New Year - New Skills

Enrich, refine, structure, and prepare data for the lifecycle

Cloudera Data Engineering helps enrich, transform, and cleanse data, making it easy to create, execute, and manage end-to-end data pipelines. It executes a wide range of data processing workloads in an extremely high-performance manner including batch and real-time stream processing using Apache Spark and Spark Streaming, supported by multiple storage options including Apache HBase, Apache Kudu, and cloud object storage.

Provide self-service access to reporting for the lifecycle

Deliver business insights on massive amounts of verified data to thousands of users at extreme speed and scale without compromising compliance and blowing budgets. Seamlessly and securely moving on-premises workloads to any cloud, Cloudera Data Warehouse outperforms shadow IT by keeping up with evolving business requirements and meeting SLAs with self-service access to reports, dashboards, and advanced analytics.

Serve all types of data from all sources for the lifecycle

Cloudera Operational Database serves structured data alongside unstructured data within a unified end-to-end open-source platform, ensuring decision making is driven by stream processing and real-time analytics on continuously changing data. Users can serve real-time data at scale, with high concurrency and low latency, and data science at scale in order to easily build, score, and deploy machine learning models into production.

Operationalise predicting for the lifecycle

Accelerate enterprise data science from research to production at scale with self-service, collaborative workflows for building and operationalising machine learning models. Using Python, R, and Scala directly in the web browser, Cloudera Machine Learning delivers a powerful self-service experience for data science teams to develop and prototype new machine learning capabilities and easily deploy them to production.