1 Data Lifecycle and Pipelines
Data engineering revolves around managing the data lifecycle, which refers to the various stages data goes through, from initial collection to final deletion. A data pipeline is a series of processes that automate the movement of data through each stage of this lifecycle. The goal is to ensure that raw data is transformed into a usable format for analytics or machine learning.
Stages of the Data Lifecycle:
- Data Collection: Data is gathered from various sources like databases, APIs, user interactions, or IoT devices.
- Data Ingestion: Data is ingested into storage systems, such as data lakes or data warehouses.
- Data Processing: Data is transformed, cleaned, and enriched. This can be done using either batch processing (large volumes at scheduled intervals) or real-time processing (as data arrives).
- Data Storage: Processed data is stored in a data warehouse for structured data or a data lake for raw/unstructured data.
- Data Analysis: The data is queried, analyzed, and visualized for decision-making using SQL, BI tools, or machine learning algorithms.
- Data Archiving: Old or unused data is archived for compliance or historical reference.
Diagram: Data Pipeline Flow
In this pipeline:
- Data is collected from sources like user clicks, sensors, or APIs.
- It is ingested into a data lake or directly into a data warehouse.
- Processing transforms the data using ETL or ELT strategies.
- Data is stored for long-term analysis or machine learning applications.
