According to a study by Seagate, only 32% of data available to enterprises is put to work. The remaining 68% is unleveraged. One of the challenges noted is: making the different silos of collected data available. Using automation to bring together figures from disparate systems helps leaders make confident and reliable decisions backed by real-time information. This overview discusses how to use CDC Change Data Capture to enable real-time analysis.

Enjoying This Article?

Receive great content weekly with the Xplenty Newsletter!

Octopus

Table of Contents

How Does CDC Work in AWS S3

Amazon Simple Storage Service (S3) is a cloud-based storage service. S3 makes your data available from any location. As it is a cloud-based service, companies can see improved scaling, availability, security, and performance

The premise of S3 is the concept of buckets. Buckets are containers for objects also sometimes referred to as files. Buckets contain the files and the metadata about the files. To store information in S3 developers upload files to the appropriate buckets. Developers can set permissions for each bucket.

Xplenty offers integrations that allow you to connect AWS S3 to other data sources.

Prerequisites for Using AWS S3 as a Target

AWS includes the Data Migration (DMS) service for using Amazon S3 as a target. There are three requisites before developers can get started:

Location of S3 Bucket

The S3 bucket you are using for the AWS region must reside in the same region as the DMS instance you are using for migration.

IAM Role Requirements

Identity Access Management (IAM) roles are used to assign permissions to accounts to determine access to the system.

Specific IAM rules include:

  • The account used for the migration has the IAM role with write and delete permissions
  • The role must have tagging so that any objects written to the target can be tagged
  • The IAM role is added as a trusted entity

Integrate Your Data Today!

Try Xplenty free for 14 days. No credit card required.

Octopus

CDC and Transaction Order

Transaction order refers to how the system writes changes to the logs. The two methods are:

CDC Without Transaction Order

By default, changes in AWS DMS are not logged in order of the transaction. Instead, it stores all changes in one or more files for each table. AWS creates directories on the target database to store the changes coming from the source.

Capturing Changes With Transaction Order

AWS DMS can be configured to store transactions in order. This approach requires setting S3 endpoint settings. These settings direct DMS to store the changes in .csv files. These files contain all row changes listed by transaction order. 

Using Xplenty’s no-code tools, you can quickly build integrations that use several AWS functions such as Amazon AuroraAmazon RDS, and Amazon Redshift.

Using AWS Data Migration Service (DMS) for CDC

Developers have several items to configure before using DMS. These tasks are:

Schema Conversion

The schema represents the logical configuration of a database. The schema from the source database must be converted to that of the target. This ensures the database configurations match so the information can be updated successfully

Configure Replication Instance

In AWS DMS, a replication instance hosts one or several replication tasks. The replication instances must have enough storage and processing power to migrate the information. DMS performs most processing in memory. However, larger transactions may require disk space for buffering. 

Specify Database Endpoints

The endpoints specify connection information about the data store. The endpoints also specify datastore type and location information. One endpoint must be an AWS service. Thus, you can’t migrate from one on-premise data store to another on-site data store.

Create Replication Tasks

The replication tasks migrate the data from the source to the target. You must specify a replication instance the task will use. 

Best Practices for Using AWS Data Migration Service for CDC Change Data Capture

Despite the benefits of CDC, it can quickly cause problems if the process isn’t configured properly. Below are a few best practices to follow to minimize issues.

Use Row Filtering When Handling Large Tables

Filtering rows to find updates on large tables could negatively affect performance during the process. To improve performance, break the process into multiple tasks.

Reduce Load on Source Database

An AWS DMS full load task performs a full table scan of the source table. The full load task also runs queries to locate changes to apply to the destination database. Running a table scan and queries could affect performance. To minimize these issues, limit the number of tasks or tables for the migration.

Removing Bottlenecks on Target Database

There may be processes running on the target database that competes with the migration. Turn off unnecessary triggers and secondary indexes for the first load. You can turn them back on for ongoing migrations

Frost & Sullivan's research shows that almost a quarter of IT decision-makers say that automation is one of the top technologies they use to reduce costs and positively affect the bottom line. Automation empowers leaders to assess new market opportunities and make strategic decisions. CDC Change Data Capture is a valuable tool in gathering these insights.

How Xplenty Can Help

Enjoying This Article?

Receive great content weekly with the Xplenty Newsletter!

Octopus

The Xplenty data integration platform enables you to bring together figures from disparate systems to supply key insight into the business. If you’d like to try these integrations firsthand get in touch with our team and experience the Xplenty platform for yourself.