Explain your project and the technologies used so far.

My project involves building a scalable, cloud-native data platform designed to ingest, process, and analyze diverse data sources, supporting both real-time analytics and batch reporting. The core stack leverages a combination of open-source and managed cloud services to balance performance, cost, and maintainability.

Technology Stack and Rationale

Our architecture follows a Medallion Lakehouse pattern (Bronze, Silver, Gold layers).
* Ingestion: For real-time event streams, we use Kafka due to its high throughput, fault tolerance, and distributed log capabilities (managing offsets for consumer groups). For scheduled batch data pulls from external APIs or databases, Airflow orchestrates Python-based ingestion scripts.
* Storage: AWS S3 serves as our cost-effective data lake, storing raw (Bronze) and processed (Silver) data, primarily in Delta Lake format on top of Parquet files for ACID transactions and schema evolution. For the highly structured, aggregated Gold layer, we use Snowflake (or BigQuery), leveraging its columnar storage, micro-partitions, and auto-clustering for analytical query performance.
* Processing: Apache Spark (PySpark) is our primary engine for both batch and structured streaming transformations. It handles large-scale ETL/ELT operations, leveraging distributed processing, partitions, and shuffle operations for efficiency.
* Orchestration: Airflow (or Dagster) manages complex data pipelines, defining dependencies, retries, and monitoring for batch jobs.
* BI & Analytics: Tableau (or Looker) connects to our Gold layer in Snowflake for business intelligence dashboards and reporting.

Example Flow and Key Trade-offs

A typical real-time data flow involves events streaming into Kafka topics. A Spark Structured Streaming application consumes these events, performs initial schema enforcement and quality checks, and writes them as raw (Bronze) data into S3 using Delta Lake. Subsequent Spark jobs or dbt models transform this Bronze data into a cleaned, conformed Silver layer, often denormalizing or joining datasets. Finally, dbt (data build tool) orchestrates SQL transformations to build the aggregated, business-ready Gold layer tables in Snowflake, defining lineage and tests.

-- dbt model for a Gold layer aggregated table
SELECT
    DATE_TRUNC('day', order_timestamp) AS order_date,
    product_category,
    SUM(order_value) AS total_sales,
    COUNT(DISTINCT customer_id) AS unique_customers
FROM {{ ref('silver_orders') }} so
JOIN {{ ref('silver_products') }} sp ON so.product_id = sp.product_id
WHERE order_timestamp >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1, 2
ORDER BY 1 DESC, 2;

Our infrastructure is managed via Terraform (Infrastructure as Code), and all code (Spark, dbt, Airflow DAGs) is version-controlled with Git and deployed through CI/CD pipelines. This setup balances scalability (Kafka, Spark, Snowflake), cost-efficiency (S3, cloud elasticity), and team expertise in Python/SQL.

In the interview, also mention a specific challenge you faced and how you solved it using these technologies.