DMS
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Introduction
Migrates databases to AWS and remains available during migration
Supports:
Homogeneous migrations: ex. Oracle to Oracle
Heterogeneous migrations: ex. Microsofr SQL Server to Aurora
Continuous Data Replication using
Must create an EC2 instance to perform the replication tasks
Works over VPC Peering / VPN / Direct Connect
Supports loads: full load / full load + CDC / CDC only
Feature
Sources:
On-premise and EC2 instances databases: Oracle, MS SQL Server, MySQL, MariaDB, PostgreSQL, SAP, DB2, Mongo DB.
Azure: Azure SQL Database
RDS: all including Aurora
S3 (csv files as input)
Targets:
On-premise and EC2 instances databases: Oracle, MS SQL Server, MySQL, MariaDB, PostgreSQL, SAP
RDS
Redshift
DynamoDB
DocumentDB
S3
ElasticSearch
Kinesis Data Steams
Schema Conversion Tool (SCT)
Converts Database schema from one engine to another
OLTP: SQL Server / Oracle to MySQL / PostgreSQL / Aurora
OLAP: Teradata / Oracle to Redshift
can filter data by column value
can filter columns according by prefix
can add prefix to table name
For Oracle:
As source: supports TDE for the source using "BinaryReader"
As target: supports BLOBs in tables that have a primary key, and TDE
For ElasticSearch:
As source: NOT SUPPORTED
As target: possible to migrate through DMS from a relational DB
Calculate the number of queue slots required for the ES bulk request as a product of the number of indexes, shards, and replicas.
Adjust DMS subtask and thread parameters accordingly.
Can combine with Snowball Edge
Steps:
Use AWS Schema Conversion Tool to extract the data locally and move it into Snowball Edge
Ship Snowball edge back to AWS
Data is loaded into S3 bucket
DMS takes the S3 files and migrates data to the target data store. With CDC, updates are written to S3 bucket and then applied to the target data store