Technology
Technology we work with
We recommend what fits your estate across AWS, Microsoft Azure and Google Cloud. Our stance is open formats, cloud-agnostic design and governance by default.
Our stance
Three principles behind every stack
- 01
Open formats
Delta Lake and Apache Iceberg keep your data portable across engines and clouds.
- 02
Cloud-agnostic
We design for AWS, Microsoft Azure and Google Cloud, and use native services where they fit best.
- 03
Governed by default
Catalog, lineage, access control and audit are part of the platform from the first release.
Tool map
Tools we commonly work with
Cloud
The three major clouds we design and build on, shaped around the estate you already run.
- AWSAmazon Web Services: broad infrastructure, data and AI services.
- Microsoft AzureMicrosoft's cloud, closely tied to Microsoft 365, Fabric and Entra ID.
- Google CloudGoogle's cloud, known for BigQuery, Dataplex and Vertex AI.
- Cloud object storageAmazon S3, Azure Data Lake Storage and Google Cloud Storage as lake foundations.
Data platforms
Warehouses, lakes and lakehouses where governed data is stored, modeled and shared.
- DatabricksLakehouse platform for data engineering, SQL analytics and machine learning.
- SnowflakeCloud data platform for warehousing, sharing and analytics.
- Microsoft FabricUnified analytics platform with OneLake, pipelines and Power BI.
- Google BigQueryServerless data warehouse with built-in machine learning.
- Amazon RedshiftCloud data warehouse in the AWS ecosystem.
- Azure Synapse AnalyticsAnalytics service combining warehousing and big data on Azure.
- Delta LakeOpen table format that adds reliability to data lake storage.
- Apache IcebergOpen table format for large analytic tables across engines.
Data engineering
Pipelines that move, transform and orchestrate data in batch, as change data capture and as streams.
- Apache SparkDistributed engine for large-scale batch and stream processing.
- dbtSQL-based transformation, testing and documentation for analytics models.
- Apache AirflowWorkflow orchestration for scheduled and dependent data jobs.
- Apache KafkaDistributed event streaming platform for real-time data.
- Azure Data FactoryManaged integration service for ELT/ETL pipelines on Azure.
- AWS GlueServerless data integration and catalog service on AWS.
- Google DataflowManaged batch and streaming processing based on Apache Beam.
- FivetranManaged connectors that load source data into a warehouse.
- DebeziumOpen source change data capture for operational databases.
- Apache FlinkStateful stream processing for low-latency event pipelines.
Analytics and BI
Dashboards, self-service analytics and a shared semantic layer so teams use the same definitions.
- Power BIMicrosoft's BI and dashboarding platform with semantic models.
- TableauVisual analytics and dashboards for exploration and reporting.
- LookerGoverned BI built on a modeled semantic layer.
- Amazon QuickSightServerless BI and embedded analytics on AWS.
- Apache SupersetOpen source data exploration and dashboards.
- CubeHeadless semantic layer that serves consistent metrics to any tool.
AI and GenAI
Machine learning, generative AI and agents, built with evaluation, guardrails and human review.
- Azure OpenAI ServiceLarge language models delivered through Azure with enterprise controls.
- Amazon BedrockManaged access to foundation models, agents and guardrails on AWS.
- Google Vertex AIGoogle Cloud platform for machine learning and generative AI.
- Amazon SageMakerManaged service to build, train and deploy machine learning models.
- Azure Machine LearningAzure service for training, deploying and monitoring models.
- Hugging FaceOpen models, datasets and libraries for machine learning.
- LangChainFramework for building retrieval and agent workflows.
- MLflowExperiment tracking and model registry for MLOps.
- Vector databasesEmbedding stores such as pgvector, Azure AI Search and OpenSearch for retrieval.
Governance and security
Catalogs, lineage, access control and key management that make data and AI auditable.
- Microsoft PurviewData catalog, classification and governance across Microsoft estates.
