Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Data Insertion and Data Persistence in a Vector Database.
In the previous post in the Deep Dive series, we have introduced how data is processed in Milvus, the world’s most advanced vector database. This article will examine the components of data insertion, illustrate the data model in detail, and explain how data persistence is achieved in Milvus.
If you attend conferences or read technical articles, you could think that microservices are the correct and only way to build a system at the moment.
Despite some pushback from cooler heads, the default architecture is microservices. In this post, I'd like to argue why it's wrong. I'll first get back to the origin of microservices and the fundamental reason to use them. Then, I'll describe why microservices don't fit most organizations' structures. Afterward, I'll move to detail the root problem to solve. I'll conclude by proposing an alternative, less-risky approach.
In the previous two posts in this blog series, we have already covered the system architecture of Milvus, the world's most advanced vector database, and its Python SDK and API.
This post mainly aims to help you understand how data is processed in Milvus by going deep into the Milvus system and examining the interaction between the data processing components.
As internet connectivity increases, an increasing number of small devices now exist with small memory and compute capacities. These sensors connect various physical entities, such as your home alarm, thermal sensors, and car. The data from millions of these connected devices needs to be collected and analyzed. For example, weather data collected from multiple sensors can be utilized to forecast weather for wind energy and farming. There are billions of connected devices in homes, factories, oil wells, hospitals, cars, and thousands of other places that are fueling digital transformation, generating huge volumes of data and growing exponentially.
As IoT has become very common in the manufacturing industry for handling machine data and optimizing production, the concept of Industrial IoT (IIoT) was developed. Let’s learn more about this now.
At Twelve Data, we have a lot of experience in creating various APIs. Some of these tips have been gained through practical errors. We would like to share with you some of them that we ourselves are following in order to make it easier to develop and maintain our own product in the future.
From the very beginning of the development of your API, no matter how small it may seem to you, take versioning into an account in your architecture. If you don't want to write code to support versioning right away, then at least try to agree on how you will do it in the future. At the very least, you need to be prepared for this.