There is no single best data platform for every organisation in India. The right choice follows from your workloads, your existing cloud and skills, where your data must reside, and how the platform will be paid for and governed. In broad terms: Databricks suits engineering-heavy, streaming and machine-learning workloads; Snowflake suits SQL-centric analytics and data sharing; BigQuery suits teams already on Google Cloud who want a serverless warehouse. Many organisations run more than one.
What are the main options?
- Databricks is a lakehouse platform built around Apache Spark, Delta Lake and Unity Catalog. It covers data engineering, streaming, SQL analytics, machine learning and generative AI on one governed copy of data.
- Snowflake is a cloud data warehouse and data platform with separate storage and compute, strong SQL experience, and built-in data sharing between organisations.
- Google BigQuery is a serverless analytics warehouse on Google Cloud, with tight integration to the Google ecosystem.
- Amazon Redshift and Microsoft's Azure analytics services (Synapse, Fabric) are the native options when your estate is committed to AWS or Azure.
How should you decide?
1. Start with the workloads
| If most of your work is… | Lean towards |
|---|---|
| Large-scale data engineering, streaming, ML and GenAI on shared data | Lakehouse (e.g. Databricks) |
| SQL analytics, BI and governed data sharing | Warehouse (e.g. Snowflake, BigQuery) |
| Mixed: pipelines, BI and ML | Lakehouse, or warehouse plus an ML platform |
2. Respect where you already are
Existing cloud commitments, security reviews, networking and team skills are usually worth more than marginal feature differences. A platform your team can operate well beats a theoretically better one they cannot.
3. Check data residency and compliance early
For Indian organisations this includes:
- Region availability. Confirm that the services you need run in an Indian cloud region, per service, at the time of purchase. Availability differs by product and changes over time.
- The Digital Personal Data Protection Act, 2023. It places obligations on how personal data is collected, used, stored and protected. Design for purpose limitation, access control, retention and deletion from the start, and take legal advice on your obligations.
- Sector rules. Financial services, insurance and healthcare often carry additional requirements on data location, audit and outsourcing.
4. Model the cost honestly
Look beyond list prices.
- Compute and storage consumption under your query and pipeline patterns
- Idle capacity, concurrency and peak load behaviour
- Data movement between clouds and regions
- The engineering time to operate and govern the platform
- How costs will be allocated and capped (tagging, budgets, auto-suspend)
Run a proof of value on a representative workload before committing.
5. Plan governance from day one
Whichever platform you choose, decide up front how you will handle access control, data classification, lineage, quality checks and cost ownership. These are much harder to retrofit.
Is a lakehouse better than a data warehouse?
Not universally. A lakehouse keeps data in open table formats and lets engineering, BI, machine learning and generative AI use the same governed copy. That is valuable when you have varied workloads. A warehouse can be simpler and faster to value when the need is mainly SQL analytics. Increasingly the two approaches overlap, so the practical question is which one fits your team and workloads.
What should you ask a data platform partner?
- Which workloads have you migrated or built on this platform, and what went wrong?
- How will you approach data quality, governance and cost control from the first sprint?
- How do you handle data residency and compliance requirements in our sector?
- What will our team be able to run without you after handover?
- How will you measure success in business terms?
A sensible way to start
- List the five to ten workloads that matter most, with data volumes and freshness needs.
- Shortlist two platforms against those workloads, residency and skills.
- Run a time-boxed proof of value on one real workload.
- Decide on evidence: performance, cost, operability and governance.
Marquedata is a Kolkata-based data and AI engineering company that designs and builds cloud data platforms on Databricks, Snowflake and the major clouds for organisations in India and worldwide. See Data & Cloud or talk to us.