We don't have vendor partnerships that influence what we recommend. We choose the right tool for your specific problem — and we've worked with enough stacks to know which tool that is before we start your engagement.
Every technology recommendation we make goes through four questions before it goes in a proposal.
The most powerful tool is useless if your team can't operate it. We factor in your existing skills before recommending anything new.
Some platforms look cheap at 1TB and crippling at 100TB. We model your growth trajectory and choose accordingly — not just for today.
We audit your existing stack before recommending anything new. Clean integrations beat best-in-class isolation every single time.
We define the specific problem first, then find the technology. Not the other way around — always.
BigQuery, Dataflow, Pub/Sub, Vertex AI. Our default for analytics-heavy workloads with complex query patterns and serverless pipelines.
S3, Glue, Kinesis, SageMaker, Redshift, EMR. Broadest ecosystem — our go-to when the client is AWS-native or needs maximum flexibility.
Azure Data Factory, Synapse Analytics, Azure ML. Natural choice for enterprises running Microsoft infrastructure or requiring deep Active Directory integration.
The modern cloud data warehouse. Exceptional separation of storage and compute, cross-cloud data sharing, and near-zero maintenance for SQL-first teams.
Lakehouse architecture combining data lakes and warehouses. Our preference for teams doing heavy ML alongside their analytics workloads.
Industry standard for Python-based workflow orchestration. Maximum flexibility and native integration with virtually every data tool in the ecosystem.
Microsoft's managed ETL and orchestration service. Drag-and-drop for non-technical users, code support for engineers, deep Azure integration.
Serverless workflow orchestration deeply integrated with the AWS ecosystem. Excellent for event-driven architectures and serverless data pipelines.
Modern Python-native orchestration frameworks with excellent observability, data contracts, and developer experience for teams who want more than Airflow.
The backbone of real-time data architectures. Distributed event streaming at any scale, with decades of production battle-testing behind it.
Stateful stream processing with true exactly-once semantics. Our choice when streaming logic is complex and correctness is non-negotiable.
Managed streaming fully integrated with the AWS ecosystem. Lower operational overhead than self-managed Kafka when you're already AWS-native.
Change Data Capture for streaming database changes in real-time. Essential for keeping downstream systems in sync without impacting source databases.
The transformation layer that's become the analytics engineering standard. Version-controlled, tested, documented SQL models with built-in lineage tracking.
Distributed processing for large-scale transformation. Our go-to when data volume exceeds what SQL-on-warehouse can handle efficiently.
The lingua franca of data engineering. Every custom transformation, enrichment, and processing logic that doesn't fit standard tooling.
In-process SQL analytics engine. Blazingly fast for local development and small-to-medium analytical queries directly on files — no server needed.
Microsoft's BI tool with deep Office 365 integration. Right choice for enterprises with Microsoft infrastructure and non-technical self-service users.
Industry-leading visualisation flexibility. When charts need to be beautiful and exploration needs to be genuinely deep, Tableau is rarely beaten.
Open-source observability and monitoring. Our standard for operational dashboards, real-time infrastructure metrics, and DevOps-facing views.
Lightweight open-source BI with an excellent no-code query builder. Our recommendation for teams that want self-service without Tableau's complexity.
Google's semantic-layer-first BI platform. Excellent when you need a single, governed metrics layer shared across many dashboards and teams.
Foundation frameworks for custom model development. We choose based on model type, team familiarity, and production deployment target.
Our standard for ML lifecycle management — experiment tracking, model registry, and deployment. Works across all cloud environments without lock-in.
Orchestration frameworks for building agentic AI applications with LLMs. Our toolkit for RAG systems, AI agents, and automated data workflows.
Managed ML platforms that reduce infrastructure overhead. We use these when you want managed model hosting without running your own ML infrastructure.
Workhorses of applied ML. Still the right choice for tabular data, classification, regression, and forecasting — especially when interpretability matters.
Enterprise data governance platforms. We implement data catalogues, lineage tracking, and stewardship workflows for compliance-driven organisations.
Data quality framework that runs validation checks directly in your pipeline. Catches bad data before it reaches production dashboards or AI models.
PII masking, column-level security, and dynamic data masking on Snowflake and BigQuery. Essential for GDPR, DPDP Act, and financial data regulations.
We use dbt's data contract features to enforce schema agreements between producers and consumers — preventing breaking changes from propagating silently.
Combines the flexibility of a data lake with the structure of a data warehouse. Single storage layer (S3/GCS), with a warehouse query engine (Snowflake/Databricks) on top. Our default for companies starting fresh or re-architecting from scratch.
Parallel batch and streaming paths serving different latency requirements. Batch for historical accuracy, streaming for real-time operational decisions. Complex but powerful — ideal for financial services and logistics.
Centralised feature store serving both training and online inference. Eliminates the training-serving skew that kills most ML projects and dramatically accelerates model iteration speed.
Book a free architecture consultation. We'll review what you have and tell you honestly what we'd change — and what we'd leave exactly as is.
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