Category: Advanced (300)

This is a guest post written by Jon Haddad an Apache Cassandra committer specializing in performance tuning, fixing broken clusters, and cost optimization. In this post, we discuss the basics of improving the performance of Amazon EBS with Cassandra to take advantage of the operational benefits. We explore some basic tools used by Cassandra operators to gain insight into key performance metrics. You can then apply these metrics to modify key operating system (OS) tuneables and Cassandra configuration. Finally, we review benchmarks on performance gains by implementing best practices for Amazon EBS.

With Oracle Multitenant, you can consolidate standalone databases by either creating them as PDBs or migrating them to PDBs. Performance Insights has introduced a new PDB dimension to help you visualize and analyze the distribution of the load on individual PDBs within the CDB on a RDS for Oracle instance. Now, you can slice the database load metric by the PDB and SQL dimensions to identify the top queries running on each of the PDBs. In this post, we will discuss how to identify resource-intensive SQL queries at a PDB level on a visual dashboard in Performance Insights.

Channel Corporation is a B2B software as a service (SaaS) startup that operates the all-in-one artificial intelligence (AI) messenger Channel Talk. In Part 1 of this series, we introduced our motivation for NoSQL adoption, technical problems with business growth, and considerations for migration from PostgreSQL to Amazon DynamoDB. In this post, we share our experience integrating with other services to solve areas that couldn’t be addressed with DynamoDB alone.

Channel Corporation is a B2B software as a service (SaaS) startup that operates the all-in-one artificial intelligence (AI) messenger Channel Talk. This two-part blog series starts by presenting the motivation and considerations for migrating from RDBMS to NoSQL. In this post, we discuss the motivation behind Channel Corporation’s architecture modernization with Amazon DynamoDB, the reason behind choosing DynamoDB, and the four major considerations before migrating from Amazon Relational Database Service (Amazon RDS) for PostgreSQL.

When clients start running queries on new Amazon Aurora replicas, they will notice a longer runtime for the first few times that queries are run; this is due to the cold cache of the replica. As the database runs more queries, the cache gets populated and the clients notice faster runtimes. In this post, we focus on how to address the cold cache so clients that are connecting through a load-balanced endpoint get a consistent experience regardless of whether the replicas are automatically or manually scaled. In addition, we also look at other caching solutions such as Amazon ElastiCache, a fully managed Memcached, Redis, and Valkey compatible service, that can further improve the overall experience for latency-sensitive applications and, in some situations (such as higher cache hits), lead to less frequent auto-scaling events of the Aurora read replicas.

Amazon DynamoDB, Amazon Bedrock, and LangChain can provide a powerful combination for building robust, context-aware chatbots. In this post, we explore how to use LangChain with DynamoDB to manage conversation history and integrate it with Amazon Bedrock to deliver intelligent, contextually aware responses. We break down the concepts behind the DynamoDB chat connector in LangChain, discuss the advantages of this approach, and guide you through the essential steps to implement it in your own chatbot.

In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In Part 3, we set up Amazon API Gateway and deployed AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job, creating a seamless data flow from IPFS to the knowledge base. In this post, we demonstrate how to configure MetaMask authentication, create a frontend interface, and test the solution.

In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia testnet using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In this post, we set up Amazon API Gateway and deploy AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job.

In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, specifically focusing on the ingestion of training data. In this post, we focus on the writing and deployment of the Ethereum smart contract that contains the outcome of the DAO decisions.

Blockchain and generative AI are two technical fields that have received a lot of attention in the recent years. There is an emerging set of use cases that can benefit from these two technologies. In this four-part series, we build a solution that governs the training data ingestion process of an AI model, using a smart contract and serverless components. We guide you through the different steps to build the solution. In this post, we review the overall architecture of the solution, and set up a large language model (LLM) knowledge base.