AUTOMATION & RPA

Data Pipeline

Automation & RPA

Definition

A data pipeline is an automated flow that moves and transforms data from a source to a destination. Unlike traditional ETL, modern pipelines process data in real time or near real time. For example, a pipeline can collect IoT data from machinery, aggregate it, and feed a production dashboard updated to the minute.

Related terms

Automation & RPA

ETL (Extract, Transform, Load)

ETL is the process of extracting data from various sources (ERP, CRM, spreadsheets), transforming it into a uniform format, and loading it into a destination system (data warehouse, dashboard). It is the foundation of business intelligence and analytics. For SMEs, a good ETL process eliminates the hours spent manually consolidating data from different systems.

Business & Strategy

Data Warehouse

A data warehouse is a database optimized for analysis and reporting, containing structured and clean data from various business sources. Unlike a data lake, data is organized in a predefined schema. It is the right choice when you need reliable dashboards, financial reports, and structured analytics.

Automation & RPA

Orchestration

Orchestration is the automatic coordination of multiple services, processes, or systems to complete a complex workflow. An orchestrator decides the order of execution, manages errors and retries, and monitors progress status. In the AI context, orchestrating means coordinating multiple models, APIs, and databases to produce a complete final result.

EXPLORE

More terms in Automation & RPA

RPA (Robotic Process Automation)

RPA is the technology that uses 'software robots' to automate repetitive tasks normally performed by people: copying data between systems, filling out forms, generating reports. It does not require changes to existing systems because it interacts with interfaces as a user would. For SMEs, it is the fastest way to automate processes without changing the management software.

Workflow Automation

Workflow automation is the design and automatic execution of sequences of business activities. For example: when an order arrives, the system checks customer credit, verifies availability, generates the order confirmation, and notifies the warehouse. Tools like n8n, Zapier, and Make allow building complex workflows without writing code.

Process Mining

Process mining is the technique that analyzes information system logs to reconstruct and visualize business processes as they actually occur, not as they should occur. It reveals bottlenecks, deviations, and hidden inefficiencies. It is the ideal starting point before automating: it shows where automation would bring the greatest benefit.

Task Mining

Task mining observes how users interact with the computer (clicks, typing, navigating between applications) to identify repetitive tasks that can be automated. Unlike process mining which analyzes system logs, task mining captures employees' daily work. It reveals automations that no manager would see by analyzing only official processes.

Intelligent Automation

Intelligent automation combines RPA with AI to automate not just repetitive tasks but also those requiring comprehension and decision-making. A 'classic' RPA bot copies data; an intelligent automation reads an invoice, understands its content, decides how to classify it, and registers it in the management system. It is the natural evolution of RPA for mature companies.

Hyperautomation

Hyperautomation is the strategy of automating as many business processes as possible by combining AI, RPA, process mining, and low-code tools. It is not about automating a single task but rethinking entire end-to-end workflows. Gartner identifies it as a mega technology trend. For SMEs, it is a medium-term goal after the first pilot automations.

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