Managing infrastructure as code (IaC) instead of using manual processes makes it easy to scale systems and minimize errors. Rosemary Wang (Developer Advocate, HashiCorp, and author of “Essential Infrastructure as Code: Patterns and Practices”) is an infrastructure engineer at heart and an aspiring software developer who is passionate about teaching patterns for infrastructure as code to simplify processes for system admins and software engineers familiar with Python, provisioning tools like Terraform, and cloud service providers.
The definition of infrastructure has expanded to include anything that delivers or deploys applications. Infrastructure as software or infrastructure as configuration, according to Rosemary, are ideas grouped behind infrastructure as code—the process of automating infrastructure changes in a codified manner, which also applies to DevOps practices, including version controls, continuous integration, continuous delivery, and continuous deployment. Whether you’re using a domain-specific language or a programming language, the practices used to collaborate between you, your team, and your organization are the same—create one application and scale systems.
The ultimate result and benefit of infrastructure as code is automation. Many developers take advantage of managed offerings like Confluent Cloud—fully managed Kafka as a service—to remove the operational burden and configuration layer. Still, as long as complex topologies like connecting to another server on a cloud provider to external databases exist, there is great value to standardizing infrastructure practices. Rosemary shares four characteristics that every infrastructure system should have:
In addition, Rosemary and Tim discuss updating infrastructure with blue-green deployment techniques, immutable infrastructure, and developer advocacy.
EPISODE LINKS:
Apache Kafka 3.5 - Kafka Core, Connect, Streams, & Client Updates
A Special Announcement from Streaming Audio
How to use Data Contracts for Long-Term Schema Management
How to use Python with Apache Kafka
Next-Gen Data Modeling, Integrity, and Governance with YODA
Migrate Your Kafka Cluster with Minimal Downtime
Real-Time Data Transformation and Analytics with dbt Labs
What is the Future of Streaming Data?
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Apache Kafka 3.4 - New Features & Improvements
How to use OpenTelemetry to Trace and Monitor Apache Kafka Systems
What is Data Democratization and Why is it Important?
Git for Data: Managing Data like Code with lakeFS
Using Kafka-Leader-Election to Improve Scalability and Performance
Real-Time Machine Learning and Smarter AI with Data Streaming
The Present and Future of Stream Processing
Top 6 Worst Apache Kafka JIRA Bugs
Learn How Stream-Processing Works The Simplest Way Possible
Building and Designing Events and Event Streams with Apache Kafka
Rethinking Apache Kafka Security and Account Management
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