platform that combines the flexibility of data lakes with the performance and ease-of-use of data warehouses. It provides a self-service data platform that allows users to access, analyze, and visualize data from various sources in real time, without the need for complex data engineering. Dremio's architecture leverages technologies like Apache Arrow and Apache Parquet to deliver high-performance analytics, enabling organizations to accelerate their data-driven decision-making processes.

10 Best Dremio Alternatives & Competitors

Finding the right data analytics platform means understanding the unique strengths of each. Here’s a quick rundown of the top alternatives to Dremio and what they bring to the table:

• Best for Manufacturing Data Integration: Factory Thread

• Best for Real-Time Analytics: Google Cloud BigQuery

• Best for Secure Data Sharing: Snowflake

• Best for Collaborative Data Science: Databricks

• Best for Governed Analytics: AtScale

• Best for Hybrid Data Management: Cloudera

• Best for Logical Data Management: Denodo

• Best for AI-Driven Data Access: Starburst

• Best for Distributed SQL Queries: Presto

• Best for User-Friendly Data Blending: Alteryx

These platforms have unique advantages for different use cases. Whether you’re dealing with massive data, need real-time insights or secure data sharing, there’s a tool for you.

Factory Thread - Best for Manufacturing Data Integration

Factory Thread is designed specifically for the needs of modern manufacturers, offering a real-time data virtualization platform that bridges ERP, MES, CRM, and shop floor systems. With its intuitive low-code interface, it empowers both technical and non-technical users to unify and analyze operational data.

Why Factory Thread is a Top Dremio Alternative in 2025

While Dremio specializes in lakehouse analytics and query acceleration, Factory Thread focuses on domain-specific manufacturing use cases. Its AI-powered integration tools simplify complex workflows and enable proactive monitoring, making it a valuable choice for operational teams.

What Factory Thread is Ideal For

Factory Thread is best suited for:

• Real-time integration of production, quality, and business data

• Visual workflow design for faster analytics deployment

• Manufacturing-specific data connectors and templates

• Streamlining governance across distributed data environments

• Enabling data access for non-technical operations teams

Factory Thread vs Dremio: 2025 Comparison

Summary:

• Factory Thread is the best fit for manufacturing companies that require real-time integration across complex systems with minimal setup and intuitive tools.

• Dremio is better for enterprises already managing data lakes and needing high-performance SQL querying at scale.

Google Cloud BigQuery - Best for Real-Time Analytics

Price: $5 per TB of data processed on-demand, $2,000 per month for flat-rate dedicated resources.

Specs:

• Fully managed and serverless

• Real-time data streaming

• Built-in machine learning

Pros:

• Scalable and fast

• Integrates with other Google Cloud services

• Real-time analytics

Cons:

• High costs for large data processing

• Performance issues with many small queries

Google Cloud BigQuery is great for:

• Real-time analytics on large datasets.

• Real-time data streaming for up-to-the-minute insights.

• Built-in machine learning to develop and deploy models directly within the platform.

While cost-effective for large datasets, BigQuery can get expensive with many small queries or large data processing. Despite the costs, its powerful querying and Google Cloud services integration make it a top pick.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 5/5

• Usability: 4.5/5

Snowflake - Best for Secure Data Sharing

Price: Pricing varies based on usage, pay-as-you-go and subscription models.

Specs:

• Cloud-based data warehousing

• Secure data sharing

• Granular access control

Pros:

• Highly scalable architecture

• Supports multiple data formats

• Strong data visualization tool integration

• Secure data sharing

Cons:

• High costs

• Performance issues with complex queries

• Snowsight user interface challenges

Snowflake is great for secure and seamless data sharing across organizations. Secure Data Sharing features:

• Share data across accounts without copying or transferring, for fast access.

• Granular access control, providers can specify which database objects can be shared.

• Controlled sharing for security.

Some users experience performance issues with complex queries involving multiple table joins. Despite that, Snowflake’s innovative features and workload management make it a versatile choice for secure data sharing.

