Insights
Product, design, AI, and engineering perspectives from our team.
Data Infrastructure Consulting: Building the AI Foundation
Most growing Australian organisations don't have a data problem — they have a data infrastructure problem. This post covers the modern data stack (Snowflake, BigQuery, Databricks), ELT vs ETL trade-offs, data contract patterns, and Australian data sovereignty considerations, with a practical look at consolidating five warehouses into one.
Snowflake vs BigQuery for Australian Mid-Market Data Platforms
Snowflake and BigQuery are the two dominant cloud data warehouses for mid-market teams in Australia, but the right choice depends on your cloud estate, data-residency obligations, cost profile, and AI roadmap. This guide cuts through the noise to help Australian data and engineering leaders make a defensible decision.
Apache Airflow vs Managed Orchestration for AI and Data Pipelines
Choose self-managed Apache Airflow when your team has strong DevOps capability and needs fine-grained infrastructure control. Choose a managed alternative when your priority is pipeline output over platform operations. This article walks through the core trade-offs, including data sovereignty considerations for Australian regulated industries.
Real-Time Data Pipelines: When You Need Them and When You Don't
Real-time data pipelines process data with minimal latency, but most mid-market businesses don't actually need them. Understanding when batch processing suffices versus when streaming is truly required can save significant complexity and cost.
dbt for Mid-Market: Data Transformation Without Enterprise Costs
dbt brings enterprise-grade data transformation capabilities to mid-market Australian companies without enterprise costs. Learn how to implement modern data pipelines using SQL and software engineering best practices.
Data Quality for AI: Why Garbage In Still Means Garbage Out
Poor data quality is the fastest way to turn a promising AI project into an expensive failure. Learn how to assess if your data is AI-ready and implement a practical framework for data quality: profiling, validation, monitoring, and remediation.
Building Your First Data Pipeline: A Guide for Business Leaders
A data pipeline is an automated system that moves data from various sources, transforms it, and delivers it where your business needs it. Understanding data pipelines is crucial for business leaders because they form the foundation for reporting, analytics, and AI initiatives.
Data Warehouse vs Data Lake vs Lakehouse: Which One Do You Need?
Choosing the right data architecture — warehouse, lake, or lakehouse — can make or break your AI initiatives. Each approach serves different needs and impacts your ability to build AI-powered features.
Data Infrastructure for AI: Why Most AI Projects Fail
85% of AI projects fail before models are built due to poor data infrastructure. Learn why data pipelines, warehousing, and governance determine AI success — and how to build incrementally for real outcomes.