Our Tech Stack

We work with modern, proven technologies to build robust and scalable data infrastructure. Our stack focuses on open-source tools and cloud-native solutions that give you flexibility, performance, and cost control.

DuckDB

Data Warehousing

An in-process SQL OLAP database management system. DuckDB provides fast analytical queries and is perfect for local analytics, embedded analytics, and data processing workflows.

Why We Use It

  • Lightning Fast: Columnar storage and vectorized query execution
  • Zero Configuration: No server needed, runs in-process
  • SQL Standard: Full SQL support with advanced analytics functions
  • Portable: Single file database, easy to backup and move
  • Python Integration: Seamless integration with pandas and arrow

Use Cases

  • Local data warehouse for fast analytics
  • Embedded analytics in applications
  • Data processing pipelines
  • Development and testing environments

dbt

Data Transformation

Data build tool (dbt) enables analytics engineers to transform data in their warehouse by simply writing SQL select statements. Dbt handles turning these into tables and views.

Why We Use It

  • SQL-Based: Write transformations in SQL, no complex coding needed
  • Version Control: Git-based workflow for data transformations
  • Testing: Built-in data quality testing framework
  • Documentation: Auto-generated documentation from your code
  • Modularity: Reusable macros and models

Use Cases

  • Data warehouse transformations
  • Data quality testing
  • Analytics engineering workflows
  • Documentation of data models

Python

Programming

Python is our primary programming language for data engineering, analysis, and automation. Its rich ecosystem of data libraries makes it ideal for building data platforms.

Why We Use It

  • Rich Ecosystem: pandas, numpy, polars for data processing
  • Data Engineering: Airflow, Prefect for orchestration
  • Flexibility: General-purpose language for any task
  • Community: Massive community and extensive libraries
  • Integration: Works with all modern data tools

Use Cases

  • ETL/ELT pipeline development
  • Data analysis and processing
  • Automation scripts
  • API development
  • Custom data tools

Apache Airflow

Orchestration

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. We use it to orchestrate complex data pipelines and ensure reliable data processing.

Why We Use It

  • Python-Based: Define workflows as code using Python
  • Scheduling: Robust scheduling with cron-like expressions
  • Monitoring: Rich UI for monitoring and troubleshooting
  • Extensible: Hundreds of operators for different systems
  • Scalability: Can scale from single machine to clusters

Use Cases

  • ETL/ELT pipeline orchestration
  • Data warehouse refresh scheduling
  • Multi-step data processing workflows
  • Task dependency management

BigQuery

Data Warehousing

Google BigQuery is a fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. Perfect for analytics workloads that require high performance.

Why We Use It

  • Serverless: No infrastructure to manage
  • Performance: Analyze terabytes in seconds
  • Standard SQL: Familiar SQL syntax
  • Cost-Effective: Pay only for queries you run
  • Integration: Native GCP integration

Use Cases

  • Large-scale data analytics
  • Real-time analytics on streaming data
  • Machine learning with BigQuery ML
  • Data warehouse for cloud-native applications

Looker Studio

Visualization

Looker Studio (formerly Google Data Studio) is a free tool that turns your data into informative, easy-to-read, easy-to-share, and fully customizable dashboards and reports.

Why We Use It

  • Free: No cost for unlimited reports and viewers
  • Easy to Use: Drag-and-drop interface
  • Connectivity: Connect to many data sources
  • Sharing: Easy sharing and collaboration
  • Customizable: Fully customizable visualizations

Use Cases

  • Marketing analytics dashboards
  • Business performance reporting
  • SEO and website analytics
  • Custom client dashboards

PostgreSQL

Databases

PostgreSQL is a powerful, open-source object-relational database system with a strong reputation for reliability, feature robustness, and performance.

Why We Use It

  • Open Source: Free and community-driven
  • ACID Compliant: Reliable transactions
  • Feature-Rich: Advanced SQL features and extensions
  • Extensible: PostGIS for geospatial, pgvector for AI
  • Performance: Excellent performance for most workloads

Use Cases

  • Application databases
  • Transactional systems
  • Hybrid OLTP/OLAP workloads
  • Geospatial data with PostGIS

Tableau

Visualization

Tableau is a visual analytics platform transforming the way we use data to solve problems. It enables people to see and understand data through interactive visualizations.

Why We Use It

  • Powerful Visualizations: Industry-leading viz capabilities
  • Interactive: Highly interactive dashboards
  • Performance: Handles large datasets efficiently
  • Self-Service: Empowers business users
  • Enterprise Ready: Robust security and governance

Use Cases

  • Executive dashboards
  • Business intelligence reporting
  • Data exploration and discovery
  • Embedded analytics