1. Data Ingestion

  • Definition: Data ingestion involves collecting data from various sources (databases, APIs, files, real-time streams) and loading it into a system for further processing or storage.
  • Concepts:
    • Batch Ingestion: Data is collected in large chunks and ingested at scheduled intervals.
    • Streaming Ingestion: Data is ingested in real-time as it arrives, continuously processing events.

2. Data Storage

  • Definition: Data storage involves storing data in a structured or unstructured format depending on the use case.
  • Concepts:
    • Relational Databases: Store data in structured, tabular formats using SQL (e.g., MySQL, PostgreSQL).
    • NoSQL Databases: Handle unstructured or semi-structured data (e.g., MongoDB, Cassandra).
    • Data Lakes: Store raw data in its original format (e.g., Amazon S3, Azure Data Lake).
    • Data Warehouses: Structured storage for analytics and reporting (e.g., Snowflake, BigQuery).

3. Data Pipelines

  • Definition: Data pipelines are automated workflows that transport data from source to destination, often with multiple transformation stages in between.
  • Concepts:
    • ETL (Extract, Transform, Load): Data is extracted from a source, transformed into a desired format, and loaded into a target system.
    • ELT (Extract, Load, Transform): Data is extracted and loaded as-is, and transformations are performed afterward, often within the storage system.
    • Data Orchestration: Scheduling and coordinating the execution of data pipelines and workflows (e.g., Apache Airflow).

4. Data Transformation

  • Definition: Transforming raw data into formats suitable for analysis, machine learning, or storage.
  • Concepts:
    • Data Cleansing: Removing or correcting erroneous data.
    • Aggregation: Summarizing data (e.g., calculating averages, totals).
    • Normalization and Denormalization: Structuring data for optimized storage and querying.

5. Data Processing

  • Definition: Processing data to generate insights or transform it into a usable format.
  • Concepts:
    • Batch Processing: Processing data in bulk at scheduled intervals (e.g., Apache Spark, Hadoop).
    • Stream Processing: Real-time data processing for time-sensitive applications (e.g., Apache Flink, Kafka Streams).
    • Distributed Processing: Using multiple machines to process large-scale data simultaneously, increasing speed and efficiency.

6. Data Quality

  • Definition: Ensuring that data is accurate, consistent, complete, and reliable for analysis.
  • Concepts:
    • Data Validation: Checking data for correctness and consistency.
    • Data Profiling: Analyzing data to understand its structure, quality, and content.
    • Error Handling: Managing and resolving data issues during ingestion and processing.

7. Data Governance

  • Definition: The practice of managing data assets with policies, processes, and standards that ensure data integrity, security, and compliance.
  • Concepts:
    • Data Cataloging: Organizing and documenting data assets.
    • Data Lineage: Tracking the flow and transformation of data through systems.
    • Access Control: Defining who can access and manipulate data (e.g., role-based access).

8. Data Security

  • Definition: Protecting data from unauthorized access and ensuring compliance with regulations.
  • Concepts:
    • Encryption: Securing data in transit and at rest.
    • Data Masking: Hiding sensitive data elements.
    • Compliance: Adhering to data privacy regulations like GDPR, HIPAA.

9. Data Scaling and Performance

  • Definition: Ensuring that data systems can handle growing data volumes and perform efficiently under heavy loads.
  • Concepts:
    • Horizontal Scaling: Adding more machines to handle larger workloads.
    • Vertical Scaling: Increasing the power of a single machine (CPU, RAM, etc.).
    • Partitioning and Sharding: Distributing data across multiple storage units for efficient querying.

10. Data Monitoring and Maintenance

  • Definition: Regularly monitoring the health and performance of data pipelines and systems, ensuring they run smoothly.
  • Concepts:
    • Monitoring Tools: Tools like Prometheus, Grafana for tracking pipeline health and system performance.
    • Alerting: Notifying engineers of pipeline failures, data quality issues, or performance bottlenecks.

11. Cloud Data Engineering

  • Definition: Using cloud platforms to build and manage data infrastructure, offering scalability and flexibility.
  • Concepts:
    • Cloud Storage: (e.g., AWS S3, Google Cloud Storage).
    • Managed Data Warehouses: (e.g., BigQuery, Redshift, Snowflake).
    • Serverless Computing: (e.g., AWS Lambda, Google Cloud Functions).

12. Data Modeling

  • Definition: Designing the structure of data to represent relationships and enable efficient querying and analytics.
  • Concepts:
    • Star Schema: A type of data model used in data warehouses.
    • Entity-Relationship Model: Mapping entities and their relationships in databases.

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