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· 14 min read

In today's world of API-driven applications, securing access to data is very important. As the amount of data generated and consumed continues to grow, the need for reliable, and efficient data access management becomes increasingly critical. One essential aspect of data security is ensuring proper authorization for API calls, allowing only the right people, applications, and services to access the required data. Managing access control for your APIs is crucial to guarantee that only authorized users can access and perform specific actions. With the increasing complexity of API ecosystems, secure and efficient authentication and authorization mechanisms are necessary.

Dozer addresses this challenge by adding authorization layer to your data APIs in an efficient way. One of the ways Dozer provides authorization is through the use of JSON Web Tokens (JWT). JWT is a widely used industry-standard method for representing claims securely between two parties. You can learn more about JWT here. Using JWT tokens simplifies the process of adding authorization to your data APIs.

To demonstrate this, we'll build a simple movie ticket booking app that has two roles - user (public) and admin. The user can only book tickets and view their bookings, whereas the admin has full access to all the APIs. We will use @dozerjs/dozer and dozer-react to enhance the functionality of our app. @dozerjs/dozer is a TypeScript wrapper over gRPC APIs generated by Dozer, facilitating seamless communication with Dozer, while dozer-react provides a set of React helpers for using Dozer as a data provider. The sample application's repository can be found here, you can aslo find the instructions to run the application directly.

· 4 min read

Real-time data processing is essential for modern applications, as it ensures that insights are timely and accurate. The open-source project Dozer now provides a runtime environment that allows developers to execute lambda functions or custom user-defined functions based on specific data conditions. This new functionality allows for more dynamic and event-driven applications, as Lambda functions can be triggered based on specific data change conditions. Just imagine triggering a Twilio notification based on a SQL statement (data change condition)!

In this blog post, we will discuss how you can use Dozer to respond to real-time data changes by triggering functions based on data conditions. In this example, we will create a React app that generates a real-time graph based on room temperature data (room temperature monitoring). We will use the pydozer_log module to react to data change conditions and trigger a Twilio message if the room temperature goes above 22.9°C.

· 7 min read

In today's data-driven world, having access to real-time data is crucial and gaining a value from the data is more cumbersome. Another challenge developers face when working with real-time data is efficiently and quickly fetching, processing, and displaying the insights from the data.

Enter Dozer, it simplifies the process of creating low-latency data APIs (gRPC and REST) from any data source easily with OpenAPI specification. Dozer is a real-time data engineering tool built with Rust, which enables seamless integration of data streams(sources), efficient data processing, and lightning-fast response times. This allows developers to focus on building applications without worrying about the complexity of managing real-time data.

To know more about it's capabilities and features you can checkout Dozer repository.

In this blog, we'll walk you through the process of creating a real-time flight data application using Dozer, React. For this particular blog, we are using PostgreSQL as our data source, but it's important to note that there can be multiple data sources that can be connected to Dozer and subsequently used by your React application. By using Dozer, you can quickly create efficient data APIs that enable seamless integration with your frontend applications. PostgreSQL is connected to Dozer via PostgreSQL connector and fetches data from multiple Postgres tables that has a flight data and combines them in real time based on the queries and produces fast low latency APIs.