Integrating Lambda With Relational Databases

Bob Strahan shows how to integrate AWS Lambda with relational databases running on EC2:

Here are a few reasons why you might find this capability useful:

  • Instrumentation: Use database triggers to call a Lambda function when important data is changed in the database. Your Lambda function can easily integrate with Amazon CloudWatch, allowing you to create custom metrics, dashboards and alarms based on changes to your data.

  • Outbound streaming: Again, use triggers to call Lambda when key data is modified. Your Lambda function can post messages to other AWS services such as Amazon SQS, Amazon SNS, Amazon SES, or Amazon Kinesis Firehose, to send notifications, trigger external workflows, or to push events and data to downstream systems, such as an Amazon Redshift data warehouse.

  • Access external data sources: Call Lambda functions from within your SQL code to retrieve data from external web services, read messages from Amazon Kinesis streams, query data from other databases, and more.

  • Incremental modernization: Improve agility, scalability, and reliability, and eliminate database vendor lock-in by evolving in steps from an existing monolithic database design to a well-architected, modern microservices approach. You can use a microservices architecture to migrate business logic embodied in database procedures into database-agnostic Lambda functions while preserving compatibility with remaining SQL packages.

His specific example is around Oracle/Postgres, but I’d imagine you could do the same on SQL Server with the CLR.

Related Posts

VM Storage Performance in the Cloud

Joey D’Antoni explains how storage architecture has changed from on-prem to the cloud: This architecture design dates back to when a storage LUN was literally a built of a few disks, and we wanted to ensure that there were enough I/O operations per second to service the needs of the SQL Server, because we only […]

Read More

Machine Learning and Delta Lake

Brenner Heintz and Denny Lee walk us through solving data engineering problems with Delta Lake: As a result, companies tend to have a lot of raw, unstructured data that they’ve collected from various sources sitting stagnant in data lakes. Without a way to reliably combine historical data with real-time streaming data, and add structure to […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031