Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.
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PostgreSQL
Score 8.7 out of 10
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PostgreSQL (alternately Postgres) is a free and open source object-relational database system boasting over 30 years of active development, reliability, feature robustness, and performance. It supports SQL and is designed to support various workloads flexibly.
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Pricing
Cloudera Data Science Workbench
PostgreSQL
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Data Science Workbench
PostgreSQL
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Cloudera Data Science Workbench
PostgreSQL
Features
Cloudera Data Science Workbench
PostgreSQL
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Cloudera Data Science Workbench
7.5
2 Ratings
11% below category average
PostgreSQL
-
Ratings
Connect to Multiple Data Sources
7.02 Ratings
00 Ratings
Extend Existing Data Sources
8.02 Ratings
00 Ratings
Automatic Data Format Detection
7.02 Ratings
00 Ratings
MDM Integration
8.02 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
PostgreSQL
-
Ratings
Visualization
7.12 Ratings
00 Ratings
Interactive Data Analysis
8.02 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Cloudera Data Science Workbench
7.8
2 Ratings
5% below category average
PostgreSQL
-
Ratings
Interactive Data Cleaning and Enrichment
7.02 Ratings
00 Ratings
Data Transformations
8.02 Ratings
00 Ratings
Data Encryption
8.02 Ratings
00 Ratings
Built-in Processors
8.02 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
PostgreSQL
-
Ratings
Multiple Model Development Languages and Tools
8.02 Ratings
00 Ratings
Automated Machine Learning
7.01 Ratings
00 Ratings
Single platform for multiple model development
7.12 Ratings
00 Ratings
Self-Service Model Delivery
8.12 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
PostgreSQL is best used for structured data, and best when following relational database design principles. I would not use PostgreSQL for large unstructured data such as video, images, sound files, xml documents, web-pages, especially if these files have their own highly variable, internal structure.
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.
Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
There are several companies that you can contract for technical support, like EnterpriseDB or Percona, both first level in expertise and commitment to the software.
But we do not have contracts with them, we have done all the way from googling to forums, and never have a problem that we cannot resolve or pass around. And for dozens of projects and more than 15 years now.
The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.
Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
Although the competition between the different databases is increasingly aggressive in the sense that they provide many improvements, new functionalities, compatibility with complementary components or environments, in some cases it requires that it be followed within the same family of applications that performs the company that develops it and that is not all bad, but being able to adapt or configure different programs, applications or other environments developed by third parties apart is what gives PostgreSQL a certain advantage and this diversification in the components that can be joined with it, is the reason why it is a great option to choose.
Easy to administer so our DevOps team has only ever used minimal time to setup, tune, and maintain.
Easy to interface with so our Engineering team has only ever used minimal time to query or modify the database. Getting the data is straightforward, what we do with it is the bigger concern.