FA-0737DevOps, Cloud & InfrastructureSoftware DevelopmentData & Analytics

SQL Server, Kafka and Container Deployment

Integrated database, messaging and Linux deployment labs

Introduction

Why this course

Develop practical skills for working with SQL Server and Apache Kafka in Linux-based container environments. Guided modules cover relational queries, database tooling, distributed messaging, client development and deployment considerations.

SQL Server 2019 provides the historical feature context; lab software and client tools are selected for compatibility and current support. The course is intended for learners with existing SQL and programming experience.

Learning outcomes

Run and inspect containerised services, including ports, volumes and persistent data.
Use SQL Server tables, queries, stored procedures and T-SQL in guided exercises.
Explore database notebooks, visualisation, Python integration and migration considerations.
Configure Kafka topics, partitions, replication and consumer groups in a controlled Linux lab.
Build and test selected Java or Python producer and consumer examples.
Explain how Docker and Kubernetes affect service networking, state and deployment.

Prerequisites

Operational SQL knowledge and experience using SQL Server.
Working knowledge of Java or Python; examples in the other language may be demonstrated.
Basic Linux command-line and remote-access skills.
A workstation and access to the designated compatible lab environment; SQL Server container exercises require a supported x86-64 Linux host.

Training outline

6 modules

01Module 1 — Docker and the Deployment Environment5 topics

Overview of SQL Server, Kafka and container-based deployment.
Docker setup appropriate to the workstation and designated Linux host.
Images, containers, processes, ports, volumes and basic image/container commands.
Run PHP/Apache and Nginx examples; map ports and volumes and inspect data persistence.
Guided exercises and deployment-environment checks.

02Module 2 — SQL Server on Linux6 topics

Pull and run a compatible SQL Server Linux container image on the supported lab host.
Connect using Visual Studio Code with the MSSQL extension, or SSMS where appropriate.
Review SQL commands; create databases and tables, query data and use stored procedures and T-SQL.
Explore selected SQL Server 2019 features in their version context rather than as the newest release.
Introduce Kubernetes, big-data concepts and Apache Spark; distinguish their roles from the relational database.
Practical database exercises.

03Module 3 — Database Tools, Analysis and Migration6 topics

Use supported SQL tooling and relevant extensions for queries and saved SQL work.
Explore notebooks, data visualisation, Python integration and query-history workflows using compatible lab tools.
Demonstrate a supplied machine-learning example and discuss its relationship to database data.
Consider data portability, migration and related Spark/container technologies.
Review retired SQL Server 2019 Big Data Clusters as historical architecture, not a currently supported deployment option.
Exercises and evaluation.

04Module 4 — Kafka Concepts and First Deployment5 topics

Brokers, clusters, network ports, topics, records, partitions and producer/consumer architecture.
Use KRaft metadata management for a compatible current Kafka lab; compare ZooKeeper ensembles as historical architecture.
Prepare Ubuntu/Linux, remote development access and the required runtime; install an appropriate Apache Kafka distribution.
Send and receive records with console tools; run multiple producers and consumers.
Create partitioned topics, work with selected partitions and offsets, and inspect log storage.

05Module 5 — Kafka Clusters, Replication and Consumer Groups6 topics

Configure and start a multi-broker lab cluster; inspect supported administration tools, topic metadata and logs.
Produce and consume against cluster endpoints; discuss multi-cluster boundaries without assuming automatic cross-cluster replication.
Create replicated topics and observe replica placement, logs and storage.
Simulate broker failures and observe recovery in the controlled lab.
Use explicit consumer groups, inspect partition assignments and understand idle members.
Assessment of cluster and consumer behaviour.

06Module 6 — Performance and Client Development6 topics

Run basic producer/consumer performance tests; compare configuration changes and inspect consumer lag.
Set up Java clients; implement producers, serializers and multiple-producer examples.
Implement consumers; distinguish consumer offset commits and partition assignment from producer configuration.
Develop Python producer and consumer examples using a compatible client.
Discuss container/Kubernetes networking, persistent storage and stateful service identity for database and messaging deployment.
Hands-on development and assessment.

A programme built around your team.

Share your training goals and requirements.