
BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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JAMS is an automation orchestration and job scheduling solution that runs, monitors, and manages critical IT processes from a single console. JAMS automates jobs across Windows, Linux, UNIX, IBM i, z/OS, and OpenVMS, with native integrations for the databases, BI tools, and ERP systems already running your business. Jobs run on any schedule or trigger off other events, with dependency management keeping workflows in order and an audit trail logging every execution.
JAMS includes two AI capabilities at no additional cost. JAX is an AI agent built into the JAMS Web Client: ask it a question in plain language, and it finds a job, troubleshoots a failure, or looks up how to do something, grounded in JAMS documentation. It acts only when asked, and every change waits for your approval. JAMS MCP brings JAMS into the AI coding tools teams already use, including Cursor, Claude Code, GitHub Copilot, and Claude Desktop.
For teams managing thousands of jobs across SQL Server, ADF, Airflow, SAP, JDE, and Banner, this cuts tribal knowledge and middle-of-the-night troubleshooting. Knowledge that once lived in one person's head becomes something any team member can ask about directly.
The AI lives in the product, not in the support queue. Support is staffed by humans JAMS will never outsource, based in the United States, the United Kingdom, and Australia. New tickets go to long-tenured engineers, and every JAMS customer has the CEO's cell phone number.
JAMS' mission is to reduce the operational burden of critical automation, so teams spend more time on the work automation was meant to free them for.
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Rivery
Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud.
Key Features:
Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines.
Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance.
Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects.
Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.
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Apache Airflow
Airflow is a community-driven platform designed for the programmatic creation, scheduling, and monitoring of workflows. With its modular architecture, Airflow employs a message queue to manage an unlimited number of workers, making it highly scalable. The system is capable of handling complex operations through its ability to define pipelines using Python, facilitating dynamic pipeline generation. This flexibility enables developers to write code that can create pipelines on the fly. Users can easily create custom operators and expand existing libraries, tailoring the abstraction level to meet their specific needs. The pipelines in Airflow are both concise and clear, with built-in parametrization supported by the robust Jinja templating engine. Eliminate the need for complex command-line operations or obscure XML configurations! Instead, leverage standard Python functionalities to construct workflows, incorporating date-time formats for scheduling and utilizing loops for the dynamic generation of tasks. This approach ensures that you retain complete freedom and adaptability when designing your workflows, allowing you to efficiently respond to changing requirements. Additionally, Airflow's user-friendly interface empowers teams to collaboratively refine and optimize their workflow processes.
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