GraphQL is a technology that makes data querying tasks much more efficient and intuitive for the users. Here, we have looked at how it solves potential problems such as data over-fetching and solves network latency problems that can arise in a services-based solution. Ballerina provides built-in support for implementing GraphQL services in a quick and easy manner, where the user can just concentrate on the business logic.
[GraphQL] has become a prominent technology in implementing data APIs. It provides a convenient and intuitive approach for querying data. Let’s look at a sample use case using the [Ballerina] programming language and see how GraphQL compares to other traditional approaches such as implementing REST-style HTTP APIs.
Let’s take a typical e-commerce scenario of processing orders in an online store. The entity-relationship diagram below shows a typical representation that can be used in a relational database. This is of course a simplified representation of a real-life implementation.
Your Data Architecture: Simple Best Practices for Your Data Strategy. Don't miss this helpful article.
In this post, we'll learn Getting Started With Data Lakes.<br><br> This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that's designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You'll also explore key benefits and common use cases.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Data Quality Testing Skills Needed For Data Integration Projects. Data integration projects fail for many reasons. Risks can be mitigated when well-trained testers deliver support. Here are some recommended testing skills.
A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".