Rating:

• Price: 3.5/5

• Design: 4/5

• Integration: 4.5/5

• Usability: 4/5

Databricks - Best for Collaborative Data Science

Price: $99/month* Analytics queries, data processing, ETL, machine learning, AI, data engineering

Pros:

• Collaborative data science environment

• Supports multiple languages

• Scalable

Cons:

• Estimated monthly cost high

• Complexity in setting up and managing clusters

Databricks is great for collaborative data science and machine learning projects. It supports:

• SQL

• Python

• R It’s for various data professionals and its primary functions include:

• Analytics queries

• Data processing

• ETL

• Machine learning

• AI

• Data engineering This is comprehensive for big data projects.

However, the estimated monthly cost of $100 to $200 might be a consideration for some organizations. Despite the costs, its powerful analytics and collaborative environment make it perfect for teams working with big data and AI.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 4.5/5

AtScale - Best for Governed Analytics

Price: Free Trial, pricing not specified

Specs:

• Universal semantic layer platform

• BI tool integration

Pros:

• Fast governed analytics

• Consistent answers across platforms

• Better data governance and consistency

Cons:

• Pricing not specified

• Steep learning curve for new users

AtScale is for governed analytics, consistent data usage. It provides a universal semantic layer for consistent answers across platforms, better governance and user experience. Users see significant improvement in data governance and consistency due to AtScale’s semantic layer.

However, pricing not specified may be a concern for potential users. Despite that, AtScale’s integration with BI tools and single source of truth make it a great choice.

Rating:

• Price: 3/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 4/5

Cloudera - Best for Hybrid Data Management

Price: Try for free

Specs:

• Hybrid data platform

• Open data lakehouse

Pros:

• More flexibility to manage data across public and private clouds

• Seamless analytics and storage integration

• Scalable

Cons:

• Complexity in managing hybrid environments

• High cost for large deploymentsCloudera offers a hybrid data management solution to integrate on-premise and cloud data. It provides flexibility to manage data across public and private clouds, with an open data lakehouse that integrates analytics and storage. This hybrid approach allows seamless data management across cloud and on-premises environments.

Managing hybrid environments can be complex and high cost for large deployments may be a consideration. Despite these challenges, Cloudera’s scalable architecture and comprehensive data management make it a top choice.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 4/5

Denodo - Best for Logical Data Management

Price: Free Trial; Denodo Express - Free; Agora - Pricing on Request

Specs:

• Data Virtualization

• Unified Data Access

• Semantic Layer

• AI-Ready Data

Pros:

• Significant reduction in data preparation time

• Faster query performance

• Flexible deployment options

Cons:

• Agora pricing not specified

• Steep learning curve for new users

Denodo is for logical data management, data virtualization. Key features include:

• Robust semantic layer for better data governance and user experience

• Reduction in data preparation time

• Faster query performance

• Flexible deployment options to manage within your own environment or use Agora, Denodo’s fully managed cloud service.

However, Agora pricing not specified may be a concern for potential users. Despite that, Denodo’s ability to deliver unified, real-time and secure data access makes it a great choice.

Rating:

• Price: 3/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 4/5

Starburst - Best for AI-Driven Data Access

Price: Up to $500 in usage credits included

Specs:

• 10x faster query performance

• 50+ Connectors to access all data

• Deployment Options: Fully managed in the cloud or self-managed on-premises using amazon redshift

Pros:

• Faster query speed

• Integration with over 50 data sources

• Versatile deployment options

Cons:

• Complexity in setup and management

• High cost for heavy usage* 10x faster query speed

• 50+ Connectors to access all data

• Deployment Options: Cloud and on-premises

Complexity in setup and management, along with high cost for heavy usage may be a consideration for some. Despite these challenges, Starburst’s powerful features make it a great choice for AI-driven data access.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 5/5

• Usability: 4.5/5

Presto - Best for Distributed SQL Queries

Price: Not specified

Specs:

• High query performance

• Query multiple sources of data in a single SQL query

Pros:

• High query performance with in-memory processing

• Supports complex SQL functions, joins and aggregations

• Scalable architecture

Cons:

• Performance may vary with different data sources

• Requires expertise to optimize and manage

Presto is for distributed SQL queries across multiple data sources. Its architecture is designed for high query performance, in-memory processing for fast execution on large datasets. Key features of Presto include:

• Query multiple data sources in a single SQL query

• In-memory processing for fast execution on large datasets

• Supports complex SQL functions, joins and aggregations

However, performance may vary with different data sources and requires expertise to optimize and manage. Despite these challenges, Presto’s scalable architecture and high SQL compatibility makes it a great solution for distributed SQL queries.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 4/5

Alteryx - Best for User-Friendly Data Blending

Price: $5,000 - $10,000 per year

Specs:

• Predictive Analytics, Spatial, Descriptive Analytics

• Compatible with multiple data sources

• Cloud and On-Premise deployment

Pros:

• User-friendly interface

• Powerful data blending capabilities

• Full analytics tools

• Team collaboration

Cons:

• High annual cost

• Limited scalability for very large datasets

Alteryx is for:

• User-friendly interface for data blending for analytics.

• Powerful data blending capabilities for complex analysis without coding.* Team collaboration and sharing insights across teams

However, the $5,000 to $10,000 annual cost may be a consideration for some. Despite that, Alteryx’s full analytics tools and data blending make it a great choice. Those interested in Alteryx alternatives may want to explore other data prep tools.

Rating:

• Price: 3.5/5

• Design: 4.5/5

• Integration: 4.5/5

• Usability: 5/5

Choosing the Right Data Analytics Platform

Choosing the right data analytics platform is key to unlocking your data. Start by understanding your organization’s needs, goals and challenges. Engage stakeholders across departments to get a complete view of data usage and analytics requirements. This will ensure you select a platform that aligns with your organization’s objectives.

Scalability is critical; the platform must scale with increasing data and user load without performance sacrifice. Platforms like Cloudera and Databricks are scalable for large data workloads. Also consider the platform’s integration capabilities. Solutions like AtScale and Alteryx integrate with multiple BI tools for single source of truth.

Data governance and security should be top of mind. Platforms like Snowflake and AtScale have strong data governance features for consistent security and compliance. Ultimately the right platform depends on your specific needs including data processing requirements, budget and features.

Conclusion

In summary the data analytics landscape has many alternatives to Dremio each with their strengths for specific use cases. From Factory Thread’s manufacturing data integration to Alteryx’s user-friendly data blending there’s a platform out there that can transform your data analytics.

Choosing the right platform involves understanding your organization’s needs, considering factors like scalability, integration, data governance and budget. By aligning these factors with the strengths of each platform you can make an informed decision that will drive your data initiatives forward. Get the right tool and future proof your data analytics and unlock your data.

FAQs

What makes Factory Thread the best for manufacturing data integration?

Factory Thread stands out for data integration manufacturing with real-time data delivery, pre-built connectors for SAP and Oracle and a low-code designer for workflows. This combination makes it efficient and easy to integrate.

Why Google Cloud BigQuery for real-time analytics?

Google Cloud BigQuery is for real-time analytics because of real-time data streaming, integrated machine learning and scalable performance. This allows organizations to analyze data as it arrives and make better decisions.

What’s unique about Snowflake for data sharing?

Snowflake’s Secure Data Sharing allows data sharing across accounts without data duplication, granular access control and the ability to create reader accounts for non-Snowflake users. This makes it secure and efficient collaboration.

How does Databricks support data science?

Databricks supports data science with a multi-language environment for analytics, data processing, machine learning and AI so teams can work together seamlessly. This integrated approach makes data-driven projects more productive and innovative.

What to consider when choosing a data analytics platform?

When choosing a data analytics platform consider your organization’s needs, scalability, integration, data governance, security and budget. Engage stakeholders to ensure the platform aligns with your data processing requirements.