Stale since Mar 2025.
The final piece of the puzzle: Data access layer In the previous installments of this series (part 1 [https://hasura.io/blog/escaping-the-data-doom-loop-in-financial-services], part 2…
Learn why tracking the evolution of your data architecture in Hasura's Data Delivery Network (DDN) isn't just nice to have – it's essential for robust governance and intelligence.
In Part 1, we covered the data doom loop [https://hasura.io/blog/escaping-the-data-doom-loop-in-financial-services] – the cycle of complexity, inefficiency, and eroding trust in financial data that persists despite massive investments. The solution isn’t another sweeping…
PromptQL addresses the limitations of traditional approaches like RAG, Text-to-SQL, and Tool Calling. PromptQL provides measurable improvements in accuracy (with a clear path to reaching 100% accuracy) when connecting LLMs to enterprise data.
Across industries – and especially in highly regulated sectors like healthcare, financial services, and government – MongoDB has been a preferred database solution for organizations handling large volumes of sensitive data that require strict compliance adherence. In such…
Financial firms that successfully escape the data doom loop are shifting toward a pragmatic, business-driven approach focused on three key elements.
In today's data-driven world, connecting AI to your organization's data remains a significant challenge. What if you could build AI assistants that can query databases, access customer support tickets, and even take actions—all using natural language?
By integrating web search in PromptQL, you can enrich your private data – stored in any systems like Snowflake, BigQuery, PostgreSQL, MongoDB, or accessed through APIs – with current real-time details from the web.
Hasura’s metadata-based approach is a data governance solution, acting as a semantic layer for your data, preserving clear lineage and robust governance.
We’re thrilled to introduce the Platform Dashboard for Hasura Data Delivery Network (DDN) – a comprehensive solution to elevate your operational efficiency, data observability, and decision-making.
One of the most effective guardrails in AI systems today is human-in-the-loop (HITL). This blog explores why HITL systems are crucial, how they align with the principles of agentic AI, and how PromptQL simplifies their implementation in the AI Assistant interface.
In this blog post, we’ll explore the features and limitations of the current monitoring setup, how we’re leveraging Grafana, Prometheus, and Hasura’s Data Delivery Network (DDN) to offer a more comprehensive solution, and what this means for the future of monitoring in Hasura…
Over the past few weeks, we’ve introduced a series of exciting updates to PromptQL that enhance its power, accessibility, and collaboration capabilities. These enhancements are focused on simplifying workflows. Everything is now live and ready to explore!
With the PromptQL Program API, you can now trigger PromptQL programs via HTTP, bringing flexibility and intelligence to your business workflows. The API ensures seamless integration outside the chat interface of the PromptQL Playground.
This last-mile perspective transforms anomaly detection from a data quality tool into a strategic business capability. So, instead of just catching errors, it becomes a tool for validating the business's narrative across all data domains
We’re excited to announce that Hasura Data Delivery Network (DDN) now offers GraphQL subscriptions, making it easy for developers to build real-time features into their applications.
We are excited to introduce plugins to Hasura Data Delivery Network (DDN), a powerful framework designed to let you easily extend the capabilities of your API with custom logic via HTTP webhooks.
Whether you’re modernizing your infrastructure, enabling telehealth, or building AI-driven solutions, Hasura DDN ensures your platform is ready to meet the data challenges of today and tomorrow.
What's becoming increasingly clear is that active metadata isn't just another tool in the modern data stack – it's a key enabler of several contemporary data management strategies.
Check out this step-by-step guide to integrating and composing data from different domain APIs using Hasura Data Delivery Network (DDN).
Imagine a simple, standardized, data-source-agnostic data quality service that could complement existing approaches and dramatically improve data health through rapid feedback loops.
Learn why building a unified data access layer is one of the most critical requirements in the modern enterprise, and about making this transition.
By adopting Hasura, ISOS has elevated the quality and performance of its digital services. This positions ISOS to better serve clients and uphold their mission of saving lives.
GraphQL and OpenAPI define agreements between data consumers and providers, but they have significant differences in capabilities and contract specifications. Whichever you use, it's essential to be intentional, understand your use case, and design for the best outcome.
