Data Formats and Middleware Training

Duration: 8 hours

Alpinum’s Data Formats and Middleware Training helps engineers understand how modern systems exchange, transform and route data across distributed software environments. The course is designed for engineers working with APIs, messaging systems, embedded platforms, IoT systems, middleware or heterogeneous data pipelines.

The training combines data representation, schema design and communication patterns used in real engineering systems.

Who Should Attend?

This course is suitable for:

• • Software engineers building distributed systems

• • Embedded and IoT engineers working with data exchange

• • Systems engineers integrating multi-protocol platforms

• • Teams using messaging, serialization or middleware technologies

What the Course Covers

Participants learn how to compare and use different data formats and middleware technologies.

Key topics include:

• • JSON, XML and YAML

• • Protocol Buffers

• • gRPC

• • MQTT

• • Kafka

• • DDS

• • Schema design and data transformation

Practical Labs

The course includes labs on serialization and data representation, middleware and message queuing. Participants work with text-based and binary formats, compare performance trade-offs, implement publisher-subscriber communication and explore streaming pipelines.

Lab 1: Serialization and Data Representation

Learning Objectives: Understand the trade-offs between human-readable and machine-efficient data formats.

Tasks:

• • Parse and validate data across JSON, XML, and YAML formats.

• • Implement schema-based serialization using Protocol Buffers to reduce payload size.

• • Compare performance metrics between text-based (JSON/XML) and binary (Protobuf) serialization.

Extension Tasks:

• • Develop a custom converter to transform data between XML and YAML.

• • Implement versioning strategies for Protobuf schemas to ensure backward compatibility.

Lab 2: Middleware and Message Queuing

Learning Objectives: Master communication patterns in distributed systems using diverse messaging protocols.

Tasks:

• • Implement an asynchronous publisher-subscriber model using MQTT for lightweight IoT messaging.

• • Develop a streaming data pipeline using Kafka to handle high-volume event processing.

• • Configure gRPC services to facilitate high-performance, contract-based communication.

Extension Tasks:

• • Analyze message latency in DDS (Data Distribution Service) vs. MQTT deployments.

• • Integrate Kafka Connect to stream data between databases and message brokers.

Project

The course can include a Data Transformation Gateway project. Participants design a service that ingests data from multiple sources, normalises it into a unified schema and routes it to selected back-end systems.

Learning Objectives: Build a functional gateway that bridges disparate data formats and messaging protocols.

Description: Develop a central “Transformation Gateway” service that ingests data from multiple sources, normalizes it, and routes it to specified backends.

Tasks:

• • Ingestion: Accept data inputs in JSON, YAML, and Protobuf formats.

• • Normalization: Convert all incoming data to a unified internal schema.

• • Routing: Dispatch processed data to Kafka or MQTT brokers based on content-based filtering.

Coverage Matrix

Assessment

• • Hands-on Exercises: Directed development blocks focused on track-specific implementation challenges.

• • Quizzes: Milestone-based knowledge checks to ensure students remain aligned with architectural best practices throughout the project lifecycle.

Learning Outcomes

By the end of the course, participants will understand how to select appropriate data formats, build middleware-based communication flows and design practical data transformation systems.

Data Formats and Middleware Training FAQs