# Which big data processing platforms do teams actually keep using past the first quarter of adoption?

Looking for input from G2 reviewers specifically on long-term retention — the big data processing and distribution platforms where the team's usage deepened over time rather than plateauing after the initial deployment, and where platform investment compounded rather than becoming shelfware or a political commitment that masked declining actual use.

The platforms with the strongest long-term adoption evidence:

Databricks: The platform becomes so integrated into daily workflow that the team handles booking and revenue metrics, provider performance dashboards, and monthly trend reporting from the same environment.
Snowflake: Reliable features and a user-friendly interface that sustains use across teams who did not originally select the platform themselves. The compute-storage separation model means teams can scale specific workloads without disrupting others, a structural property that sustains adoption as team size grows.
Google Cloud BigQuery: Shareable, saved queries are the feature that embeds BigQuery into daily workflow, team members build on each other's saved queries rather than starting from scratch each time, which creates a compounding library of analytical work that makes the platform stickier over time. The serverless model removes the cluster management friction that can drive teams away from Spark-based platforms after the initial enthusiasm fades.
Kyvos Semantic Layer: For teams whose long-term adoption challenge is that query performance degrades as concurrent user counts grow, Kyvos's sub-second query performance at high concurrency addresses the specific failure mode that causes teams to fall back to pre-aggregated exports and spreadsheets.
Posit Team: For R and Python data science teams, Posit Workbench, Connect, and Package Manager address the full workflow from development through publication and package management.

Would value input from data engineering leads who can describe how their team's Databricks or Snowflake usage changed between months 1–3 and months 12–18. Did usage expand into new workloads (ML, streaming, governance) or consolidate into fewer but more reliable pipelines? And was there a feature or capability that the team discovered after the initial deployment that meaningfully changed how much they used the platform?

##### Post Metadata - Posted at: 22 days ago - Author title: Marketing Executive - Net upvotes: 1 ## Comments ### Comment 1 Honestly the ones that stick are usually the ones with the least operational drag, because that's what kills adoption early. Google Cloud BigQuery comes up a lot for this, it's serverless so there's no cluster babysitting, and teams tend to stay because it just runs and scales without much care and feeding. Microsoft SQL Server is sticky for a different reason, it's so entrenched and familiar that teams keep building on what they already know. Teradata holds on in big enterprises where it's woven into everything. To be real, the two names people most often "keep using" long term, Databricks and Snowflake, sit in adjacent lakehouse and warehouse categories on G2 rather than this one, so worth looking there too. What's driving the churn risk for you, ops burden or skills gaps? That predicts what'll survive quarter two. ##### Comment Metadata - Posted at: 21 days ago ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: over 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: over 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: over 13 years ago - Comments: 4