Cloud · Since 2019 · Analytics estates

Google Cloud

Strongest where the problem is analytics — BigQuery, Dataflow, Composer and Dataproc.

Our position

Google Cloud is where we build when analytics is the centre of gravity. BigQuery remains the most operationally simple serverless warehouse available — there is no cluster to size, no warehouse to suspend, and concurrency is not something you plan for. For teams without dedicated platform engineering, that is a substantial advantage.

Around it, Dataflow handles streaming and batch processing on Apache Beam, Cloud Composer provides managed Airflow, Dataproc runs Spark and Hadoop workloads that need a cluster, Bigtable serves very high-throughput key-value access, and Looker Studio covers reporting where a free tool is adequate.

The trade-off is the pricing model. BigQuery charges by bytes scanned on the on-demand model, which is beautifully simple and unforgiving of careless queries. A single unpartitioned table scanned by a dashboard refreshing every fifteen minutes can produce a genuinely alarming monthly bill.

Where it fits

Analytical warehouse for commerce, marketing and operational reporting

Streaming and batch processing through Dataflow on Apache Beam

Managed Airflow orchestration via Cloud Composer

Spark and Hadoop workloads on Dataproc, including ephemeral clusters

High-throughput key-value serving on Bigtable

Why we choose it

What Google Cloud genuinely gives us

Serverless with no capacity planning

No clusters to size or suspend, and concurrency is handled by the platform rather than by an administrator.

Excellent for large scans

Aggregations over very large tables complete in seconds without any tuning effort.

Streaming inserts built in

Real-time data lands and is immediately queryable without a separate ingestion tier.

Straightforward governance

IAM, dataset-level access and column-level policy tags are simple to reason about.

Controlling BigQuery cost

On-demand pricing charges for bytes scanned, so cost control is entirely about scanning less. Partition every large table on the column queries filter by — usually a date — and cluster on the next most common filter. Never select star in a scheduled query. Materialise aggregations that dashboards refresh repeatedly rather than recomputing the base table each time.

Above a certain steady volume, capacity-based pricing through editions becomes cheaper and, more usefully, makes the bill predictable. We model both against your actual query history rather than guessing.

Partitioning and clustering on real filter columns, verified with dry-run byte estimates.

Scheduled queries reviewed for scan volume — this is where surprise bills originate.

Materialised views for repeatedly computed aggregations.

Custom cost controls and per-project quotas to cap runaway queries.

Capacity pricing modelled against actual history before switching.

Dataflow for streaming without cluster management

Dataflow runs Apache Beam pipelines with autoscaling and no cluster to operate, which makes it a genuinely low-overhead option for streaming ingestion and transformation. Windowing, watermarking and late-data handling are first-class in the Beam model rather than bolted on.

Where a client already has significant Spark investment we use Dataproc instead, often with ephemeral clusters created per job and destroyed afterwards, which is both cheaper and cleaner than a long-running cluster.

Honest warnings

How Google Cloud projects usually go wrong

These are the failure modes we look for first when we are called in to rescue somebody else's implementation.

Unpartitioned large tablesEvery query scans everything. The single biggest cost mistake on the platform and the easiest to fix.

SELECT * in scheduled queriesScans every column including the large ones nobody reads. Costs multiply by refresh frequency.

Streaming inserts where batch would doStreaming ingestion is priced differently and is unnecessary when a few minutes of latency is acceptable.

No project-level cost controlsOne badly written ad-hoc query can consume a month of budget. Quotas make that impossible.

Straight answers

Google Cloud questions we get asked

Including where we would recommend something other than Google Cloud. Call +91 70033 91355 and you will get the same answer from an engineer.

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BigQuery if you want fully serverless with no capacity management at all, if you are already on Google Cloud, or if your workload is spiky and unpredictable. Snowflake if you want more control over compute isolation between workloads, prefer predictable warehouse-based pricing, or need its data sharing features. Both are excellent. We model both against your actual query patterns during assessment rather than recommending from preference.

Looker Studio is genuinely capable for straightforward dashboards on BigQuery and it is free, which makes it a reasonable starting point. It becomes limiting with complex modelling, row-level security or many users needing governed self-service. At that point Looker or Power BI on a proper semantic layer is worth the cost. We usually start clients on Looker Studio and move only when a specific limitation is actually hit.

Services built on Google Cloud

Data EngineeringPetabyte-scale pipelines, lakehouses and warehouses — built by engineers who have run them in production for cement, steel, solar and commerce clients.Business Intelligence & AnalyticsDashboards people actually open, built on a modelled semantic layer so two departments never bring different numbers to the same meeting.ETL & Data PipelinesBatch and real-time pipelines that run every day without drama — orchestrated, monitored, idempotent, and honest about failure.Cloud, Hosting & DevOpsAWS, Azure and GCP infrastructure as code, domains, CDN, CI/CD and observability — built so a deployment is boring and an outage is short.

Technologies we pair it with

Apache AirflowOrchestration for hundreds of pipelines with owners, SLAs and honest failure handling.PythonThe language our data, scraping and AI work is written in.Apache SparkDistributed processing for the workloads that genuinely need it — batch and streaming.SnowflakeWarehouse of choice where SQL analytics dominates and operational simplicity is valued.

Next step

Tell us what is slowing your business down.

A 30-minute call with a senior engineer — not a salesperson. You leave with an architecture sketch and an honest cost range, whether or not you hire us.

Book a discovery call