Heterogeneous Data Servers Training

Duration: 8 hours

Alpinum’s Heterogeneous Data Servers Training helps engineers design systems that collect, normalise, store and visualise data from multiple protocols and sources. The course is designed for software engineers, systems engineers, embedded teams and data infrastructure teams working with connected devices, telemetry streams and multi-protocol platforms.

The training focuses on practical data ingestion, storage strategy and real-time system visibility.

Who Should Attend?

This course is suitable for:

• • Software engineers building data ingestion services

• • Embedded and IoT engineers working with telemetry data

• • Systems engineers integrating multiple communication protocols

• • Teams using SQL, NoSQL or time-series databases

What the Course Covers

Participants learn how to design services that handle different data sources and storage models.

Key topics include:

• • REST APIs and gRPC

• • MQTT and CAN data sources

• • ETL pipelines

• • SQL and NoSQL storage

• • Time-series databases

• • Streaming pipelines

• • Multi-protocol data aggregation

• • Dashboard visualisation

Practical Labs

The course includes labs on multi-protocol data ingestion and database integration. Participants work with structured and semi-structured data, real-time event streams, ETL logic, schema design and database storage strategies.

Lab 1: Multi-Protocol Data Ingestion

Learning Objectives: Implement ingestion pipelines capable of handling diverse communication protocols and data streams.

Tasks:

• • Develop service endpoints using REST APIs and gRPC to ingest structured and semi-structured data.

• • Configure streaming pipelines to handle real-time event data from MQTT and CAN bus sources.

• • Implement ETL (Extract, Transform, Load) logic to normalize data from these disparate protocols into a unified format.

Extension Tasks:

• • Implement backpressure mechanisms in the streaming pipeline to handle data spikes.

• • Develop a schema registry to manage evolving data structures across gRPC and REST services.

Lab 2: Database Integration and Storage

Learning Objectives: Optimize data persistence strategies by leveraging SQL, NoSQL, and time-series database architectures.

Tasks:

• • Design database schemas for relational (SQL) and non-relational (NoSQL) storage.

• • Implement time-series database storage for high-frequency sensor and telemetry data.

• • Execute complex queries to retrieve and aggregate data across multiple database types.

Extension Tasks:

• • Optimize database indexing strategies for time-series data retrieval.

• • Implement data replication and sharding to ensure high availability and scalability.

Project:

The course can include a Multi-Protocol Data Aggregation Server project. Participants configure listeners for CAN, MQTT and REST inputs, route data to suitable storage systems and build a live dashboard for current and historical system metrics.

Learning Objectives: Integrate heterogeneous data ingestion, multi-model storage, and real-time visualization.

Description: Build a comprehensive “Data Aggregation Server” that collects data from CAN, MQTT, and REST sources, stores it efficiently, and presents it on a live dashboard.

Tasks:

• • Aggregation: Configure listeners for CAN, MQTT, and REST inputs.

• • Persistence: Route incoming data to the appropriate storage (SQL for metadata, NoSQL/Time-series for telemetry).

• • Visualization: Develop a web-based dashboard to display real-time system metrics and historical trends.

Coverage Matrix

Assessment

• • Hands-on Exercises: Interactive labs provided for each protocol and database type.

• • Quizzes: Knowledge checks integrated throughout the module to validate understanding of heterogeneous data architectures.

Learning Outcomes

By the end of the course, participants will understand how to build data servers that manage heterogeneous inputs, storage models and real-time visualisation needs.

Heterogeneous Data Servers Training FAQs