Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.
| Product | Market Share (%) |
|---|---|
| Anaconda Business | 2.1% |
| Databricks | 15.3% |
| Dataiku | 12.9% |
| Other | 69.69999999999999% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Data Science Platforms | Aug 27, 2025 | Download |
| Product | Reviews, tips, and advice from real users | Aug 27, 2025 | Download |
| Comparison | Anaconda Business vs Databricks | Aug 27, 2025 | Download |
| Comparison | Anaconda Business vs KNIME Business Hub | Aug 27, 2025 | Download |
| Comparison | Anaconda Business vs Amazon SageMaker | Aug 27, 2025 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Databricks | 4.1 | 15.3% | 96% | 91 interviewsAdd to research |
| KNIME Business Hub | 4.1 | 11.9% | 94% | 60 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 9 |
| Company Size | Count |
|---|---|
| Small Business | 61 |
| Midsize Enterprise | 43 |
| Large Enterprise | 219 |
Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.
What are the key features of Anaconda Business?Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.
LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
| Author info | Rating | Review Summary |
|---|---|---|
| VP Product at Medint | 4.0 | No summary available |
| Cluster Manager - Risk at a financial services firm with 10,001+ employees | 4.0 | In my experience, Anaconda struggles with heavy workloads, and I wish it could integrate with Databricks for better enterprise-level data handling. While Databricks handles large data well, Anaconda is preferable for development due to lower computing costs. |
| AI/ML Co-Lead at Developer Student Clubs - GGV | 5.0 | I started using Anaconda during my first year to learn Python on a MacBook with an M1 chip. Anaconda simplifies creating environments for multiple projects, but it's behind in automated data cleaning and could benefit from a graphical user interface. |
| Data Scientist at NUCES | 4.5 | I use Anaconda for data science, primarily for developing forecasting models in Python. Its valuable features include a unified platform for installing tools like Jupyter and Python Spider, and it supports multiple languages. However, it consumes significant processing memory, affecting performance. |
| Consultant - Data Analytics and Reporting at a tech vendor with 51-200 employees | 4.0 | I use Anaconda primarily for Jupyter Notebook due to its cloud-based accessibility, which simplifies bug and error handling. However, the user interface could be more attractive and user-friendly, as it currently feels mundane. |
| Data Scientist Chapter Lead, Workflow & Automation at ANZ Banking Group | 3.5 | No summary available |
| Solution Architect/Technical Manager - Business Intelligence at a tech services company with 5,001-10,000 employees | 4.0 | No summary available |
| Global Data Architecture and Data Science Director at FH | 4.5 | No summary available |
