Amazon Web Services
Where most of our production infrastructure and data platforms run.
AWS carries the majority of the production infrastructure we operate, both application estates and data platforms. The breadth of the service catalogue and the depth of the talent pool in India are the practical reasons — when a client eventually brings operations in-house, AWS skills are the easiest to hire for in Kolkata.
On the application side that means ECS or EKS for containers, RDS for Postgres, ElastiCache, CloudFront and Route 53, with everything defined in Terraform and deployed through a pipeline. On the data side it means S3 as the storage layer with Iceberg tables, Glue for cataloguing and crawling, EMR for Spark, Athena for ad-hoc SQL, Redshift where a warehouse is required, and DynamoDB where a genuinely key-value access pattern exists.
The two things we spend most client time on are cost and security posture, because both degrade silently. An estate that was well-designed three years ago has accumulated idle resources, over-permissive keys and snapshots nobody deletes.
Where it fits
What Amazon Web Services genuinely gives us
Service breadth
Almost every architecture we need has a managed option, which reduces the amount of infrastructure a client must operate themselves.
Deep talent availability
Kolkata has genuine AWS depth, which matters for handover and for the client hiring their own team later.
Mature data services
S3, Glue, EMR, Athena and Iceberg together form a complete lakehouse without proprietary storage formats.
Predictable governance tooling
IAM, Organizations, Control Tower and CloudTrail give the control and audit surface that larger clients require.
A lakehouse on S3 without proprietary lock-in
Our standard AWS data architecture keeps data in the customer's own S3 buckets in Apache Iceberg format, catalogued in Glue. EMR runs Spark for heavy transformation, Athena serves ad-hoc SQL, and Redshift Spectrum or Snowflake external tables can query the same files if a warehouse engine is wanted.
The point is that the data never becomes hostage to a query engine. Changing engines later is a decision rather than a migration project, which is worth a great deal in a three-year negotiation.
Cost control that is specific rather than exhortative
Every resource is tagged to an owner, an environment and a purpose, so the monthly report attributes spend to something actionable. Non-production environments shut down outside working hours automatically. Savings plans cover the stable baseline only, never the variable peak. Snapshots and unattached volumes are lifecycle-managed rather than accumulating.
The median reduction across estates we review is 38%, and the single largest saving we found was a development environment running continuously for two years that four people used on weekday afternoons.
How Amazon Web Services projects usually go wrong
These are the failure modes we look for first when we are called in to rescue somebody else's implementation.
Amazon Web Services questions we get asked
Including where we would recommend something other than Amazon Web Services. Call +91 70033 91355 and you will get the same answer from an engineer.
AWS has the broadest catalogue and the deepest Indian talent pool. Azure is the pragmatic choice if you are committed to Microsoft 365 and Active Directory, and enterprise agreements often price well. Google Cloud is strong for data and analytics and has excellent networking. Honestly, for most workloads the difference between the three matters less than the difference between good and bad architecture on any of them.
Yes, and it is most of our cloud work. We start with a review covering security posture, cost, backup verification and deployment process, then bring the estate under Terraform progressively rather than rebuilding. An undocumented estate is typically fully codified and instrumented within four to eight weeks with no service disruption.
Yes. We deploy into the Mumbai or Hyderabad regions with policy constraints preventing resource creation elsewhere, and configure logging and backup to stay in-region. For clients whose data cannot go to a public cloud at all, we build the equivalent architecture on-premise with MinIO, Spark on Kubernetes and Iceberg.
Services built on Amazon Web Services
Technologies we pair it with
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.
Direct line
+91 70033 91355Mon–Sat · 9:30 AM – 7:30 PM IST · Sealdah, Kolkata