Alliances & Collaborations

We use AWS Glue to build and manage scalable ETL workflows in the cloud. As a serverless data integration service, Glue automates schema discovery, job scheduling, and data cataloging—enabling seamless data preparation across diverse sources. It accelerates analytics and machine learning by streamlining the movement of data into AWS ecosystems.

We leverage Amazon Redshift to build high-performance, cloud-based data warehouses optimized for analytics at scale. With features like columnar storage, massively parallel processing (MPP), and seamless integration with the AWS ecosystem, Redshift enables fast query performance, real-time insights, and cost-effective scalability for enterprise workloads.

As an AWS Select Tier Services Partner, we leverage a comprehensive suite of cloud-native tools—including EC2, S3, RDS, Redshift, Glue, and SageMaker—to engineer scalable, secure, and cost-efficient solutions. Our accredited team specializes in legacy data warehouse migration, infrastructure optimization, and deployment of enterprise-grade analytics and ML environments.

Anthropic’s Claude models, including Claude 3.5 Sonnet, are designed for high-integrity, controllable AI performance. We leverage their advanced reasoning capabilities, support for large context windows (up to 200K tokens), and Constitutional AI framework to build safe, enterprise-ready applications across legal research, technical writing, strategic analysis, and code generation.

A cornerstone of our data orchestration strategy, Apache Airflow enables the automation and monitoring of complex workflows. Its DAG-based framework supports scalable, modular pipeline development with fine-grained control over task dependencies, execution timing, and failure handling—making it ideal for managing enterprise-grade ETL, analytics, and machine learning workflows.

Optimized for performance and scalability, Apache MXNet is a deep learning framework ideal for both symbolic and imperative programming. We leverage MXNet for building flexible, efficient neural networks—especially in distributed environments—enabling advanced computer vision, NLP, and time-series models with seamless deployment across GPUs, CPUs, and edge devices.

Built for large-scale data processing, Apache PySpark combines Python's ease with Spark’s distributed computing power. Ideal for handling big data workloads, it enables fast ETL, real-time analytics, and scalable machine learning—making it a core tool for enterprise-grade data engineering and advanced analytics in cloud or hybrid environments.

We work with Azure Data Factory, Microsoft’s powerful cloud-based data integration service, to design and automate ETL/ELT workflows at scale. ADF streamlines data movement across hybrid environments, reduces manual overhead through low-code orchestration, and empowers faster analytics by seamlessly preparing data from diverse sources for cloud warehouses and AI models.

We partner with DataChannel, a best-in-class no-code ETL and Reverse ETL platform with 150+ integrations, GenAI-powered analytics (Ask Neo), and fully automated syncs. It frees data teams from pipeline firefighting, enabling real-time insights, business-user autonomy, and seamless activation of data across CRMs, ads, warehouses, and business apps.

As official partners, we build unified data and AI platforms on Databricks using its Lakehouse architecture. We leverage Apache Spark, Delta Lake, and MLflow to enable scalable data processing and machine learning. With Unity Catalog, we ensure centralized governance, fine-grained access control, and end-to-end data lineage.

We leverage dbt Labs’ data transformation framework, dbt, to enable modular, SQL-based modeling and version-controlled analytics workflows within cloud data warehouses. Designed for the “T” in ELT, dbt empowers data teams to build scalable, testable, and maintainable pipelines—bringing software engineering rigor to modern data transformation.

We work extensively with Docker to streamline AI and ML workflows. By containerizing models and their dependencies, Docker ensures consistent, scalable deployment across environments—from development to production. It plays a key role in enabling reproducible experiments, efficient resource utilization, and seamless integration within modern data and ML pipelines.

Our team uses Domo to unify data, dashboards, and workflows into a single cloud-based platform. With its real-time data pipelines, app-building tools, and enterprise scalability, Domo enables organizations to operationalize insights faster—connecting strategy to execution across departments and leadership levels.

Designed for high-speed analytics at scale, Google BigQuery is our platform of choice for serverless data warehousing. We leverage its distributed architecture, columnar storage, and native support for BQML to enable real-time insights, advanced analytics, and seamless integration across GCP services and diverse data sources.

We utilize GCP’s enterprise-grade infrastructure and advanced services—such as BigQuery, Dataflow, Vertex AI, and Looker—to engineer scalable, high-performance data ecosystems. Built on the industry’s cleanest cloud, our solutions enable seamless data integration, real-time analytics, and AI/ML-driven transformation tailored to complex business environments.