Big Data Analysis with Databricks

Problem Statement

Organizations generate terabytes of data daily, but raw data is unusable without transformation, cleaning, and aggregation. Traditional single-machine processing (Pandas, SQL) breaks down beyond a few million rows. The challenge: build a scalable pipeline that transforms raw data into business insights efficiently and reliably.

Technical Approach

Architecture

• Apache Spark: Distributed computing engine processing data across multiple worker nodes. Used DataFrames API for type-safe transformations — filtering, joining, aggregating, windowing — on datasets too large for single-machine memory.
• Delta Lake: ACID-compliant storage layer on top of data lake (cloud object storage). Enabled time travel (querying historical data versions), schema enforcement, and upserts — critical for production pipelines where data quality is non-negotiable.
• Databricks Notebooks: Interactive development environment mixing SQL, Python, and Markdown. Enabled rapid iteration and collaborative review with stakeholders.

Optimization Techniques

Applied Delta Lake Z-ordering on high-cardinality join keys to co-locate related data — reducing shuffle during joins by 40%. Used partition pruning on date columns to skip irrelevant data. Cached intermediate DataFrames for iterative analysis. These optimizations combined to reduce query times by 60% compared to naive Spark SQL.

Pipeline Design

Built a three-stage ETL pipeline: (1) Ingestion — auto-loader incrementally ingesting new files from cloud storage, (2) Transformation — Spark jobs cleaning, normalizing, and enriching raw data with reference tables, (3) Serving — aggregated tables registered as SQL views for BI tools and dashboards. The pipeline processed 5M+ records in under 10 minutes, running on an autoscaling cluster.

Key Results

• Reduced query times by 60% using Delta Lake caching, Z-ordering, and partition pruning
• Built automated ETL pipeline processing 5M+ records in under 10 minutes
• Achieved ACID compliance on data lake — enabled safe concurrent reads/writes and rollback capabilities
• Delivered interactive dashboards directly in Databricks for business stakeholders

Tech Stack