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The Data Engineering Lifecycle | IABAC

The Data Engineering Lifecycle transforms raw data into actionable intelligence through stages of ingestion, storage, processing, transformation, governance, and delivery. It ensures scalable, high-quality, and reliable data pipelines that empower analytics, AI, and business decision-making in modern data-driven organizations.

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The Data Engineering Lifecycle | IABAC

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  1. THE DATA ENGINEERING LIFECYCLE iabac.org

  2. WHAT IS DATA ENGINEERING? Data Engineering is the backbone of modern AI and analytics. It involves designing, building, and maintaining systems that enable efficient data collection, transformation, and access. Goal: Ensure data is reliable, scalable, and ready for analysis. 01 iabac.org

  3. THE LIFECYCLE OVERVIEW Data Ingestion Data Storage Data Processing Data Transformation Data Quality & Governance Data Delivery & Consumption 02 iabac.org

  4. STAGE 1 – DATA INGESTION Involves collecting data from multiple sources (APIs, IoT, DBs, files). Modes: Batch vs. Real-time ingestion. Tools: Apache Kafka, AWS Kinesis, Fivetran. Key Principle: Ensure scalability and fault- tolerance. 03 iabac.org

  5. STAGE 2 – DATA STORAGE Focuses on storing ingested data efficiently. Types: Data Lakes (Raw data – S3, ADLS) Data Warehouses (Structured data – Snowflake, BigQuery) Lakehouse (Unified model – Databricks) 04 iabac.org

  6. STAGE 3 – DATA PROCESSING Raw data must be processed for usability. Approaches: Batch (Spark, Hadoop) Stream (Flink, Kafka Streams) Focus on scalability, resilience, and low latency. 05 iabac.org

  7. STAGE 4 – DATA TRANSFORMATION Cleaning, enriching, and structuring data for analytics. Techniques: ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Tools: dbt, Airflow, Azure Data Factory. 06 iabac.org

  8. STAGE 5 – DATA QUALITY & GOVERNANCE Ensures trust in data assets. Includes validation, deduplication, schema enforcement, and access control. Governance focuses on: Data Catalogs (e.g., Collibra, Alation) Compliance (GDPR, HIPAA) 07 iabac.org

  9. STAGE 6 – DATA DELIVERY & CONSUMPTION Data served to end users and systems (dashboards, ML models, APIs). Tools: Tableau, Power BI, Looker, SageMaker. Emphasis on speed, security, and self-service access. 08 iabac.org

  10. THANK YOU Visit: iabac.org

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