Pricing score

Cloudera Data Engineering Pricing Profile

Cloudera Data Science provides better access to Apache Hadoop data with familiar and performant tools that address all aspects of modern predictive analytics.

HQ Location

Boulder, Colorado, USA.

Category:

Key Takeaways

Integrated Data Science Workflows

CDS streamlines the entire workflow from data ingestion and preparation to model training, testing, and deployment. It is built to handle end-to-end machine learning workflows on enterprise data at scale.

Collaborative Data Science

CDS offers tools for teams to collaborate effectively on projects. With features such as shared environments, version control (e.g., Git), and integrated notebooks, data scientists can work together on datasets, algorithms, and models. It also includes a rich interface for managing notebooks and pipelines.

Machine Learning Model Development and Training

CDS simplifies the model development process by providing easy access to popular machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost. Data scientists can easily train models on massive datasets, leveraging Cloudera’s distributed processing capabilities.

Product Overview

Cloudera Data Science is a comprehensive platform for data science, machine learning, and advanced analytics. Built to work seamlessly with Cloudera's enterprise data cloud infrastructure, it enables teams to explore, experiment, and deploy machine learning models at scale.

With its integration of tools and features tailored for collaborative data science work, Cloudera Data Science provides an efficient environment for both data scientists and data engineers.

INSIGHTS

Our insights about Cloudera Data Engineering pricing

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Available Pricing Models

How much does Cloudera Data Engineering cost?

Cloudera Data Engineering does not publicly disclose its pricing on its website, as costs can vary based on factors like company size, the products selected, and specific requirements. For the most accurate and personalized pricing information, it’s best to contact Cloudera Data Engineering directly through their website or speak with a sales representative.

What users say about Cloudera Data Engineering pricing

Ridhim U.

One of the major things that I dislike about Cloudera Data Engineering is its documentation and community support. I feel the documentation is very limited.

Nishaant C.

Cloudera offers robust big data solutions, empowering businesses with efficient data management and analytics. Their user-friendly platform and excellent support enable organizations to harness the power of data for smarter decisions and growth.

Nikhil T.

Can be expensive, Not as flexible as some other solutions, Not as well-known as some other solutions.

Shristy S.

Airflow jobs on running the privileges applied on these jobs are of the ones who created it not submitted the job.

Suprakash M.

It can be costly for organization having large amount of data. The pricing structure for enterprises can be high. Also the platform lacks the unavailability of data science and visualization interface.

Ridhim U.

One of the major things that I dislike about Cloudera Data Engineering is its documentation and community support. I feel the documentation is very limited.

Shristy S.

Airflow jobs on running the privileges applied on these jobs are of the ones who created it not submitted the job.

Nishaant C.

Suprakash M.

Nikhil T.

Can be expensive, Not as flexible as some other solutions, Not as well-known as some other solutions.

Cloudera Data Engineering Pricing Rating

Customer Analytics: 4.8/5

CDS enables organizations to analyze large datasets, build predictive models, and generate insights on customer behavior, preferences, and trends. This can be used for recommendation systems, customer segmentation, churn prediction, and more.

Operational Efficiency: 4.7/5

By utilizing machine learning models to automate and optimize operational processes, organizations can increase efficiency, reduce costs, and improve decision-making. CDS helps build models that can be deployed to production for real-time operational use cases.

Scalability: 4.9/5

CDS is designed for large-scale operations, enabling teams to work with datasets of any size. Whether you are running on-premise infrastructure or leveraging cloud services, CDS scales seamlessly to accommodate growing data science workloads.

FAQ on Cloudera Data Engineering Pricing

How is CDS different from other data science platforms?

CDS integrates deeply with Cloudera's enterprise data platform, including Apache Spark, Hadoop, and data lakes, allowing for seamless data management, advanced analytics, and large-scale model training. It also includes built-in governance, security, and collaboration features for enterprise teams.

Can I use CDS for both cloud and on-premise deployments?

Yes, Cloudera Data Science supports both cloud-based and on-premise deployments, providing flexibility for hybrid cloud environments and seamless integration with Cloudera Data Platform (CDP).

What machine learning frameworks are supported?

CDS supports popular machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, and others, allowing data scientists to leverage the tools they are most comfortable with.

Can I collaborate with my team using CDS?

Yes, CDS offers collaborative tools like shared environments, Jupyter notebooks, and version control (Git), which help data scientists work together on projects, share insights, and track changes.