Technical Profile

Datapand builds data platforms, custom machine learning pipelines, and cloud infrastructure. The tech stack supports real-time stream processing, industrial IoT analytics, MLOps, and system observability.

Tooling & Methodology

Different data products demand different technologies depending on scale, architecture, and performance goals. The tools best suited for a problem can change over time, and performance often depends on continuous tuning. Multiple technical paths can frequently solve the same challenge effectively.

The technologies listed below represent the core stacks we have hands-on experience implementing in production projects. As tools evolve, continuous learning and adaptation remain an integral part of how we engineer solutions.

Technical Capabilities

Data Engineering & Streaming

Building ETL/ELT pipelines for batch and streaming data, alongside distributed lakehouse architectures.

• Technologies: PySpark, Apache Kafka, Apache NiFi, Apache Airflow, Azure Data Factory, AWS Glue, Delta Lake, Databricks
• Querying: SQL, Kusto Query Language (KQL)

AI, Machine Learning & Time-Series

End-to-end MLOps lifecycles applied to industrial IoT sensor data, computer vision, and natural language processing.

• Frameworks: TensorFlow, PyTorch, MLflow, Kubeflow
• Focus Areas: Time-Series Analytics, Industrial IoT, Computer Vision, NLP, MLOps

Cloud Infrastructure & DevOps

Multi-cloud resource provisioning using Infrastructure-as-Code (IaC), containerization, and GitOps pipelines.

• Cloud Environments: Azure (AKS, Data Lake), AWS (S3, Kinesis, EC2), Google Cloud Platform (GKE)
• Orchestration & CI/CD: Docker, Kubernetes, Terraform, Helm, ArgoCD, Jenkins, Azure DevOps, GitHub Actions

Database Engineering

Database management across relational, time-series, document, and search systems.

• Relational: PostgreSQL, Oracle SQL
• Time-Series: TimescaleDB
• NoSQL & Search: Elasticsearch, MongoDB

Observability & Operations

System monitoring, logging, and automated alerting to maintain operational SLAs.

• Monitoring Stack: ELK Stack (Elasticsearch, Logstash, Kibana), Grafana, Prometheus
• Protocols & Integration: REST APIs, Pub/Sub, MQTT, Linux/Unix, Bash, PowerShell

Core Programming & Query Languages