Looking for Dremio alternatives for your data analytics needs?
This article reviews top 10 competitors, comparing features, strengths and use cases.
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.
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 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.
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.
Factory Thread is best suited for:
Real-time integration of production, quality, and business data
Visual workflow design for faster analytics deployment
Streamlining governance across distributed data environments
Enabling data access for non-technical operations teams
|
Feature / Aspect |
Factory Thread |
Dremio |
|---|---|---|
|
Primary Use Case |
Manufacturing data integration and operational BI |
Data lakehouse query acceleration |
|
Industry Focus |
Manufacturing |
Industry-agnostic |
|
Query Engine |
AI-assisted visual workflows |
SQL query engine with Apache Arrow and Iceberg |
|
Ease of Use |
High – low-code tools, intuitive UI |
Moderate – requires SQL and technical setup |
|
Data Connectors |
Prebuilt ERP, MES, CRM, IoT integrations |
JDBC, NoSQL, S3, Hive, HDFS |
|
Security |
Built-in role-based access controls |
External integrations for governance |
|
Deployment Options |
Cloud, on-prem, edge runtime |
Cloud-native and on-prem |
|
Governance |
Centralized manufacturing-focused governance |
Open-source governance with lineage features |
|
Best Fit |
Manufacturers needing real-time, low-code analytics |
Teams focused on accelerating lakehouse queries |
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.
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
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
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
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
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
Price: Free Trial; Denodo Express - Free; Agora - Pricing on Request
Specs:
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
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
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
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 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.
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.
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.
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.
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.
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.
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.