Rob Richardson

Database DevOps with Containers

Stop letting database environments slow your team down. Learn to build an automated pipeline that delivers safe, production-like data inside a container.

Database DevOps with Containers
#1about 4 minutes

Defining the core principles of a DevOps pipeline

A standard application DevOps pipeline focuses on reliability, consistency, and cost, with source control serving as the single source of truth.

#2about 4 minutes

Understanding the unique challenges of database DevOps

Unlike applications, the production database is the master copy, requiring a pipeline to safely bring production data into non-production environments.

#3about 3 minutes

Preparing production data for development environments

Safely use production data by anonymizing private information, sanitizing secrets, and shrinking its size while preserving its unique characteristics.

#4about 5 minutes

How containers differ from virtual machines

Containers virtualize the operating system for greater efficiency, while virtual machines virtualize the underlying hardware.

#5about 7 minutes

Exploring the Docker ecosystem and image layers

The Docker ecosystem uses a Dockerfile to build a layered, immutable image which is then run as a lightweight, isolated container.

#6about 4 minutes

Using SQL Server containers for dev and test

While production databases may run on VMs or as a service, containers provide an ideal, ephemeral environment for development and testing.

#7about 5 minutes

Building a dev-safe database image with a Dockerfile

A multi-stage Docker build can restore a production backup, run a transformation script, and package only the sanitized data into a clean final image.

#8about 3 minutes

Running and verifying the sanitized database container

After building the image, run the container and connect to it to verify that all data has been correctly anonymized, sanitized, and is ready for development.

#9about 2 minutes

Features and limitations of SQL Server on Linux

SQL Server on Linux supports core database engine features like SQL CLR, but lacks Windows-dependent components like Reporting and Analysis Services.

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Matching moments

A DBA's journey to running SQL Server on Kubernetes
03:02 MIN

A DBA's journey to running SQL Server on Kubernetes

Adjusting Pod Eviction Timings in Kubernetes

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The evolution of running databases in containers
03:05 MIN

The evolution of running databases in containers

Databases on Kubernetes: Why you should care

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Why running databases in containers is now a reality
04:41 MIN

Why running databases in containers is now a reality

Databases on Kubernetes

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Bridging gaps with DevOps and containerization
02:34 MIN

Bridging gaps with DevOps and containerization

From Punch Cards to AI-assisted Development

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Solution design using dacpacs and Azure DevOps pipelines
02:41 MIN

Solution design using dacpacs and Azure DevOps pipelines

Automated MS SQL Server database deployments with dacpacs and Azure DevOps

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Integrating serverless deployments into a DevOps pipeline
01:52 MIN

Integrating serverless deployments into a DevOps pipeline

Serverless on Cloud

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Demonstrating the business value of containerization
02:01 MIN

Demonstrating the business value of containerization

Using Containers to deploy AI Models across our microscopy platform

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Using the Modern Data Stack and DBT for transformations
05:00 MIN

Using the Modern Data Stack and DBT for transformations

Modern Data Architectures need Software Engineering

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