Cloudera Enterprise Core provides a single Hadoop storage and management platform that natively combines storage, processing and exploration for the enterprise.
Snowflake’s platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything “just works”. To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python, or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.
Teradata Autonomous Knowledge Platform easily and efficiently handles complex data requirements and simplifies management of the data warehouse environment.
Alteryx drives transformational business outcomes through unified analytics, data science, and process automation.
Gemini Enterprise Agent Platform is a managed machine learning (ML) platform that helps you build, train, and deploy ML models faster and easier. It includes a unified UI for the entire ML workflow, as well as a variety of tools and services to help you with every step of the process. Gemini Enterprise Agent Platform Workbench is a cloud-based IDE that is included with Gemini Enterprise Agent Platform. It makes it easy to develop and debug ML code. It provides a variety of features to help you with your ML workflow, such as code completion, linting, and debugging. Gemini Enterprise Agent Platform and Gemini Enterprise Agent Platform Workbench are a powerful combination that can help you accelerate your ML development. With Gemini Enterprise Agent Platform, you can focus on building and training your models, while Gemini Enterprise Agent Platform Workbench takes care of the rest. This frees you up to be more productive and creative, and it helps you get your models into production faster. If you're looking for a powerful and easy-to-use ML platform, then Gemini Enterprise Agent Platform is a great option. With Gemini Enterprise Agent Platform, you can build, train, and deploy ML models faster and easier than ever before.
Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon, Microsoft and Google Clouds
As a cloud-native AI, analytics and data management platform, SAS Viya enables you to scale cost-effectively, increase productivity and innovate faster, backed by trust and transparency. SAS Viya makes it possible to integrate teams and technology enabling all users to work together successfully to turn critical questions into accurate decisions.
dbt is a transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
SAP HANA Cloud is the cloud-native data foundation of SAP Business Technology Platform, it stores, processes and analyzes data in real time at petabyte scale and converges multiple data types in a single system while managing it more efficiently with integrated multitier storage.
Databricks outperforms Cloudera Data Platform with an average rating of 4.6/5 from 803 reviews compared to Cloudera's 4.1/5 from 136 reviews. Databricks scores higher across key dimensions: it leads by 0.4 points in meeting requirements (9.0 vs 8.6), by 0.6 points in usability (8.9 vs 8.3), by 0.6 points in ease of setup (8.7 vs 8.1), by 0.5 points in ease of administration (8.4 vs 7.9), by 0.8 points in support quality (8.8 vs 8.0), and by 0.5 points in ease of doing business (8.9 vs 8.4). Users praise Databricks for its extensive features (192 mentions), ease of use (155 mentions), integrations (141 mentions), collaboration (114 mentions), scalability (111 mentions), and machine learning integration (106 mentions). In contrast, Cloudera is noted for ease of use (22 mentions), scalability (17 mentions), and security (9 mentions), but with fewer mentions overall and more concerns about cost and complexity. Databricks offers a unified platform combining data engineering, analytics, and AI/ML workflows with collaborative notebooks and strong Spark integration, while Cloudera emphasizes big data management with Apache Spark and Airflow integration but has a steeper learning curve and higher complexity. Cost management is a challenge for both, but Databricks' pricing model is frequently cited as complex and expensive if not carefully managed. Overall, Databricks provides a more comprehensive, user-friendly, and scalable solution with stronger support and collaboration features according to G2 data.
The best alternatives to Databricks include Snowflake (4.5/5 with 755 reviews), Google Cloud BigQuery (4.5/5 with 1227 reviews), Teradata Autonomous Knowledge Platform (4.3/5 with 375 reviews), Cloudera Data Platform (4.1/5 with 136 reviews), and Alteryx (4.6/5 with 816 reviews). These platforms are favored for their scalability, ease of use, and strong integration capabilities.
Databricks users report challenges with cost management, steep learning curve, cluster startup times, and complex debugging processes. Alternatives offer features that address some of these issues, such as easier administration, better cost predictability, and integrated AI/ML capabilities.
Reviewers recommend Snowflake for its ease of administration, better usability, and setup simplicity compared to Databricks. Google Cloud BigQuery is praised for its serverless architecture and fast query performance with easier administration. dbt is noted for better support, usability, and meeting requirements in data transformation workflows. Teradata and SAS Viya are recognized for their scalability and advanced analytics capabilities. Users suggest considering these alternatives for improved cost management, simplified administration, and enhanced AI/ML integration.
Users choose Cloudera Data Platform over Databricks primarily for its robust security features and strong scalability in hybrid and enterprise environments. Cloudera is recognized for its ease of use in big data management, with 22 mentions highlighting usability and 9 mentions emphasizing security, making it attractive for organizations prioritizing data governance and compliance. Its integration with Apache Spark and Apache Airflow supports efficient batch processing and job orchestration, which appeals to teams needing reliable pipeline automation. Additionally, Cloudera's support for hybrid cloud deployments and its comprehensive suite of bundled big data technologies provide flexibility for enterprises managing complex, multi-cloud infrastructures. Some users appreciate Cloudera's customer support and the platform's reliability for handling large-scale data engineering tasks. Despite a smaller review base and lower overall ratings compared to Databricks, Cloudera's strengths in security, scalability, and enterprise-grade features make it a preferred choice for organizations with stringent compliance requirements and hybrid cloud strategies, as reflected in G2 user feedback.