1010data

Name: 1010data
Brand: 1010data
Rating: 4 (65 reviews)

We empower companies to monitor shifts in consumer and market demand and rapidly respond with highly-targeted strategies

Est 2000|Valuation $500M|Raised $35M|10 Ref Customers

LinkedIn 13K|Web traffic 4K|FTEs 203|HQ New York, NY

MarketsSelf-Service Analytics

01Competitive Landscape

Narrative analysis: where alternatives excel

1010data vs Snowflake - Snowflake is commonly selected when buyers prioritize standard SQL-based warehousing, a broad hiring pool, and cloud-native elasticity. - 1010data is selected where teams require an interactive spreadsheet-like experience for very large tables and accept platform-specific workflows. - A partner-authored migration story framed Snowflake as faster and cheaper for a particular workload. 1010data vs Databricks - Databricks is typically selected for data engineering and ML/AI pipelines; it provides Spark-based workloads, notebooks, and model lifecycle features. - 1010data may be simpler for analyst-driven exploration, while Databricks offers deeper data science and ML tooling. 1010data vs Teradata - Teradata targets large-scale enterprise analytics with consumption-based cloud packaging and more explicit entry points. - 1010data’s differentiation is primarily UX and workflow (TRS/Macro) rather than a pure enterprise warehouse narrative. Market share and adoption trends - Some technographic sources estimate limited market share for 1010data within broad Big Data Analytics categories; these estimates are directional and methodologies may be opaque.

	1010data	Snowflake	Databricks
Starting Price	Not publicly listed (quote-based)	Usage-based; editions and separate compute/storage billing	Usage-based; pay-as-you-go with commitment discounts
Primary Use Case	Spreadsheet-like exploration of very large enterprise datasets; retail/CPG analytics workflows; time-series focused analytics	Cloud data warehouse + data sharing; scalable SQL analytics; broad ecosystem	Data engineering + ML/AI workloads on lakehouse; open formats + data science tooling
Key Strength	TRS user experience for exploration; Macro Language for expressive transformations; built-in connectors	Separation of compute/storage; governance features; large partner ecosystem	Unified analytics and ML workflows; strong notebooks and model lifecycle support
Key Weakness	Proprietary language/workflow dependency; limited price transparency; not primarily oriented to advanced statistical workflows per some user feedback	Cost management can be complex; credit model requires governance	Operational complexity for some use cases; pricing depends heavily on workload

01Competitive Landscape

Narrative analysis: where alternatives excel

1010data vs Snowflake

Snowflake is commonly selected when buyers prioritize standard SQL-based warehousing, a broad hiring pool, and cloud-native elasticity.
1010data is selected where teams require an interactive spreadsheet-like experience for very large tables and accept platform-specific workflows.
A partner-authored migration story framed Snowflake as faster and cheaper for a particular workload.

1010data vs Databricks

Databricks is typically selected for data engineering and ML/AI pipelines; it provides Spark-based workloads, notebooks, and model lifecycle features.
1010data may be simpler for analyst-driven exploration, while Databricks offers deeper data science and ML tooling.

1010data vs Teradata

Teradata targets large-scale enterprise analytics with consumption-based cloud packaging and more explicit entry points.
1010data’s differentiation is primarily UX and workflow (TRS/Macro) rather than a pure enterprise warehouse narrative.

Market share and adoption trends

Some technographic sources estimate limited market share for 1010data within broad Big Data Analytics categories; these estimates are directional and methodologies may be opaque.

	1010data	Snowflake	Databricks
Starting Price	Not publicly listed (quote-based)	Usage-based; editions and separate compute/storage billing	Usage-based; pay-as-you-go with commitment discounts
Primary Use Case	Spreadsheet-like exploration of very large enterprise datasets; retail/CPG analytics workflows; time-series focused analytics	Cloud data warehouse + data sharing; scalable SQL analytics; broad ecosystem	Data engineering + ML/AI workloads on lakehouse; open formats + data science tooling
Key Strength	TRS user experience for exploration; Macro Language for expressive transformations; built-in connectors	Separation of compute/storage; governance features; large partner ecosystem	Unified analytics and ML workflows; strong notebooks and model lifecycle support
Key Weakness	Proprietary language/workflow dependency; limited price transparency; not primarily oriented to advanced statistical workflows per some user feedback	Cost management can be complex; credit model requires governance	Operational complexity for some use cases; pricing depends heavily on workload