Pipelines, Databricks & PySpark
Data Engineering
AI is only as good as the data behind it. We build the scalable data infrastructure that powers your analytics and ML systems — from cloud data lakes to real-time streaming pipelines, designed for reliability and cost efficiency.
Challenges We Solve
Sound Familiar?
- Data siloed across disconnected systems with no unified view
- Fragile ETL pipelines that break with every upstream schema change
- Analytics teams waiting days for data that should be available in minutes
- No data lineage or quality monitoring causing silent corruption downstream
- Spiraling cloud data costs from unoptimized storage and compute
Our Approach
How We Help
Cloud Data Lake & Lakehouse
Azure Data Lake + Databricks Delta Lake architecture for unified storage and compute across batch and streaming workloads.
ETL / ELT Pipeline Development
Robust, schema-evolution-tolerant pipelines using dbt for transformations, Azure Data Factory for orchestration, and PySpark for scale.
Real-Time Streaming Pipelines
Event-driven data ingestion using Azure Event Hubs and Spark Structured Streaming for operational analytics and ML feature serving.
Data Quality & Governance
Great Expectations data validation, lineage tracking with Azure Purview, and automated data quality dashboards.
Tech Stack
Technologies We Use
How We Work
Delivery Process
Data Source Discovery
Catalogue all data sources, understand update frequencies, volumes, and downstream consumer requirements.
Architecture Design
Design the medallion architecture (bronze/silver/gold) with partitioning strategy, retention policies, and access patterns.
Pipeline Development
Build ingestion, transformation, and serving layers with schema enforcement, error handling, and dead-letter queues.
Data Quality Framework
Implement automated data quality checks at each layer with alerting for anomalies and SLA breach detection.
Orchestration & Scheduling
Set up Azure Data Factory or Databricks Workflows for dependency management, SLA monitoring, and failure recovery.
Optimization & Handoff
Tune Spark jobs for cost and performance, document lineage, and train your team on operations and extension.
What You Get
Deliverables
Every engagement has a defined scope and concrete outputs. No vague “consulting reports” — you get production-ready artifacts.
- Production data pipelines (ADF + Databricks + dbt)
- Medallion architecture implementation (bronze/silver/gold)
- Data quality framework with automated validation
- Pipeline monitoring dashboards and SLA alerting
- Data lineage documentation and Purview catalog
- Runbook and on-call guide for pipeline operations
Why StarkLogik
What Makes Us Different
ML-Ready Data Architecture
We design data platforms for AI workloads from the start — feature store patterns, point-in-time correct joins, and training/serving skew elimination.
Cost-Optimized Databricks
We've reduced Databricks spend by 40–60% for clients through cluster right-sizing, autoscaling policies, and photon acceleration. Data infrastructure shouldn't cost more than the value it generates.
Schema Evolution Built In
We build pipelines that handle upstream schema changes gracefully — not brittle ETL that requires manual intervention every time a source system changes.
FAQs
Common Questions
Get Started
Ready to Get Started with Data Engineering?
Book a free 30-minute call with our engineering team to discuss your use case.