AWS Database Blog
Category: Learning Levels
Model molecular SMILES data with Amazon Neptune and RDKit
Modeling chemical structures can be a complex and tedious process, even with the help of modern programs and technology. The ability to explore chemical structures at the most fundamental level of atoms and the bonds that connect them is an essential process in drug discovery, pharmaceutical research, and chemical engineering. By infusing chemical research with […]
Build hypothetical indexes in Amazon RDS for PostgreSQL with HypoPG
Indexes in PostgreSQL are essential for improving the performance of database queries. They serve as data structures that organize and optimize the retrieval of information from database tables. By creating indexes on specific columns, PostgreSQL can locate and access relevant data more efficiently. Indexes work by creating a separate data structure that contains a sorted […]
Amazon Keyspaces (for Apache Cassandra) support for Cassandra v3.11 end of life schedule
Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra-compatible database service. With Amazon Keyspaces, you can run your Cassandra workloads on AWS using the same Cassandra application code and developer tools that you use today. You don’t have to provision, patch, or manage servers, and you don’t have to install, […]
Alternatives to the Oracle flashback database feature in Amazon RDS for Oracle
Customers may prefer to host their Oracle database workloads in a managed service such as Amazon Relational Database Service (Amazon RDS) for Oracle because of the benefits offered by managed services. However, there could be workloads that have dependencies on Oracle features that aren’t supported by Amazon RDS for Oracle. For example, the flashback database […]
Cost-effective bulk processing with Amazon DynamoDB
Your Amazon DynamoDB table might store millions, billions, or even trillions of items. If you ever need to perform a bulk update action against items in a large table, it’s important to consider the cost. In this post, I show you three techniques for cost-effective in-place bulk processing with DynamoDB. Characteristics of bulk processing You […]
Automate the migration of Microsoft SSIS packages to AWS Glue with AWS SCT
When you migrate Microsoft SQL Server workloads to AWS, you might want to automate migration and minimize changes to existing applications, but still use a cost-effective option without commercial licenses and reduce operational overhead. For example, SQL Server workloads often use SQL Server Integration Services (SSIS) to extract, transform, and load (ETL) data. In this […]
How Twilio modernized its Messaging Postflight service data store with Amazon DynamoDB
Twilio is a customer engagement platform that drives real-time, personalized experiences for leading brands. Twilio has democratized communications channels like voice, text, chat, and video by virtualizing the world’s telecommunications infrastructure through APIs that are simple enough for any developer to use, yet robust enough to power the world’s most demanding applications. Twilio supports an […]
Accelerate graph query performance with caching in Amazon Neptune, Part 3: Neptune cluster-wide caching architectures with Amazon ElastiCache
Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]
Accelerate graph query performance with caching in Amazon Neptune, Part 2: Additional Neptune caching features
Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]
Accelerate graph query performance with caching in Amazon Neptune, Part 1: Queries and buffer pool caching
Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]