Tools & Technologies
The platforms and frameworks we use to build modern data infrastructure — battle-tested in production, not just on slides.
Storage & Warehousing
ClickHouse
Our go-to OLAP database for high-performance analytical workloads. We design ClickHouse clusters for real-time analytics, log processing, and large-scale reporting — handling billions of rows with sub-second query times.
Greenplum
Massively parallel processing (MPP) database based on PostgreSQL. We deploy Greenplum for large-scale on-premise and hybrid analytical workloads — segment configuration, resource group management, and migration from legacy MPP systems.
Snowflake
Cloud-native data warehouse with elastic compute and zero-maintenance storage. We build Snowflake-based analytics platforms with proper warehouse sizing, access controls, cost governance, and integration with dbt for transformation layers.
BigQuery
Google Cloud’s serverless data warehouse. We use BigQuery for projects in the GCP ecosystem — setting up datasets, optimizing partitioning and clustering, managing costs with slot reservations, and connecting to dbt and BI tools.
Redshift
AWS’s columnar data warehouse. We’ve built and optimized Redshift clusters including distribution key selection, sort key strategies, WLM configuration, and migrations to Redshift Serverless.
PostgreSQL
The backbone of many data architectures we build. We use PostgreSQL as application databases, metadata stores, and lightweight analytics backends. Deep expertise in performance tuning, extensions (TimescaleDB, pgvector, PostGIS), replication, and partitioning.
MySQL
Widely adopted relational database used across many client environments. We work with MySQL for data extraction, CDC pipelines, migration projects, and performance tuning.
Databricks
Unified lakehouse platform combining data engineering, analytics, and ML on Apache Spark. We use Databricks for large-scale data processing, Delta Lake implementations, Unity Catalog governance, and ML workflows.
Transformation
dbt
The standard for SQL-based data transformation. We build dbt projects for modular, tested, and documented analytics — from initial setup to production CI/CD pipelines. Core expertise across dbt Core and dbt Cloud.
Apache Spark
Distributed data processing engine for large-scale ETL and analytics. We use Spark (PySpark) for heavy transformation workloads that exceed single-node capabilities — batch processing, data lake transformations, and feature engineering for ML pipelines.
Orchestration
Apache Airflow
The leading open-source orchestrator for data pipelines. We build and manage Airflow deployments for scheduling, monitoring, and orchestrating complex data workflows — from simple ETL jobs to multi-system data platform operations.
DMP.AF
Our own data management platform for metadata, lineage, and data catalog. DMP.AF provides a unified view of your data landscape — connecting pipelines, warehouses, and BI tools into a single observable system.
Ingestion
Airbyte
Open-source data integration platform with 300+ connectors. We deploy Airbyte for ELT pipelines — extracting data from SaaS tools, databases, and APIs into warehouses. Experienced with self-hosted deployments, custom connectors, and orchestration via Airflow.
dlt (data load tool)
Lightweight Python library for building data pipelines as code. We use dlt for custom ingestion scenarios where flexibility matters more than a UI — API extractions, incremental loading, schema evolution, and Python-native pipeline development.
BI & Visualization
Redash
Lightweight open-source BI tool for SQL-first teams. We deploy Redash for quick, no-frills analytical environments — connecting directly to warehouses and databases, creating dashboards, and setting up alerts.
Tableau
Enterprise-grade visualization and analytics platform. We build Tableau dashboards for executive reporting, operational monitoring, and client-facing analytics. Experienced with Server/Cloud administration and embedding.
Streaming
Apache Kafka
Distributed event streaming platform for real-time data pipelines. We design Kafka architectures for event-driven systems, CDC streams, log aggregation, and real-time analytics.
Apache Flink
Stream processing framework for stateful computations over data streams. We use Flink for real-time aggregation, event-time processing, and complex event detection — typically paired with Kafka.
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