![]() Table partitioning is the technique of distributing data across multiple partitions that in turn improves query performance and data manageability. A basic understanding of Amazon Athena federated queries.Ĭreate partitioned and standalone tables in the RDS for PostgreSQL instance.For this post, our bucket is called historicalbucket. An S3 bucket where you store the historical data.User credentials (access key and secret access key) configured on the EC2 instance by running aws configure.The AWS CLI installed on the EC2 instance.An EC2 instance with psql installed to connect to Amazon RDS for PostgreSQL.For this post, we use Amazon RDS for PostgreSQL. An AWS account with an Amazon RDS instance running.You should have the following prerequisites in place: Test joining the historical data with RDS for PostgreSQL tables using Athena.To join historical data with the RDS for PostgreSQL instance current tables, we use Athena federated queries.Specify the input dataset location as the S3 bucket where the CSV files are stored. In the Data Catalog database, create table structures (partitioned and non-partitioned) from the actual database whose historical data you want to put in Amazon S3.This database enables you to query historical data stored in Amazon S3. Create a database in the AWS Glue Data Catalog.This is set for our Athena workgroup, and is required before running any query. Archive the table’s data in CSV files and upload the CSV files to the S3 bucket in the respective table directories.Create a directory structure inside the S3 bucket to store the historical data from the partitioned and standalone tables.Create partitioned and standalone (non-partitioned) tables in the RDS for PostgreSQL instance. ![]() To build out this solution, this blog post walks you through the steps listed below: This solution helps set up the pipeline to join historical data with current data in the system using Amazon Athena. The diagram also shows querying the current data from Amazon RDS for PostgreSQL using AWS Lambda. However, you can use other formats as well.Ī user queries the historical data from Amazon S3 via the AWS Glue Data Catalog, using Amazon S3 as the data source. In the following diagram, the client is an Amazon Elastic Compute Cloud (Amazon EC2) instance where you can install your application, or install the utilities like psql client, AWS Command Line Interface (AWS CLI), and others.įor this solution, we’ll upload historical data to Amazon S3 in the form of CSV files. In this post, we walk through how to move archived data from Amazon Relational Database Service (Amazon RDS) for PostgreSQL to Amazon Simple Storage Service (Amazon S3), fetch historical (archived) data from S3, and use SQL’s to write queries that join data between Amazon RDS for PostgreSQL and Amazon Athena. Developers need a solution that lets them benefit from using cheaper storage for archived data, but they can still use SQL queries to join data between the active and archival systems. However, there are often business requirements where an application must query both active data and archived data simultaneously. While databases are used to store and retrieve data, there are situations where applications should archive or purge the data to reduce storage costs or improve performance.
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