ML Pipeline Data Integration

- Overview

Data integration is the process of combining data from multiple sources into a single, unified view. This process can consolidate structured, unstructured, batch, and streaming data. Data integration is often used for machine learning (ML) and artificial intelligence (AI), which can help overcome challenges and harness the full potential of data assets. 

AI can integrate ML models and stream-processing technologies to enable organizations to gain instant insights and make data-driven decisions. For example, ML models can analyze large amounts of data and provide users with personalized recommendations, predictions, and insights. 

Data integration can involve:

The three integration stages for ML are data acquisition, data understanding, and company acceptance. An ML model is only as good as the data being used to train it. Bad data is often referred to as “Garbage in, Garbage out”. 

Data integration is commonly used for: AI and ML, data lake development, cloud migration and database replication, IoT, and real-time intelligence.

- ETL Processes

The ETL process, or extract, transform, and load, is a data integration process that combines data from multiple sources into a single data store. The data is then prepared for storage, analysis, and machine learning. 

The ETL process involves three steps:

• Extract: Identify and copy data from its sources
• Transform: Clean and organize the data using business rules
• Load: Move the transformed data into the target data store

The ETL process can help businesses make informed decisions, Increase productivity, and Ensure compliance with data laws. 

Some challenges that can occur during the ETL process include improper planning. The three phases of the ETL process are often run in parallel to save time.