- Unity CatalogUnified governance for data and AI assets on Databricks.
- CollibraData governance and catalog platform for policies and stewardship.
- AWS Lake FormationFine-grained access control for data lakes on AWS.
- Google DataplexData management and governance across Google Cloud data.
- Azure Key VaultManaged storage for secrets, keys and certificates on Azure.
- AWS KMSManaged encryption keys for services and applications on AWS.
- Microsoft Entra IDIdentity and access management for people and workloads.
- OpenLineageOpen standard for collecting data lineage across tools.
- Great ExpectationsOpen source framework for data quality checks.
DevOps and IaC
Infrastructure as code, CI/CD and observability so platforms are repeatable, reviewed and resilient.
- TerraformInfrastructure as code that works across all three clouds.
- BicepAzure-native language for declarative infrastructure.
- AWS CloudFormationAWS-native templates for repeatable infrastructure.
- GitHub ActionsCI/CD workflows that live next to the code.
- Azure DevOpsBoards, repositories and pipelines for delivery teams.
- KubernetesContainer orchestration for portable, scalable workloads.
- DockerContainer packaging for consistent builds and deployments.
- OpenTelemetryOpen standard for traces, metrics and logs.
These tools are examples of what we commonly work with, listed as text only. We are vendor-neutral and recommend what fits your estate. Product names belong to their owners, and their appearance here does not imply a partnership, endorsement or certification.
Choosing tools
When we choose what
| Layer | Tools we commonly use | When we choose it |
|---|---|---|
| Ingest | Apache Kafka, Fivetran, Azure Data Factory, AWS Glue, Google Dataflow, Debezium | Managed connectors for speed, streaming for low-latency events, and change data capture when sources cannot be queried directly. |
| Store | Delta Lake, Apache Iceberg, Amazon S3, Azure Data Lake Storage, Google Cloud Storage | Open table formats when portability and several engines matter, on the object storage of your chosen cloud. |
| Model | dbt, Apache Spark, Great Expectations | dbt for tested, documented SQL models, and Spark for very large or complex processing. |
| Warehouse | Databricks, Snowflake, Microsoft Fabric, Google BigQuery, Amazon Redshift, Azure Synapse Analytics | The one that fits your cloud, skills and licenses, not a fixed preference. |
| Serve | Power BI, Tableau, Looker, Amazon QuickSight, Cube | Whatever your business users already use, with metrics defined once in a semantic layer. |
| AI | Amazon Bedrock, Azure OpenAI Service, Google Vertex AI, Amazon SageMaker, Azure Machine Learning, LangChain | Managed model services for governance and data residency, and open frameworks to limit lock-in. |
| Govern | Microsoft Purview, Unity Catalog, Collibra, AWS Lake Formation, Google Dataplex | The catalog native to your platform first, and a dedicated catalog when governance spans several platforms. |
| Deliver | Terraform, Bicep, AWS CloudFormation, GitHub Actions, Azure DevOps, Kubernetes | Terraform for multi-cloud estates, and native infrastructure as code when you run on one cloud. |
Security and data handling
Designed to protect your data
- Your data stays in your own cloud accounts and under your access controls wherever practical.
- Least-privilege, identity-based access with separation of duties.
- Encryption in transit and at rest, with keys held in your key management service.
- Personal and sensitive data classified, masked or tokenized where needed.
- Audit logs, lineage and model records kept to support review.
- Region and residency choices built into the architecture. We aim to support alignment with privacy laws such as PIPEDA, but we do not provide legal advice.
In more detail
Go deeper by topic
Data platforms and open table formats
We build lakehouses and warehouses with raw, refined and curated layers, and store tables in open formats such as Delta Lake and Apache Iceberg. That keeps your data readable by several engines and reduces the cost of changing platforms later. We pick Databricks, Snowflake, Microsoft Fabric, Google BigQuery, Amazon Redshift or Azure Synapse Analytics based on your cloud, your skills and your workloads.
