Data Pipelines

Jul 1, 2024

Data Pipelines

Introduction

  • Definition: Automated systems for collecting, transforming, and delivering data to make it usable. 
  • Purpose: Cleaning, structuring, and moving large amounts of data from various sources.
  • Importance: Enables informed business decisions and drives innovation.

General Stages of a Data Pipeline

  1. Collect
    • Sources: Databases (e.g., MySQL, PostgreSQL, DynamoDB), data streams (e.g., Apache Kafka, Amazon Kinesis), applications, IoT devices.
  2. Ingest
    • Tools: Apache Kafka, Amazon Kinesis (for real-time streaming), batch processing, change data capture.
  3. Store
    • Options: Data lakes (e.g., Amazon S3, HDFS), data warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery).
  4. Compute
    • Processing Types: Batch processing (e.g., Apache Spark, Apache Hadoop, Apache Hive), stream processing (e.g., Apache Flink, Google Cloud Dataflow, Apache Storm).
    • ETL/EL: Tools like Apache Airflow, AWS Glue for loading and transforming data.
  5. Consume
    • Uses: Data science (e.g., Jupyter notebooks, TensorFlow, PyTorch), Business Intelligence (e.g., Tableau, PowerBI), self-service analytics (e.g., Looker).
    • Applications: Predictive modeling, dashboards, machine learning (e.g., fraud detection).

Key Concepts and Tools

  • Batch Processing: Processes large volumes of data at scheduled intervals (e.g., Apache Spark, Hadoop).
  • Stream Processing: Processes real-time data as it arrives (e.g., Apache Flink, Google Cloud Dataflow).
  • Data Lakes: Store raw and processed data; efficient for large-scale storage (e.g., Amazon S3, HDFS).
  • Data Warehouses: Store structured data for querying and analysis (e.g., Snowflake, Amazon Redshift).
  • ETL (Extract, Transform, Load): Critical to the compute phase; transforms raw data into structured format.
  • Consume Phase: Data is used by various stakeholders like data scientists and business intelligence teams.
  • Machine Learning Models: Continuous learning and improvement from new data (e.g., fraud detection models).

Summary

  • The lecture provided a broad overview of what data pipelines are, their importance, key stages, and tools involved in each stage.
  • Emphasis on both technology and use cases in real-world scenarios (e-commerce, business intelligence, machine learning).