DataRobot vs Databricks

Overview

DataRobot

Stacks27

Followers83

Votes0

Databricks

Stacks525

Followers768

Votes8

DataRobot vs Databricks: What are the differences?

Introduction:

When comparing DataRobot and Databricks, it's important to note the key differences between these two popular platforms for data analytics and machine learning.

Focus on Automated Machine Learning: DataRobot is known for its automated machine learning capabilities, allowing users to easily build and deploy machine learning models without extensive coding or data science expertise. In contrast, Databricks focuses more on providing a unified analytics platform that combines data engineering, data science, and business analytics.
Collaboration and Integration: DataRobot offers strong collaboration features, allowing teams to work together on model development and deployment. Databricks, on the other hand, is geared towards seamless integration with existing data sources and tools, making it easier for organizations to leverage their existing infrastructure.
Deployment Options: DataRobot primarily offers a cloud-based platform for model deployment, while Databricks provides both cloud-based and on-premises deployment options. This difference in deployment options can be crucial for organizations with specific requirements around data security and compliance.
Data Processing Capabilities: Databricks excels in data processing and analytics, with robust support for big data processing and advanced analytics. DataRobot, on the other hand, is specifically designed for machine learning model building and optimization, making it more specialized in this area.
Pricing Model: DataRobot typically operates on a subscription-based pricing model, while Databricks offers a pay-as-you-go pricing structure. The pricing difference can impact organizations' decision-making process based on their budget and usage requirements.
Community and Support: Databricks has a large and active community of users and contributors, making it easy to find resources and support online. DataRobot also provides strong customer support but may not have the same level of community engagement as Databricks.

In Summary, DataRobot and Databricks differ in their focus on automated machine learning, collaboration and integration, deployment options, data processing capabilities, pricing models, and community support.

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CLI (Node.js)

Manual

Detailed Comparison

DataRobot	Databricks
It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.	Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Automated machine learning; Data accuracy; Speed; Ease of use; Ecosystem of algorithms; Data preparation; ETL and visualization tools; Integration with enterprise security technologies; Numerous database certifications; Distributed and self-healing architecture; Hadoop cluster plug and play	Built on Apache Spark and optimized for performance; Reliable and Performant Data Lakes; Interactive Data Science and Collaboration; Data Pipelines and Workflow Automation; End-to-End Data Security and Compliance; Compatible with Common Tools in the Ecosystem; Unparalled Support by the Leading Committers of Apache Spark
Statistics
Stacks 27	Stacks 525
Followers 83	Followers 768
Votes 0	Votes 8
Pros & Cons
No community feedback yet	Pros 1 Best Performances on large datasets 1 Multicloud 1 Data stays in your cloud account 1 Security 1 Usage Based Billing
Integrations
Tableau Domino Looker Trifacta Cloudera Enterprise Snowflake Qlik Sense AWS CloudHSM	MLflow Delta Lake Kafka Apache Spark TensorFlow Hadoop PyTorch Keras

What are some alternatives to DataRobot, Databricks?

Google Analytics

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Mixpanel

Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience.

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Piwik

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Clicky

Clicky Web Analytics gives bloggers and smaller web sites a more personal understanding of their visitors. Clicky has various features that helps stand it apart from the competition specifically Spy and RSS feeds that allow web site owners to get live information about their visitors.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Related Comparisons

DataRobot vs Databricks: What are the differences?

Introduction:

When comparing DataRobot and Databricks, it's important to note the key differences between these two popular platforms for data analytics and machine learning.

Focus on Automated Machine Learning: DataRobot is known for its automated machine learning capabilities, allowing users to easily build and deploy machine learning models without extensive coding or data science expertise. In contrast, Databricks focuses more on providing a unified analytics platform that combines data engineering, data science, and business analytics.
Collaboration and Integration: DataRobot offers strong collaboration features, allowing teams to work together on model development and deployment. Databricks, on the other hand, is geared towards seamless integration with existing data sources and tools, making it easier for organizations to leverage their existing infrastructure.
Deployment Options: DataRobot primarily offers a cloud-based platform for model deployment, while Databricks provides both cloud-based and on-premises deployment options. This difference in deployment options can be crucial for organizations with specific requirements around data security and compliance.
Data Processing Capabilities: Databricks excels in data processing and analytics, with robust support for big data processing and advanced analytics. DataRobot, on the other hand, is specifically designed for machine learning model building and optimization, making it more specialized in this area.
Pricing Model: DataRobot typically operates on a subscription-based pricing model, while Databricks offers a pay-as-you-go pricing structure. The pricing difference can impact organizations' decision-making process based on their budget and usage requirements.
Community and Support: Databricks has a large and active community of users and contributors, making it easy to find resources and support online. DataRobot also provides strong customer support but may not have the same level of community engagement as Databricks.

DataRobot vs Databricks

Overview