Welcome to Your Yes Energy + Snowflake Starter Hub
This page is your resource center for everything you need to explore and start working with Yes Energy data in the Snowflake Data Cloud. Whether you're here to explore new or enriched data sets, dive deeper into DataSignals features, or activate a free 30-day trial of Snowflake, you’re in the right place.
You’ll also find links to information on powerful Snowflake features and critical best practices, from cost optimization and AI capabilities, to help you get the most out of your data workflows. Scroll down for product access, sample data, documentation, or to connect with our expert team.
DataSignals Resources
- Yes Energy Data Catalog: now available to all DataSignals API and Cloud subscribers
- DataSignals API home page
- Yes Energy GitHub Request Form
- IESO-Ontario nodal data: now available in DataSignals
- Calculated data types: now available in DataSignals Cloud
- Vintaged, non-forecast data types: now available in DataSignals Cloud
- ERCOT 60-Day SCED/DAM Disclosure Report Data: now available in DataSignals Cloud
- For more information watch our on-demand webinar.
Additional Reading on Useful Snowflake Features
- Snowflake Notebooks: familiar, easy-to-use notebook interface seamlessly blends Python, SQL, and Markdown
- Workspaces: unified editor for creating, organizing, and managing code
- Git Integration: source control through version management via Git branches
- Adaptive Compute: intelligent query routing to the right size warehouse to optimize price-performance
- Snowflake Cost Management:
- Cortex AI SQL: simple to call AI/ML functions within an SQL query
- Cortex Analyst: create applications capable of reliably answering business questions based on structured data
- Snowflake Intelligence: converse with all their enterprise data (both structured and unstructured) and take action
- Data Science Agent: data science trained chatbot that helps with feature engineering and model iteration
- AI-Powered Marketplace: Snowflake Marketplace has evolved to allow sharing semantic views and agentic products
Discover how DataSignals Cloud can take your data strategy further with scalable, AI optimized, and governable market data delivery, designed for teams ready to innovate and grow.
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Frequently Asked Questions
While it's possible to scale DataSignals API by using multiple license keys, that approach can become complex to manage over time. Many customers find that as their data needs grow, moving to DataSignals Cloud simplifies access and supports more advanced use cases.
Yes Energy offers three distinct versions of DataSignals—each built to support different technical environments and use cases. All three integrate well with modern cloud platforms but offer different benefits depending on your team’s needs:
DataSignals Cloud: DataSignals Cloud provides read-only access to Yes Energy’s full production database via Snowflake. It’s ideal for teams that want instant access to curated data without the overhead of building or maintaining replication pipelines. Data can also be exported to other cloud platforms like AWS, Azure, or Google Cloud for staging or further integration. Snowflake offers robust support for tools and connectors such as Python, JDBC, and ODBC, making it easy to plug into most modern data ecosystems.
DataSignals API: This is a REST API product that enables flexible, programmatic access to Yes Energy data. It’s well-suited for teams who want to embed data directly into their own pipelines or applications. While it integrates with a wide range of platforms, it requires building queries in REST format and is subject to usage limits. It’s not designed for SQL-based querying but can still support many workflows through custom development.
DataSignals Lake: Best suited for teams that already use a cloud-based data lake or warehouse, this option provides access to Yes Energy’s historical and near real-time data stored in Amazon S3. You can connect directly to that storage and ingest files into your own system—whether you’re on AWS, Azure, Databricks, GCP, or another platform. It’s a lightweight, flexible option for users who just need raw data access without added tooling or interface layers.
Performance Insights provides visibility into how queries are performing, which is especially helpful when diagnosing slow query times or optimizing pipelines. This feature helps users better understand where resources are being consumed and how to adjust workloads more efficiently.
Adaptive Compute features in Snowflake offer smarter resource allocation that can help reduce unnecessary compute costs—particularly important when you're running complex queries or maintaining active pipelines.
Both these tools can help users balance performance with cost-effectiveness in their DataSignals Cloud environment.
Customer input is the most important driver—we maintain open conversations with users to understand their data challenges, technical needs, and feature requests. Because many of our users are data scientists and data engineers, their feedback is both detailed and forward-looking.
We also rely on the experience of our internal engineering teams, who use Snowflake themselves and provide valuable insights into usability, scalability, and workflow optimization. Their input often complements and refines what we hear from customers.
Finally, our product team actively tracks new technologies, especially platform advancements like those from Snowflake, to ensure we’re building for the future and helping our users stay ahead of industry trends.
Here’s what that entails:
Pre-Cleaned and Standardized Data: We handle the complexities of collecting, cleaning, and standardizing data from various sources, ensuring consistency and reliability across all datasets.
Real-Time and Historical Access: Our data is continuously updated, providing you with both real-time feeds and extensive historical records, enabling comprehensive analysis and back-testing of strategies.
Structured for Immediate Use: Data is organized in a way that aligns with common analytical tools and workflows, allowing for seamless integration into your existing systems without the need for extensive preprocessing.
Quality Assurance: We proactively monitor and resolve data issues, including discrepancies and reporting errors, to maintain the highest data quality standards.
By providing analytics-ready data, Yes Energy empowers you to focus on deriving insights and making informed decisions, rather than spending time on data preparation.
A leading utility-scale trading organization shared several valuable insights from their multi-year experience adopting Snowflake and DataSignals Cloud. Their journey offers practical lessons for others looking to modernize data operations in the energy sector:
1. Break Down Data Silos to Unlock Value
Before adopting Snowflake and DataSignals, this organization faced major challenges with siloed operational databases and highly complex SQL pipelines. These barriers made it difficult for analysts and traders to access and work with large volumes of market data. Centralizing their data infrastructure in Snowflake significantly simplified access and removed the overhead of managing fragile, custom-built pipelines.
2. Empower End Users with Self-Service Access
By layering Yes Energy’s structured, analytics-ready data into a Snowflake environment, the team enabled broader self-service analytics. Traders and analysts could now explore both real-time and historical datasets on demand—without needing to rely heavily on technical teams or rebuild transformations for each query.
3. Accelerate Time to Insight
The new architecture dramatically improved speed from data acquisition to actionable insight. Having a reliable and governed data source readily accessible in Snowflake helped shorten feedback loops, allowing faster scenario testing and more agile decision-making—especially on volatile grid days.
4. Focus on Strategic Data Partnerships
A major takeaway was the importance of working with data providers as long-term partners rather than transactional vendors. Close alignment on roadmap planning and technology adoption helped ensure the solution continued evolving with their needs.
5. Stay Current with Evolving Features
Finally, they stressed the value of keeping up with new Snowflake and DataSignals features—such as improvements in compute efficiency and performance visibility. These innovations continue to reduce friction in data workflows and enhance overall system flexibility.
