Services

We build modern data foundations that eliminate silos and enable cross-functional analysis. Whether you are migrating from a legacy on-premise system or optimizing a cloud-native architecture, we ensure your platform is built on industry best practices.

• Cloud Infrastructure: Expert deployment on AWS, GCP, and Azure.
• Infrastructure as Code (IaC): Using Terraform and CloudFormation to ensure your environment is version-controlled and reproducible.
• Storage Architecture: Designing Data Lakes (S3/GCS), Lakehouses (Databricks), and modern Warehouses (Snowflake/BigQuery).
• Containerization: Utilizing Docker and Kubernetes for portable, scalable microservices.

Data is only valuable if it’s where it needs to be, when it needs to be there. We specialize in building resilient, automated pipelines that handle everything from simple batch processing to high-throughput real-time streaming.

• Orchestration with Apache Airflow: We design complex DAGs with robust error handling, retries, and alerting to ensure your pipelines never “silent fail.”
• Custom Python Development: Leveraging the full power of the Python ecosystem—Pandas for data cleaning, NumPy for high-performance transformations, and PySpark for big data processing.
• Stream Processing: Implementation of Kafka or Kinesis for sub-second data availability.
• Data Quality & Validation: Built-in testing to ensure the data reaching your analysts is clean, consistent, and trusted.

This is our core specialty. We help organizations unlock the power of Elasticsearch to gain deep visibility into their systems and their data.

• Full-Stack Observability: Centralizing logs, metrics, and traces (APM) to reduce Mean Time to Resolution (MTTR).
• Security & SIEM: Architecting Elasticsearch clusters for threat detection and security information management.
• Enterprise Search: Building lightning-fast, relevant search experiences for large-scale unstructured datasets.
• Cluster Tuning & Maintenance: Optimizing shard strategies, indexing rates, and query performance for mission-critical clusters.

We help bridge the gap between “notebook” data science and production-scale engineering. We provide the plumbing so your data scientists can focus on modeling, not data cleaning.

• Feature Store Engineering: Building the pipelines that feed your ML models.
• Library Integration: Optimizing workflows that utilize SciPy, Scikit-learn, and TensorFlow.
• Model Deployment: Helping wrap models in scalable APIs or batch inference pipelines.

The Peace of Mind: Continuous optimization and expert oversight for your data ecosystem.

Modern data stacks—especially the Elastic Stack—are not “set and forget.” They require proactive tuning, shard management, and version upgrades to stay performant. Our retainer services provide you with a fractional Data Platform Engineer to ensure your infrastructure remains healthy, secure, and cost-effective without the overhead of a full-time hire.

• Proactive Elasticsearch Management: We monitor cluster health, manage indices, and tune query performance to prevent latency before it impacts your users.
• Airflow Pipeline Monitoring: We provide “watchdog” services for your mission-critical DAGs, ensuring that data delivery remains consistent and resolving failures before they cascade.
• Security & Version Upgrades: We handle the critical task of keeping your stack updated, applying security patches, and managing version migrations (e.g., Elastic 7.x to 8.x) with zero downtime.
• Monthly Performance Audits: Every month, we review your cloud spend and system resource allocation to ensure you aren’t overpaying for underutilized infrastructure.
• Priority Architectural Support: Retainer clients get a dedicated block of hours each month for ad-hoc consulting—whether you’re planning a new feature or need an emergency deep-dive into a production issue.

The Benefit: You get the expertise of a senior data engineer on-tap, ensuring your platform is always “production-ready” while your internal team stays focused on core product development.