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LakehouseLab

Cloud-native lakehouse platform with medallion architecture. Transform raw data into analytics-ready insights with Delta Lake, PySpark, and enterprise-grade governance.

Explore ArchitectureView Features
ACID
Delta Lake
< 5s
Query Speed
99%+
Data Quality
99.9%
Uptime

Platform Features

Production-grade capabilities for modern data engineering

Medallion Architecture

Bronze, Silver, and Gold layers for progressive data refinement. Immutable raw data with curated analytics-ready datasets.

Delta Lake ACID

ACID transactions with time travel capabilities. Query historical data versions and ensure data consistency.

Incremental Processing

Watermark-based incremental loading with change data capture. Process only what changed, save compute costs.

Dimensional Modeling

Star schema design with fact and dimension tables. SCD Type 2 for tracking historical changes over time.

Data Quality Framework

Great Expectations for validation, data contracts, and automated anomaly detection. > 99% quality score.

CI/CD Pipeline

Automated testing, deployment, and environment promotion. GitHub Actions with comprehensive test coverage.

Cloud-Native

Multi-cloud ready for AWS, Azure, and Databricks. Object storage with serverless compute scaling.

Self-Healing Pipelines

Automatic retry logic, error handling, and data quality monitoring. Idempotent processing for reliability.

Enterprise Governance

RBAC, encryption at-rest and in-transit, audit logging, and data lineage tracking for compliance.

See Architecture Details

Medallion Architecture

Progressive data refinement from raw ingestion to analytics-ready datasets

Bronze

Raw Data

Immutable raw data from sources

  • •CSV/JSON files
  • •API responses
  • •Streaming events
  • •Schema validation

Silver

Curated

Cleansed and normalized data

  • •Data quality checks
  • •Deduplication
  • •Standardization
  • •Enrichment

Gold

Analytics

Business-ready dimensional models

  • •Star schema
  • •Fact tables
  • •Dimension tables
  • •KPIs & metrics

Consumption

BI & ML

Self-service analytics

  • •Dashboards
  • •Reports
  • •ML models
  • •Ad-hoc queries

Why Medallion Architecture?

Data Integrity

Preserve raw data immutability while enabling progressive transformation

Scalability

Process only changed data at each layer with incremental pipelines

Governance

Clear data lineage and quality checkpoints at every stage

Tech Stack

Modern, production-tested technologies for enterprise data engineering

Processing & Storage

Apache Spark

Distributed processing engine

Delta Lake

ACID transactions & time travel

PySpark

Python API for Spark

Parquet

Columnar storage format

Cloud Platforms

AWS

S3, EMR, Glue, Athena

Azure

ADLS Gen2, Synapse, Data Factory

Databricks

Unified data + AI platform

GCP

BigQuery, Dataproc, GCS

Data Quality & Testing

Great Expectations

Data validation framework

pytest

Python testing framework

dbt

Data transformation testing

Data Contracts

Schema validation

DevOps & Orchestration

GitHub Actions

CI/CD automation

Docker

Containerization

Airflow

Workflow orchestration

Terraform

Infrastructure as Code

Language Distribution

Python75%
SQL15%
TypeScript8%
Shell2%

Use Cases

Real-world applications powered by lakehouse architecture

Customer Segmentation

RFM analysis to identify high-value customers and create targeted marketing campaigns.

Key Metrics

→Customer Lifetime Value
→Retention Rate
→Churn Prediction

Revenue Analytics

Track revenue trends, forecast growth, and analyze performance by region and category.

Key Metrics

→GMV by Category
→Average Order Value
→Revenue Growth Rate

Product Performance

Identify best sellers, slow movers, and optimize inventory based on demand forecasting.

Key Metrics

→Units Sold
→Inventory Turnover
→Product Margin

Predictive Analytics

ML models for demand forecasting, price optimization, and personalized recommendations.

Key Metrics

→Forecast Accuracy
→Model Performance
→Prediction Confidence

Cohort Analysis

Track customer behavior over time, measure retention, and identify engagement patterns.

Key Metrics

→Cohort Retention
→User Lifetime
→Engagement Score

Real-Time Dashboards

Live monitoring of KPIs, operational metrics, and business health indicators.

Key Metrics

→Daily Active Users
→Order Volume
→System Uptime

Example: Customer Lifetime Value Query

SELECT 
  c.customer_id,
  c.customer_segment,
  COUNT(DISTINCT f.order_id) as total_orders,
  SUM(f.total_amount) as lifetime_value,
  AVG(f.total_amount) as avg_order_value,
  MAX(d.full_date) as last_order_date
FROM gold.fact_orders f
JOIN gold.dim_customer c ON f.customer_key = c.customer_key
JOIN gold.dim_date d ON f.date_key = d.date_key
WHERE c.is_current = TRUE
GROUP BY c.customer_id, c.customer_segment
ORDER BY lifetime_value DESC
LIMIT 100;

Query the Gold layer for instant analytics insights

Performance Metrics

Real-time monitoring of platform health and data quality

Data Processed

1.2 TB

+23% this month

Query Performance

< 5s

P95 latency

Data Quality Score

99.2%

+0.5% vs last week

Pipeline Success

99.8%

10,432 runs

Data Freshness

< 1 hour

Source to Gold

Test Coverage

87%

+12% this quarter

Layer Statistics

LayerTablesRecordsSize
Bronze Layer122.4M450 GB
Silver Layer242.1M380 GB
Gold Layer181.8M120 GB
Total546.3M950 GB

Data Freshness SLA

< 1 hour

From source to Gold layer

Uptime SLA

99.9%

Platform availability

Query SLA

< 5 seconds

P95 response time

Code Examples

Production-ready PySpark and SQL code for lakehouse pipelines

Incremental batch ingestion with watermark-based loading

Data Ingestion.pythonpython
1from pyspark.sql import SparkSession
2from delta.tables import DeltaTable
3

4def ingest_to_bronze(spark, source_path, target_path):
5    """Incremental ingestion to Bronze layer"""
6    
7    # Read with watermark for incremental load
8    df = spark.read.format("csv") \
9        .option("header", "true") \
10        .option("inferSchema", "true") \
11        .load(source_path)
12    
13    # Add metadata columns
14    df_with_metadata = df \
15        .withColumn("ingestion_timestamp", current_timestamp()) \
16        .withColumn("source_file", input_file_name())
17    
18    # Merge into Delta table (upsert)
19    if DeltaTable.isDeltaTable(spark, target_path):
20        delta_table = DeltaTable.forPath(spark, target_path)
21        delta_table.alias("target").merge(
22            df_with_metadata.alias("source"),
23            "target.id = source.id"
24        ).whenMatchedUpdateAll() \
25         .whenNotMatchedInsertAll() \
26         .execute()
27    else:
28        df_with_metadata.write.format("delta").save(target_path)
29    
30    return df_with_metadata.count()

Type-Safe

Full TypeScript support with type checking

Production-Ready

Error handling, logging, and retry logic

Tested

Unit tests with >80% code coverage

Get In Touch

Have a question about the architecture or want to discuss data engineering? Reach out!

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