The director of the data platform team explains that Hasura far surpasses traditional catalogs by providing critical integration with data warehouses – an essential capability for effective governance and self-serve access.
With the support of Python functions on Hasura DDN, you can focus on writing sync or async functions that return some data. Hasura will do the code introspection and generate all the configurations needed for a production-ready GraphQL API.
Leveraging the supergraph architecture to achieve data governance objectives is a strategic play that delivers a solid return on investment. It reduces complexity, lowers costs, and improves metadata accuracy.
In enterprise data and API landscapes, this helps address challenges with federated data access and makes use cases like API orchestration and API composition easier to tackle.
Discover what goes into a good GraphQL API for MongoDB, what it takes to build and maintain one, and how Hasura’s new framework allows developers to kickstart new projects and scale existing projects rapidly.
Discover core tenets of the supergraph architecture and how it benefits API integration and API aggregation use cases in part one of this blog series.
In the first implementation, HMH leveraged Hasura's Snowflake connector with Snowflake data warehouse to provide a lightweight API for service-to-service use cases within the organization.
GraphQL may have crested the Peak of Inflated Expectations since, but it's reached a broad flat plateau where the weather is mild and the terrain is gentle. There have been laudable successes and no overt failures. Needs were met, products were created, wars were fought, and…
"With Hasura, we're not just keeping pace; we're setting the pace, offering new features and experiences faster than ever before," said the Head of Data Architecture, capturing the transformative effect of Hasura on the company’s data operations.
If you’ve ever found yourself wishing you could use Hasura-flavored GraphQL with your SQLite database, you’re in luck because today we are excited to announce the release of the Hasura Turso connector.
It’s your lucky day, because today we are excited to bring you instant GraphQL APIs on top of DuckDB with the Hasura DuckDB connector.
Today we are excited to announce that performing a vector search has never been easier with the release of the new Hasura Qdrant connector.
For GraphQL adoption to become viable for the organization, they needed Hasura's connect-and-compile approach. Hasura not only made GraphQL feasible but also satisfied additional criteria the organization had established for their chosen GraphQL solution.
Today I’ll talk about what it’s been like working as a Community Engineer at Hasura, including a bit about my weekly schedule, and the things we’re working on to support our community.
Hasura’s supergraph architecture addressed many challenges of this Fortune 100 while meeting their need for a unified query engine on all data.
With local development support in Hasura DDN developer tooling, you can now easily build a complete supergraph, and run connectors and the engine on your local machine. This allows you to iterate quickly with hot reload support and build with no limits.
When migrating to Hasura, the team had not set explicit targets for speed, other than “not worse.” And the team has blown that goal away by an order of magnitude.
In this blog, we will share our (opinionated, but grounded) perspective on why Hasura is better than Apollo for building and running production GraphQL APIs at scale in most enterprise use cases.
Discover how Hasura and Apollo can collaborate effectively, especially using Apollo Federation. We'll highlight scenarios where Hasura excels in developing and managing scalable GraphQL subgraphs and how it accelerates GraphQL Federation with Apollo, particularly for enterprises.
Practical step-by-step recipe for building a GraphQL API server in Java, utilizing Spring Boot, Spring for GraphQL, and a relational database. It emphasizes the importance of persistence, flexibility, efficiency, and modernity in the design.
We are excited to announce the release of Hasura’s native data connector for MongoDB. With this connector, users are able to rapidly author extremely capable APIs for data stored in MongoDB using Hasura’s Data Delivery Network (DDN).
Learn how benchmark the potential value of your GraphQL adoption journey to get leadership approval or, in some cases, to better articulate the self-evident benefits and promote GraphQL to other teams.
Focus on writing sync or async functions that return some data, and Hasura will do the code introspection and generate all the configurations needed for a production-ready GraphQL API.
In Hasura v3, developers enable access to their data sources via connectors. These connectors can be chosen from the Hasura Connector Hub, and customized. Alternatively, developers can build bespoke connectors for unique data sources and APIs.