What users are saying about
48 Ratings
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Score 8.7 out of 101
16 Ratings
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Score 8.7 out of 101

Likelihood to Recommend

Anaconda

Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.
Maike Holthuijzen profile photo

Databricks Unified Analytics Platform

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.
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Feature Rating Comparison

Platform Connectivity

Anaconda
7.0
Databricks Unified Analytics Platform
8.3
Connect to Multiple Data Sources
Anaconda
10.0
Databricks Unified Analytics Platform
9.0
Extend Existing Data Sources
Anaconda
8.0
Databricks Unified Analytics Platform
9.0
Automatic Data Format Detection
Anaconda
3.0
Databricks Unified Analytics Platform
7.0

Data Exploration

Anaconda
7.5
Databricks Unified Analytics Platform
6.0
Visualization
Anaconda
8.0
Databricks Unified Analytics Platform
6.0
Interactive Data Analysis
Anaconda
7.0
Databricks Unified Analytics Platform
6.0

Data Preparation

Anaconda
7.5
Databricks Unified Analytics Platform
8.0
Interactive Data Cleaning and Enrichment
Anaconda
8.0
Databricks Unified Analytics Platform
8.0
Data Transformations
Anaconda
9.0
Databricks Unified Analytics Platform
9.0
Data Encryption
Anaconda
6.0
Databricks Unified Analytics Platform
7.0
Built-in Processors
Anaconda
7.0
Databricks Unified Analytics Platform
8.0

Platform Data Modeling

Anaconda
8.1
Databricks Unified Analytics Platform
8.3
Multiple Model Development Languages and Tools
Anaconda
10.0
Databricks Unified Analytics Platform
9.0
Automated Machine Learning
Anaconda
6.5
Databricks Unified Analytics Platform
8.0
Single platform for multiple model development
Anaconda
8.5
Databricks Unified Analytics Platform
9.0
Self-Service Model Delivery
Anaconda
7.5
Databricks Unified Analytics Platform
7.0

Model Deployment

Anaconda
6.5
Databricks Unified Analytics Platform
7.5
Flexible Model Publishing Options
Anaconda
7.0
Databricks Unified Analytics Platform
7.0
Security, Governance, and Cost Controls
Anaconda
6.0
Databricks Unified Analytics Platform
8.0

Pros

Anaconda

  • Anaconda itself already carries the most popular Python packages so for most developers it is sufficient enough to deal with the normal work requirements.
  • The Jupyter Notebook is a very encouraging feature which allows the researcher to apply the data analysis in an intuitive way. It provides step by step understanding the data, processing the data, visualizing the data and trying out the different methodology and algorithm
  • Both the old version of Python and the new version of Python are supported, giving a very good backward compatibility of some old Python codes developed beforehand.
No photo available

Databricks Unified Analytics Platform

  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.
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Cons

Anaconda

  • Requires dedicated administration.
  • Expensive.
  • Removes some control from end-users (analysts).
No photo available

Databricks Unified Analytics Platform

  • The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
  • Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.
Ann Le profile photo

Likelihood to Renew

Anaconda

Anaconda 7.0
Based on 1 answer
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
Matthew Deakyne profile photo

Databricks Unified Analytics Platform

No score
No answers yet
No answers on this topic

Usability

Anaconda

Anaconda 8.0
Based on 1 answer
It's really good at installing and getting started. It's less usable and configurable after that. If you stay in the ecosystem, and don't know how to Python any other way - it works really well.
Matthew Deakyne profile photo

Databricks Unified Analytics Platform

Databricks Unified Analytics Platform 9.0
Based on 1 answer
This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent
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Alternatives Considered

Anaconda

Anaconda is way easier to set-up. On Anaconda we have users working on Machine Learning in minutes, where on PyCharm is takes a lot longer to set-up and often involves getting help from IT. PyCharm is easier to integrate with Code repositories (such as GitHub), so if that's very important to you, you might want to take a look at PyCharm
No photo available

Databricks Unified Analytics Platform

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.
Ann Le profile photo

Return on Investment

Anaconda

  • We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
  • We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.
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Databricks Unified Analytics Platform

  • Machine learning is a very new concept and not many universities offer to teach it. My school and a few others have been utilizing Databricks as one of the tools to teach and learn machine learning. By doing this, my university is creating a strong future workforce for the job market.
Ann Le profile photo

Pricing Details

Anaconda

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Databricks Unified Analytics Platform

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Add comparison