Extending DevOps to SQL Server

Aug 7, 2017Download as PPTX, PDF0 likes428 views

The document discusses extending DevOps practices to SQL Server databases. It defines DevOps as representing a change in IT culture that focuses on rapid delivery through agile practices, collaboration between operations and development teams, and use of automation tools. The document outlines DevOps practices for databases including developing database code alongside application code, testing in isolation, source control, and shifting validation and testing earlier in the development cycle. It emphasizes automating provisioning, monitoring throughout the development lifecycle, and balancing speed with production protection.

Extending Devops
to SQL Server
Grant Fritchey | Redgate Software

Goals
• Learn methods and approaches that can speed
development of database-centric applications using
SQL Server
• Understand practices that will help to ensure
enhanced protection of production systems.

Let’s Talk
scarydba.com
grant@scarydba.com
@gfritchey
Grant Fritchey

Devops
• Gartner:
Devops represents a change in IT culture, focusing on rapid IT
service delivery through the adoption of agile, lean practices in the
context of a system-oriented approach. DevOps emphasizes
people (and culture), and seeks to improve collaboration between
operations and development teams. DevOps implementations
utilize technology – especially automation tools that can leverage
an increasingly programmable and dynamic infrastructure from a
life cycle perspective.

Devops
• An agile culture that better supports
collaboration between people that uses
automation and tooling in support of
process.

Develop
Database is code
Source
control
Isolation Testing

Build
• Compare between database & database
• Compare between database & source control
• Compare between ? & ?
State
• Scripts build on scripts
• Which build on scriptsMigrations
• Mix & match as neededHybrid

Validate & Test
One test at a time
Build and
deployments are a
test
Only write tests for
what will break

Package
Application and
database together
Branches
Known definition

Release
Marked in
some way for
clarity
Works only
from source
control
Uses
validation
Uses testing
Crosses
Environments

Monitor
Monitor
Starts
before
deployment
Through
deployment
After
deployment
Drives
innovation
and tuning

Provision
Fundamental
to “shift left”
As automated
as possible
As self-service
as possible
Protection
cannot be
sacrificed

Reading
• DevOps and Database Lifecycle Management
• DevOps for the Database: The Inside Story
• Shift Left to Avoid Failure
• DevOps and “Shift Left” for Databases

Moving from manual processes for development and deployment to a more automated approach requires a great deal of work and knowledge. In this all day seminar we will go through the steps to help you along this journey. We will start with understanding how source control works and end with automated deployments across environments You’ll learn about processes and tools that not only make it easier and faster to move database changes, but add protection to your production information. We will discuss tools, process and the fundamental changes in culture necessary to take your database development and deployment into a high functioning DevOps team.

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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

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How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik

This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements. Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows. We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure. The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.

Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

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Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach. Read More:- https://www.esofttools.com/nsf-to-pst-converter.html

What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell

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In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals. This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft. What You’ll Learn in Part 2: Explore real-world nonprofit use cases and success stories. Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!

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