Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Artificial Intelligence › Machine Learning & Data › Data pipeline
Ingestion pipeline
concept · part of Data pipeline
An ingestion pipeline is a series of processes that automate the collection, transformation, and movement of data from various sources into a centralized data repository (e.g., data warehouse, data lake, cloud storage). The goal is to ensure data is consistently available, in a suitable format, for analytics or machine learning tasks. Pipelines can support real-time streaming or batch processing. A well-designed pipeline ensures data is clean, accurate, and delivered in a timely manner.
Three key considerations: Scalability: Design pipeline to scale with increasing data volumes; use cloud services that allow dynamic scaling. Data quality: Validate and clean data before loading; integrity is crucial for analytics and modeling. Data security: Implement encryption, authentication, and access controls; protect data in transit and at rest; comply with regulations like GDPR or CCPA.
An e-commerce company collects data from user interactions, sales transactions, and customer feedback. Using an ingestion pipeline, data is brought into a centralized data lake in near real-time. Examples:
- Website interactions: Streaming ingestion captures clickstream data in real time for user behavior analysis.
- Sales transactions: Batch ingestion aggregates daily sales data for financial reporting.
- Customer feedback: Feedback from emails, social media, surveys is ingested, transformed, and stored for holistic customer sentiment analysis.
Inside Ingestion pipeline (6)
- Batch ingestion — Batch ingestion processes large volumes of data at scheduled intervals.
- Data extraction — Automate extraction from each source.
- Data loading — Load processed data into target storage system.
- Data source identification — Identify sources of data to be ingested: databases, APIs, flat files, logs, structured or unstructured data.
- Pipeline scheduling and monitoring — Schedule batch ingestion processes and monitor pipeline health.
- Streaming ingestion — Streaming ingestion brings in data continuously in real time.
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