September 2020 Summaries

GraphQL enables schema stitching by creating a single schema from multiple underlying APIs, allowing developers to merge different databases such as Contentful and Postgres into a unified GraphQL API. This is achieved through remote schema relationships in Hasura, which establishes connections between the two databases. By setting up a remote schema relationship, developers can create a single query that retrieves user data from Hasura and content from Contentful, eliminating the need for separate queries to each database. The process involves creating a new table on Hasura, establishing a connection with Contentful through an API key and authorization header, mapping fields between the two databases, and defining a mutation to set favorite recipes. With this setup, developers can build a unified backend API using GraphQL, which can be further expanded upon for a full application.

I've set up a content model in Contentful and created the content type "dogToy" with fields like toyName, toyPhoto, puppyPhoto, estimatedPrice, and linkToBuy. I used GraphiQL to write my first GraphQL query and explore the schema of my Contentful space. I learned how to use transformations to resize images and how to fetch all dog toys using a different query field called "dogToyCollection". With this knowledge, I can now build powerful tools like GraphiQL and leverage my API's type system to control the data I get from my server.

The text discusses the challenges of scaling a content model using Contentful and proposes a multi-space architecture as a solution, which offers autonomy, scalability, and flexibility. The author argues that establishing a multi-space architecture from the start can set up a project for long-term success, even if it requires initial planning. The proposed architecture uses multiple spaces to power different business units, each with its own organization-wide content and components, while also allowing for global content sharing and governance. The text also highlights the benefits of using microservices architecture, which allows for autonomy, productivity, and scalability, as well as minimizing tech debt and flexibility in adapting to changing needs. A real-world example from a customer is presented, demonstrating how a multi-space solution can be tailored to meet specific requirements, such as omnichannel functionality, governance, and scalability.

Retailers carefully plan their store designs to maximize revenue, just like website designers should plan the content placement on their site to offer content in exchange for conversion. To create an AI-driven customer journey with personalization software, personalize your site based on context and behavior before a visitor gives their contact information, use data wisely to make every experience relevant, and combine website personalization with marketing automation to acknowledge every customer need. By doing so, you can provide recommendations and content that are even more personalized, feel confident about displaying up-to-date content, and continuously optimize the customer journey with AI-driven testing and experimentation.

The challenges of operating a global scale come with the need to balance standardization and localization in content strategies, as different regions have unique audiences and cultural differences that must be considered. A multi-space strategy is proposed, where a global space contains all content for all regions, and each region has its own space populated with content from the global space, allowing full autonomy without governance complexities. This approach enables scalability and reduces front-end complexity, ensuring consistency in the content model across markets and reducing human error during replication. The setup also allows for 80% of global content to be reused, with 20% being adapted to local markets, achieving a balance between global strategy and local execution. Implementing AI-based translation pipelines and A/B testing are possibilities to further enhance this strategy.

The new Reference View feature in the Contentful web app enables users to visualize and manage their content model more efficiently, saving time by quickly identifying entry issues and jumping straight to where they need to go to make fixes. The feature provides a clear and nested list of all content used in an entry, showing its status and allowing for easy validation and publication. This allows users to publish complex nested content entries faster and with fewer errors, making it easier to create impactful digital products and experiences.