AI, generative AI and agent tooling
We use managed model services such as Amazon Bedrock, Azure OpenAI Service and Google Vertex AI, along with open frameworks such as LangChain and Hugging Face. Retrieval-augmented generation, evaluation, guardrails and human-in-the-loop review are designed in from the start, and models are tracked in a registry with monitoring after release.
Identity, keys and data protection
Access is tied to your identity provider, such as Microsoft Entra ID, with roles scoped to the minimum needed. Encryption keys live in AWS KMS, Azure Key Vault or Google Cloud KMS under your control, and sensitive fields are classified in the catalog so policies can follow the data.
Infrastructure as code, CI/CD and observability
Landing zones, networks and data services are defined as code with Terraform, Bicep or AWS CloudFormation, deployed through reviewed pipelines in GitHub Actions or Azure DevOps, and monitored with logs, metrics and traces. Cost controls and backup are part of the baseline, so the platform stays resilient and affordable.
Example services by layer on each cloud
| Layer | Capability | AWS | Microsoft Azure | Google Cloud |
|---|---|---|---|---|
| Sources | Operational databases | Amazon RDS and Aurora | Azure SQL Database | Cloud SQL and AlloyDB |
| Files and documents | Amazon S3 | Azure Blob Storage | Google Cloud Storage | |
| SaaS and APIs | Amazon AppFlow | Azure Logic Apps | Google Application Integration | |
| Ingestion and streaming | Streaming | Amazon Kinesis or MSK | Azure Event Hubs | Google Pub/Sub |
| Batch and ELT | AWS Glue | Azure Data Factory | Google Dataflow | |
| Change data capture | AWS Database Migration Service | Azure Data Factory | Google Datastream | |
| Lakehouse and warehouse | Storage | Amazon S3 | Azure Data Lake Storage | Google Cloud Storage |
| Warehouse | Amazon Redshift | Azure Synapse or Fabric | Google BigQuery | |
| Open table formats | Delta Lake and Apache Iceberg run on all three clouds. | |||
| Governance and catalog | Catalog and access | AWS Lake Formation | Microsoft Purview | Google Dataplex |
| Identity and keys | AWS IAM and KMS | Microsoft Entra ID and Key Vault | Cloud IAM and Cloud KMS | |
| Lineage and quality | OpenLineage, dbt tests and Great Expectations work across all three clouds. | |||
| Analytics and semantic layer | BI and dashboards | Amazon QuickSight | Power BI | Looker |
| Semantic layer | dbt metrics or Cube sit in front of any warehouse. | |||
| ML and GenAI | Machine learning | Amazon SageMaker | Azure Machine Learning | Vertex AI |
| Generative AI | Amazon Bedrock | Azure OpenAI Service | Vertex AI | |
| Vector search for RAG | Amazon OpenSearch Service | Azure AI Search | Vertex AI Vector Search | |
| Guardrails and evaluation | Amazon Bedrock Guardrails | Azure AI Content Safety | Vertex AI evaluation | |
| Applications and agents | Agent frameworks | Amazon Bedrock Agents | Azure AI Foundry | Vertex AI Agent Builder |
| App hosting | AWS Lambda or ECS | Azure Container Apps | Google Cloud Run | |
FAQ
Frequently asked questions
Which clouds and tools do you work with?
We work on AWS, Microsoft Azure and Google Cloud, and with tools such as Databricks, Snowflake, Microsoft Fabric, Apache Spark, dbt, Power BI and Terraform. We recommend what fits your estate rather than one vendor.
Are you a partner or certified reseller of these vendors?
The tools on this page are examples of what we commonly work with. We do not claim partnership, certification or preferred-vendor status, and we stay vendor-neutral in our recommendations.
Can you work with the tools we already own?
Yes. We start from the platforms, skills and licenses you already have, and we only recommend new tools when they solve a problem the current ones cannot.
Let's talk about your data and AI priorities
Tell us what you are trying to solve. We aim to reply within one business day.