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Data Engineer Guide

Updated 2025.03.24

What is a Data Engineer?​

A data engineer is a professional with expertise in large-scale data processing and infrastructure management, supporting the successful deployment and operation of AI Solutions. They are responsible for designing and building optimal data architectures that enable AI Solutions to function smoothly. The primary goal of a data engineer is to build reliable and high-performance systems that meet business needs. Additionally, increasing operational efficiency through system automation and optimization is a key objective.

Challenges Faced by Data Engineers​

Common challenges for data engineers include high technical requirements for constructing complex data pipelines, time-consuming data management and cleaning processes, compatibility issues across different systems, and the need for ongoing learning of modern data technologies and tools. Mellerikat provides a wide range of features to help data engineers overcome these challenges and achieve their goals.

Automated Solutions with Mellerikat​

Mellerikat supports data engineers in designing and installing optimal architecture. Data engineers can use Mellerikat to install and manage Edge App, Edge Conductor, and AI Conductor. During this process, they thoroughly evaluate the functions and requirements of each component to design and implement an architecture suited to the field environment. They also integrate with existing MLOps systems and continuously optimize and maintain the infrastructure.

With Mellerikat, data engineers can build more advanced data architectures and make meaningful AI-driven contributions to business. Mellerikat plays a key role in helping data engineers meet their goals and provides practical support for addressing common technical challenges.

User Manual for Data Engineers​

The following sections provide comprehensive guides for data engineers to utilize Mellerikat efficiently, with step-by-step instructions for key features and workflows. This enables data engineers to make full use of the platform and achieve their objectives quickly and accurately.


Mellerikat Workflow for Data Engineers​

  1. Environment Analysis & Requirements Definition: The data engineer analyzes the deployment environment for AI Solutions and defines the installation requirements for Edge App, Edge Conductor, and AI Conductor. This involves thoroughly understanding the user’s needs and ensuring that the system is well-suited to its intended environment.

  2. Architecture Design & Implementation: Based on each component’s functionality and requirements, the data engineer designs and builds an optimized architecture. For example, Edge App may be installed on Splunk Edge Hub, NVIDIA Jetson Nano, On-Premise WSL, or cloud environments. The goal is to implement an architecture that fits the real-world deployment scenario.

  3. Component Installation: Once the architecture is in place, the data engineer installs Edge App, Edge Conductor, and AI Conductor. When installing Edge App, configuration settings must include connection information for Edge Conductor.

  4. System Integration & Interfacing: The system is configured to integrate smoothly with existing MLOps platforms (e.g., SageMaker). This ensures that legacy systems and Mellerikat-based solutions operate together without issues.

  5. Monitoring & Maintenance: With Mellerikat’s support, the system is optimized and continuously monitored for performance. In case of issues, prompt action is taken to ensure stable and reliable system operations.


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