Google Cloud
Strongest where the problem is analytics — BigQuery, Dataflow, Composer and Dataproc.
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
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.
Keeping a BigQuery bill predictable
Because BigQuery charges for bytes scanned, cost is a property of how people query rather than of how much data you store, and that catches teams out. Storage is comparatively cheap; a single analyst exploring a wide table without filters can spend more in an afternoon than the storage costs in a month.
The first control is structural. Partition every large table on the column people filter by — usually a date — so a query for last week does not read three years. Cluster on the columns used in grouping and joining. Both are decided at table creation and are tedious to change later, which is why we insist on modelling before ingestion rather than after.
The second is behavioural, and it needs enforcing rather than recommending. Custom quotas at project and user level cap the damage from a mistake. Dashboards should read materialised aggregates rather than recomputing over raw tables on every refresh, which is the single most common source of surprise cost we find. Preview and dry-run features tell an analyst what a query will scan before it runs, and building the habit of checking is worth more than any amount of policy documentation.
BigQuery is the reason most people arrive at Google Cloud
In our experience the overwhelming majority of Google Cloud engagements begin with BigQuery, and often the rest of the estate stays wherever it already is — which is a perfectly reasonable architecture that vendors rarely encourage. BigQuery is genuinely strong for analytics: serverless, no cluster to size or forget to switch off, fast on large scans, and priced by bytes processed rather than by time.
That pricing model is the thing to understand before adoption rather than after the first invoice. Because you pay for bytes scanned, a dashboard doing SELECT * against a wide unpartitioned table every fifteen minutes is expensive in a way that has no equivalent in a fixed-cluster warehouse. The controls are straightforward when applied deliberately: partition on the column people filter by, cluster on the columns they group by, forbid SELECT * in production queries, materialise the aggregates that dashboards read instead of recomputing them, and set custom quotas so a mistake is capped rather than unlimited.
The other reason clients choose Google Cloud is the analytics and marketing data ecosystem — GA4 exports, Ads data and the surrounding tooling land in BigQuery natively, and stitching that data to first-party business data is one of the more valuable pieces of work available to a company that sells online.
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.
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.
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.
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.
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
Technologies we pair it with
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