As we continue to innovate and add new features on behalf of our customers, you can now maintain and manage query plans in Amazon Aurora PostgreSQL-Compatible Edition. This feature enables you to identify the query plans contributing to the current database load and track the performance statistics of query plans over time. In this post, we demonstrate how you can monitor query plans to maintain optimal database performance, and discuss some key use cases of the monitoring query plan feature. Efficient query planning is a crucial part of database performance, so we have added features such as ..read more

At Amazon Web Services (AWS), we have been making steady progress towards bringing our zero-extract, transform, and load (ETL) vision to life. With Amazon Aurora zero-ETL integration to Amazon Redshift, you can bring together the transactional data of Amazon Aurora with the analytics capabilities of Amazon Redshift. The integration helps you derive holistic insights across many applications, break data silos in your organization, and gain significant cost savings and operational efficiencies. At AWS re:Invent 2023, AWS announced four new zero-ETL integrations to make data access and analysis f ..read more

One critical aspect during the migration process from Oracle to PostgreSQL is identifying any semantic issues that may arise when translating Oracle-specific code to PostgreSQL. Although Oracle performs both semantic and syntax checks during code compilation, PostgreSQL focuses solely on syntax checks during code compilation, leaving room for potential semantic discrepancies in the converted code base. This could result in issues during functional testing and application runtime. To address this issue, database developers working with PL/pgSQL code in PostgreSQL can use the plpgsql_check exten ..read more

In recent times, there has been a growing interest in using foundation models (FMs) to build generative AI applications. These models are trained on vast amounts of data and are capable of performing tasks that were previously thought to be the exclusive domain of humans, such as creating art and music. However, when it comes to using FMs in enterprise-level applications, there is a significant limitation. These models are trained on publicly available data and lack awareness of enterprise-specific contexts, such as customer segments, product inventory, and individual customer histories. This ..read more

Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in Amazon Aurora PostgreSQL-Compatible Edition using pgvector. pgvector adds additional capabilities for efficient storage and fast retrieval of data stored as high-dimensional vectors. Some use cases, such as searching ecommerce product ..read more

PostgreSQL has become the preferred open-source relational database for many enterprise companies and startups, powering leading business and mobile applications. Many customers choose PostgreSQL as the target database when migrating from commercial engines such as Oracle and Microsoft SQL Server. AWS offers Amazon Relational Database Service (Amazon RDS) for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition as fully managed PostgreSQL database services. You can migrate from SQL Server to PostgreSQL using the AWS Schema Conversion Tool (AWS SCT) and the AWS Database Migration Service ..read more

Migrating databases from Oracle to either Amazon Aurora PostgreSQL-Compatible Edition or Amazon Relational Database Service (Amazon RDS) for PostgreSQL presents a unique challenge when migrating JSON data. Oracle CLOB data type can contain both well-formed and invalid JSON, whereas the PostgreSQL JSON and JSONB data type requires JSON data to be correctly formatted according to RFC 8259. Therefore, transferring data stored in Oracle’s CLOB data type to JSON and JSONB data type in Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL requires a meticulous examination of data ..read more

This post was co-written with Rajesh Saluja, Principal Engineer at Intuit. Intuit is the global financial technology environment that powers prosperity for 100 million consumer and small business customers with TurboTax, Credit Karma, QuickBooks, and Mailchimp. Intuit has built on AWS since 2013 and has taken an “all-in” approach in its move to the cloud. The company has been at the forefront of innovation in the cloud, adopting AWS technologies for infrastructure, machine learning (ML), data analytics, and more. It also helps in integrating its own capabilities to create best-in-class custome ..read more

The typical migration process for an Oracle database to Amazon Aurora PostgreSQL-Compatible Edition or Amazon Relational Database Service (Amazon RDS) for PostgreSQL requires both automated and manual procedures. The AWS Schema Conversion Tool (AWS SCT) can handle the automated duties of schema conversion. For specific database objects that can’t be automatically migrated, the manual duties entail post-schema AWS SCT migration touch-ups. The majority of the custom code and the source database’s schema are automatically converted by AWS SCT to a format that is compatible with the target databas ..read more

This is the first post of a two-part series. In this post, we discuss how to migrate SQL Server indexed views to materialized views in Amazon Aurora PostgreSQL-Compatible Edition or Amazon Relational Database Service (Amazon RDS) for PostgreSQL. The second post in this series will walk you through the migration of SQL Server indexed views to materialized views in Babelfish for Aurora PostgreSQL. SQL Server indexed views are used to improve the performance of queries that aggregate data from multiple tables. Materialized views, on the other hand, are used in PostgreSQL databases to store a quer ..read more