Data Analytics & BI.

Snowflake when workloads are bursty, multi-cloud portability matters and you want strong governance features out of the box. BigQuery when GCP is already your platform or analytics-on-events at scale is the workload. Redshift when you're deep in AWS, costs need tight control via reserved instances and your data team is SQL-native. We benchmark on your actual data and queries before recommending - vendor demos don't reflect real workloads.

Yes. dbt is our default transformation tool because it brings software engineering discipline (version control, tests, documentation, CI) to analytics. We pair it with a semantic layer (dbt Semantic Layer, Cube, or LookML when you're on Looker) so business metrics are defined once and consumed consistently across dashboards, notebooks and reverse-ETL into operational tools. Without a semantic layer, every dashboard recomputes 'revenue' slightly differently and trust evaporates.

Buy. Tableau and Power BI for executive and finance audiences, Looker for embedded analytics with strong governance, Metabase or Superset for cost-conscious internal teams. We have never seen a custom BI tool pay back its TCO against these options. We build custom only when you're embedding analytics into a product you sell to your own customers and the off-the-shelf embed experience genuinely doesn't fit.

We classify data at ingestion (public, internal, confidential, restricted), enforce column-level masking and row-level security in the warehouse, track lineage end-to-end via dbt or OpenLineage, and run automated PII scanners against new tables. For NDPC, NDPA, POPIA and GDPR compliance we maintain a record-of-processing register and data-subject-request runbooks. Governance lives in code and dashboards, not Word documents that go stale.

A focused implementation - source ingestion, dbt models for two or three priority domains, governance baseline and three flagship dashboards - typically takes 10 to 14 weeks. Full enterprise rollouts run 6 to 12 months because the constraint is rarely the technology but rather agreeing on metric definitions across finance, ops and sales. We sequence the work so value lands every 2 weeks, not in one big-bang launch.

Yes, when the business genuinely needs sub-minute latency - fraud detection, operational dashboards for call centers, IoT telemetry. We use Kafka or Kinesis plus Flink, Materialize or ClickHouse depending on the access pattern. Most 'real-time' requests are actually 'fresher than yesterday' and are better served by 5-minute micro-batches in dbt - much cheaper and easier to operate. We push back on real-time when it isn't justified.

If you have under USD 10M revenue, a small team and most analysis happens in three spreadsheets, a well-modeled Postgres replica plus Metabase will serve you for years. We have advised early-stage clients against committing to Snowflake and dbt prematurely - the platform tax is real and the payoff curve doesn't start until you have multiple teams asking different questions of the same data.