For Amazon OpenSearch Service, our customers usually want their application for application debugging and security monitoring, and they also want performance monitoring. For those three use cases, we implemented Amazon OpenSearch Service, which can fulfill all these requirements for them. It's also affordable and cheap compared to other tools, so that's the reason we choose Amazon OpenSearch Service. Additionally, a major requirement for clients is that they want cloud native services, specifically for AWS. For analytics tasks in Amazon OpenSearch Service, we use it for application debugging and it has been very useful for us over the last six months.
I have used Amazon OpenSearch Service ( /products/amazon-opensearch-service-reviews ) in an e-commerce project that handles a large number of products with pricing and photos. We have employed it as a search tool for documents containing all product information. I have also used it for analyzing APIs and performance, creating dashboards with analytics for our platforms. Furthermore, I use it for analyzing logs from our back-end systems, allowing me to extract data to identify errors and endpoints failing, presenting this information in visualizations for troubleshooting and monitoring.
I primarily use Amazon OpenSearch Service for log management and data storage. It's used to store third-party data and manage large volumes of query data across various services, including AWS Lambda and Kubernetes.
I use it for database. For RAG, you need a vector store to store embeddings. To store the vectors, you need embedding models to convert the data into vectors. You then need to store those vectors in any vector store. Popular ones are like Chroma DB. As a new alternative, I selected OpenSearch, which falls under the whole AWS infrastructure. So to bring our full architecture into AWS, I use OpenSearch as a service as my vector store.
Amazon OpenSearch Service is often used for log analysis, real-time application monitoring, and searching large datasets. Users benefit from its scalability, ease of use, and AWS integration, appreciating its capability to handle high data volumes while providing efficient search functionalities.
Many users choose Amazon OpenSearch Service for its powerful search and indexing capabilities, real-time analytics, and strong integration with AWS services. Key highlights include minimal...
For Amazon OpenSearch Service, our customers usually want their application for application debugging and security monitoring, and they also want performance monitoring. For those three use cases, we implemented Amazon OpenSearch Service, which can fulfill all these requirements for them. It's also affordable and cheap compared to other tools, so that's the reason we choose Amazon OpenSearch Service. Additionally, a major requirement for clients is that they want cloud native services, specifically for AWS. For analytics tasks in Amazon OpenSearch Service, we use it for application debugging and it has been very useful for us over the last six months.
I have used Amazon OpenSearch Service ( /products/amazon-opensearch-service-reviews ) in an e-commerce project that handles a large number of products with pricing and photos. We have employed it as a search tool for documents containing all product information. I have also used it for analyzing APIs and performance, creating dashboards with analytics for our platforms. Furthermore, I use it for analyzing logs from our back-end systems, allowing me to extract data to identify errors and endpoints failing, presenting this information in visualizations for troubleshooting and monitoring.
I primarily use Amazon OpenSearch Service for log management and data storage. It's used to store third-party data and manage large volumes of query data across various services, including AWS Lambda and Kubernetes.
I use it for database. For RAG, you need a vector store to store embeddings. To store the vectors, you need embedding models to convert the data into vectors. You then need to store those vectors in any vector store. Popular ones are like Chroma DB. As a new alternative, I selected OpenSearch, which falls under the whole AWS infrastructure. So to bring our full architecture into AWS, I use OpenSearch as a service as my vector store.
We use the solution as a login platform. We have a lot of microservices, and we get log records from there, which we host on Amazon OpenSearch.