Cloudera Data Services 1.5.4 on private cloud is now generally available. This release delivers key new features across Cloudera Machine Learning, Cloudera Data Warehouse, Cloudera Data Engineering, and platform management, ensuring that customers can build cutting-edge analytics and AI solutions leveraging modern data architectures on cloud-native, on-premises infrastructure.
This feature-rich release boasts 100+ new features and a multitude of enhancements, so whether you’re a data scientist, data analyst, data engineer, or platform administrator, there is plenty to be excited about. The full details of the release are published in the release notes and documentation.
In this post, I’d like to highlight three new features, and why they can be a game-changer for your business:
- Natural language querying in Cloudera Data Warehouse. You no longer need to wrestle with complex SQL queries and cryptic database jargon. With this innovative feature, data analysts and business users can unlock hidden insights from their data troves by simply asking questions of the data. This also removes the skill barrier for business users to extract valuable data insights, meaning fewer bottlenecks and faster business outcomes.
- Model registry in Cloudera Machine Learning. Every data scientist and AI engineer knows that managing models is a headache - versioning the code is just the start - keeping track of parameters, weights, training data, performance metrics, dependencies, and environments can quickly become a tangled mess. That's why we're thrilled to introduce our new model registry, designed to tame the chaos. Say goodbye to convoluted tracking sheets, and hello to streamlined model management and serving.
- Security, resilience, and governance. While these may not sound like the flashiest of features, they're the foundation that ensures your data platform is rock-solid and your proprietary data is locked down tight. In this release, we've doubled down on security and resilience with automatic container in-cluster backups, password-protected ingress private key support, and a host of certifications.
That’s only a few features from the list of 100+ so please check out the release notes to see all the features in this release. Here’s what you can expect to find in the release notes:
- Platform features add improved data discovery and accessibility, while strengthening security, data protection, and platform reliability. These include:
- Automatic backups turned on by default for ECS deployments with in-cluster backups
- Fresh install prerequisite checks (see more details here) to validate that the necessary storage space, virtual cores, connectivity ports, and other configurations are met.
- Cloudera Data Warehouse adds improved data access and querying, enabling users to interact with data more efficiently for faster insights, with features such as:
- Bind users are no longer required to retrieve users and groups. Cloudera Data Warehouse now uses Kerberos tickets to do this automatically.
- Support for custom pod sizing for Hive LLAP Virtual Warehouses, Hive Metastore (HMS) Service and improvements to the user interface
- Support in Cloudera Hue for LLM-based SQL generation (Tech Preview)
- Cloudera Impala audit logs and audit log storage in the Hadoop Distributed File System (HDFS) enabled
- Support for mTLS-enabled databases for HMS
- Improvements to Quota management
- Automatic spill to HDFS
- Cloudera Machine Learning adds model serving, management, and governance, as well as improved compute utilization, with features such as:
- Cloudera Machine Learning service accounts in private cloud enabled
- Cloudera Model Registry available in private cloud
- Quota Management for Group/Team (Technical Preview) available
- Heterogeneous GPU hardware support enables different types of GPUs in the same cluster which can optimize computational efficiency
- Cloudera Data Engineering adds streamlined access to services and improved user productivity for faster business insights, with features such as:
- Security improvement to enable the UI when port 80 is blocked on the k8s cluster ingress level.
- Spark Connect for interactive development via external Jupyter notebook. This feature is only for Spark 3.4 VC with 7.1.8 runtime on a subset of connectors.
- Initialization of virtual clusters with password-protected private keys.
For the full list of features, see the release notes.
To learn more about how cloud-native data management can help you optimize your data workloads and stay competitive in today's fast-paced data landscape, check out our introductory blog and short video for Cloudera Data Services on private cloud.
Cloudera’s Professional Services team is available to ensure your migration to Cloudera is successful. In addition, Cloudera’s Support Team is available for your mission-critical support needs. We encourage you to open a support case via our Support Portal to alert us of your planned upgrade.
Additional helpful resources about Cloudera on private cloud:
