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Cazena for Data Science, Machine Learning and Analytics

Cazena makes it easy to centralize and operationalize data in the cloud, radically simplifying pipelines for data science, analytics and machine learning (ML). By using Cazena as a data science platform, you can take on more data sources and more new use cases, delivering business value faster.

 

The self-service web console ensures easy access to Cazena’s powerful cloud data lake, which includes a variety of data engineering and analytics capabilities. Cazena consoles are customized for your team, and include a variety of best-of-breed capabilities. It only takes a few clicks in Cazena to upload, store, explore and share data. It’s a truly turnkey cloud data science platform.

 

For analytics, teams can use built-in capabilities for SQL, Spark, etc. or tools such as AWS SageMaker, RStudio, DataRobot, H20.ai, Anaconda, Jupyter (Lab and Hub), Dataiku, KNIME, Alteryx and most others.

 

Cazena works seamlessly with best-of-breed tools, supercharging processing power and scale — and streamlining collaboration. That’s why Cazena is the ideal data science platform for global teams.

 

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Supercharge Data Science, Analytics & Machine Learning with Cazena

Centralize Data for Consistency & Easily Access with Any Tool

Simplify every part of your data science platform and pipeline with Cazena. Get end-to-end capabilities out of the box and use your favorite tools such as RStudio, DataRobot, H20.ai, Anaconda, Jupyter (Lab and Hub), Dataiku, KNIME, Alteryx and most others. Easily use R, Python, Java, etc. with Spark, Hadoop and other cloud data engines. It’s easy to add new tools to Cazena as you grow.

Collect & Store ANY Data Types

Collect, stream, organize and store any dataset, from social media to IOT/sensor logs or even real-time streams. Cazena’s Instant AWS Data Lake makes it easy to collect, store and manage data in the cloud in clicks, no special skills required. No more waiting for pre-processing or negotiating for access or infrastructure. Cazena is a scalable data science platform for any dataset, even secure or regulated data. Everything is enterprise-grade, with gold-standard security and compliance certifications.

 Cloud Flexibility, Power and Efficiency

Cazena makes it easy to get the benefits of the cloud: a powerful distributed processing environment, global access and unlimited scale. Using Cazena as a centralized data science platform also helps teams stay consistent and efficient. Be confident with common datasets and tool versions, improve collaboration and work securely from different locations.


Operationalize Data for Analytics & ML Use Cases
  • Collect & Store Data

    Ingest and securely store any data, including streaming, semi-structured or unstructured datasets.
  • Simplify Data Prep

    Use best-of-breed capabilities to structure and prepare data for ML, then save the results for re-use and consistency.
  • Self-Service Analytics/ML

    Use built-in tools for SQL or Spark, or connect best of breed tools like RStudio, DataRobot and others.
  • Deliver Outcomes

    Quickly deploy new use cases and show results, shortening the data to insight lifecycle and accelerating iteration.
  • Rinse & Repeat

    Expand, change and optimize with Cazena as a data science platform for all machine learning and analytics use cases.

“Cazena saves us time and accelerates results. With Cazena, you can support everything like R, Python and distributed open-source systems like Spark, in one easy environment.”


Example: How to Use Jupyter Notebooks with Cazena

Teams like using Cazena as a data science platform because we support many different methods and languages. Cazena gives you more power, flexibility and scalability. This demo is a hands-on walkthrough of using Jupyter with Cazena via our free “Try it Now” experience. You’ll mostly see Jupyter. Behind the scenes is Cazena’s powerful cloud data platform, making the Jupyter experience seamless and secure. Watch now and then Try it yourself!

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Live Demo: Using Cazena + Jupyter Notebooks

Try Cazena Now -- Ready for Analytics on a Sample Dataset