Amazon Aurora MySQL-Compatible Edition offers multiple authentication methods to securely authenticate database user access and meet different security needs. The most common method of authentication is using a user name and password. This can create additional overhead for both users and database administrators to manage and rotate these credentials; it also requires additional investments in auditing and governance. Amazon Aurora MySQL now supports Microsoft Active Directory (AD) authentication using Kerberos for Amazon Aurora MySQL version 3.03 and higher. With support for AWS Directory Ser ..read more

In order to build an application that interacts with the Bitcoin blockchain, whether it be a wallet, an Ordinals marketplace, or a BTC exchange, you must be able to reliably access the Bitcoin network. For example, you will need to read critical data from the blockchain that acts as input for properly constructed Bitcoin transactions. The most foundational of these data needs is retrieving data pertaining to transaction outputs for a given wallet from the Bitcoin network, which serves a variety of use cases. For example, retrieving unspent transaction outputs (UTXOs) to be consumed as inputs f ..read more

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this two-part series, we demonstrate how to find and link similar entities in Amazon Neptune, a managed graph database service. In part 1, we used lexical search to find entities with similar text. In this post, we use semantic search to find entities with similar meaning. In both cases ..read more

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this two-part series, we demonstrate how to find and link similar entities in Amazon Neptune, a managed graph database service. In this post, we use lexical search to find entities with similar text. In part 2, we use semantic search to find entities with similar meaning. In both cases ..read more

Organizations want to move their critical Oracle workloads to Amazon Relational Database Service (Amazon RDS) for Oracle with minimal downtime and disruption to unlock the agility, elasticity, and innovation of the AWS Cloud. In this post, we explore options for migrating Oracle databases from a legacy platform (for example HPUX, AIX, SOLARIS and others) to Amazon RDS for Oracle using RMAN Cross-Platform Transportable Tablespaces Backup Sets (XTTS) and AWS Database Migration Service (AWS DMS). Oracle database migration using XTTS is a physical migration strategy where Oracle data is copied at ..read more

In PostgreSQL, object binding (or dependencies) encompasses the connections existing among various database elements. These interdependencies hold significant importance when it comes to the management and modification of objects within the database. They ensure that adjustments made to one object don’t inadvertently disrupt other dependent objects. This series is divided into two posts. In the Managing object dependencies in PostgreSQL – Overview and helpful inspection queries (Part 1) post, we introduced object dependencies and discussed various types of dependencies with examples. We also d ..read more

In PostgreSQL, object binding (or dependencies) encompasses the relationships existing among various database elements. These interdependencies hold significant importance when it comes to the management and modification of objects within the database. They ensure that adjustments made to one object don’t inadvertently disrupt other dependent objects. For instance, when a view relies on a table, any alterations to the table structure, such as modifying utilized columns, altering data types, or even dropping the table, can directly influence the functionality of the associated view. To make inf ..read more

Amazon DynamoDB is a serverless, NoSQL database service that enables you to develop modern applications at any scale. DynamoDB on-demand mode offers a truly serverless experience that can serve millions of requests per second without capacity planning, and automatic scale down to zero when no requests are being issued against the table. With on-demand mode’s simple, pay-per-request pricing model, you don’t have to worry about idle capacity because you only pay for the capacity you actually use. Customers increasingly use on-demand mode to build new applications where the database workload is c ..read more

In Part 1 of this series, we discussed the high-level architecture of multi-threaded full load and change data capture (CDC) settings to tune related parameters for better performance to replicate data to an Amazon Kinesis Data Streams target using AWS Database Migration Service (AWS DMS). In Part 2, we provided some examples of how we can yield different results by adjusting the multi-threaded settings. In this post, we discuss other key considerations when using Kinesis Data Streams as a target. Prerequisites To follow along with this post, you should have familiarity with the following AWS ..read more

In Part 1 of this series, we discussed the architecture of multi-threaded full load and change data capture (CDC) settings, and considerations and best practices for configuring various parameters when replicating data using AWS Database Migration Service (AWS DMS) from a relational database system to Amazon Kinesis Data Streams. In this post, we demonstrate the effect of changing various parameters on the throughput for the full load and CDC phases. The main parameters we considered are the AWS DMS settings for the parallel load and parallel apply and the number of shards in Kinesis Data Stre ..read more