Data Workshop Overview

The Data Workshop is a comprehensive platform for creating sophisticated data-driven projects tailored to solve specific organizational challenges such as Machine Learning models, custom APIs, ETL processes, and more. The Data Workshop serves as a robust workspace equipped with versatile tools and environments, enabling data teams to innovate and deploy specialized solutions effectively. ARPIA's intuitive interface and extensive capabilities empower organizations to harness the full potential of their data assets, offering development, visualization, storage, processing, and integration tools to address diverse business needs.

Objects

The Data Workshop encompasses various types of essential objects necessary for constructing comprehensive data solutions, including:

• Extract and Load:It is a process that involves extracting data from one source (such as a database or file) and loading it into another destination (such as a data warehouse or application).
• Repository Table: This could be a specific table used to store metadata or information about other objects in the repository.
• Transform and Prepare: This refers to the process of cleaning, structuring, and transforming data for further analysis or use. This may include filtering data, changing formats, or merging information from different sources.
• AI and Machine Learning: It is a field of artificial intelligence that focuses on developing algorithms and models that allow computers to learn from data without being explicitly programmed. These models can perform tasks such as classification, prediction, clustering, and pattern detection.
• Visual Objects: Utilize ARPIA's embedded visualization engine to create insightful data visualizations and analytics.
• Data Models (Kubes):Data models (also known as Kubes) are logical representations of data structure. They can include tables, relationships, attributes, and other elements.
• DataApps: Build interactive applications that consume processed data, enhancing user interaction and data utilization.
• APP Droplets Object: App Droplets are small components or modules that can be added to an application in the Data Workshop. These droplets can provide specific functionality, such as visualizations, data processing, or integrations with other tools.

Development Environments

ARPIA Data Workshop supports multiple development environments tailored to specific project requirements:

• PHP: Develop rapid applications and APIs suitable for web applications and ETL processes.
• Python: Ideal for building Machine Learning models and performing advanced data analysis tasks.
• WAC Web Application Container: Deploy and manage web applications persistently as servers. ?????????

Serverless Objects (Containers)

Create and deploy applications that run continuously, providing custom solutions or integration APIs accessible to end-users across your organization.

Computing Resources

ARPIA utilizes Docker technology to execute processing and application containers efficiently:

• Shared Container Resources: Economical and controlled computing resources shared among users, suitable for comprehensive data solutions.
• Dedicated Container Resources: Exclusive cluster resources allocated to an organization, ensuring performance and scalability tailored to specific enterprise needs.

Development Interface

Each object within ARPIA Data Workshop offers a development interface for streamlined application creation and management:

• Global Files: Share libraries and functions across project objects to enhance development efficiency and code reuse.
• Connecting to Data Repository: Define and access the project's data repository, managing tables and datasets essential for project execution.
• Running and Scheduling Projects: Execute projects manually or schedule automatic executions based on predefined timeframes and conditions.
• Cloning Projects and Objects: Clone projects and objects for reuse, facilitating code replication and project adaptation to different data repositories.
• Dynamic Project Execution: Configure project execution parameters dynamically to manage workload and optimize performance based on organizational needs.