Google Cloud Run
Fully managed compute platform to deploy and scale containerized applications securely and quickly. You can write code in your favorite languages, including Go, Python, Java Ruby, Node.js and other languages. For a simple developer experience, we abstract away all infrastructure management. It is built upon the open standard Knative which allows for portability of your applications. You can write code the way you want by deploying any container that listens to events or requests. You can create applications in your preferred language with your favorite dependencies, tools, and deploy them within seconds. Cloud Run abstracts away all infrastructure management by automatically scaling up and down from zero almost instantaneously--depending on traffic. Cloud Run only charges for the resources you use. Cloud Run makes app development and deployment easier and more efficient. Cloud Run is fully integrated with Cloud Code and Cloud Build, Cloud Monitoring and Cloud Logging to provide a better developer experience.
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JAMS
JAMS serves as a comprehensive solution for workload automation and job scheduling, overseeing and managing workflows critical to business operations. This enterprise-grade software specializes in automating IT tasks, accommodating everything from basic batch jobs to intricate cross-platform workflows and scripts. JAMS seamlessly integrates with various enterprise technologies, enabling efficient, unattended job execution by allocating resources to execute jobs in a specific order, set time, or in response to specific triggers. With its centralized console, JAMS allows users to define, manage, and monitor essential batch processes effectively. Whether you’re executing straightforward command lines or orchestrating complex multi-step tasks that utilize ERPs, databases, and business intelligence tools, JAMS is designed to streamline your organization’s scheduling needs. Additionally, the software simplifies the transition of tasks from platforms like Windows Task Scheduler, SQL Agent, or Cron through built-in conversion tools, ensuring that jobs continue to run smoothly without requiring substantial effort during migration. Overall, JAMS empowers businesses to optimize their job scheduling processes efficiently and effectively.
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Bright Cluster Manager
Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects.
Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython.
Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).
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Rocket Data Virtualization
Hybrid data stacks create duplication and delay: mainframe records, on prem apps, and cloud platforms often end up with mismatched copies, brittle ETL, and long lead times for “just one more feed.” Moving large datasets for every use case is slow, costly, and expands the security surface.
Rocket® Data Virtualization™ is a data virtualization and federated query solution that enables a governed, virtual data model across mainframe, distributed, and cloud sources—so BI tools, analysts, and applications can query sensitive data in place.
Key capabilities:
• Federated SQL queries/joins across heterogeneous sources with pushdown
• Standard connectivity (e.g., JDBC/ODBC/REST) for BI, analytics, and apps
• Virtual views/semantic layer to simplify access and reuse logic
• Centralized security controls, auditing, and masking (where supported)
• Optional caching/materialization to balance performance and freshness
Result: faster time to data with less ETL and lower migration risk.
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