Increment
Using our insights and recommendation suite, controlling and optimizing costs becomes a walk in the park. Our models calculate cost to the lowest granularity. Understand what a single query costs, or a table. Aggregate data workloads to understand their collective cost over time. Understand which actions will yield which results. Keep your team focused and tackle only the tech debt worth tackling. Understand how to configure your data workloads in a cost-optimal way. Drive efficient savings without having to re-write queries or drop tables. Educate your team members through query recommendations. Balance effort and impact and ensure your work has an optimal ROI. Teams save up to 30% of their costs with increments.
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Capital One Slingshot
Capital One Slingshot is a cloud data platform optimization and management solution that helps organizations simplify, optimize, and maximize their use of Snowflake and Databricks by providing enhanced visibility into financial and compute spend, continuous monitoring, dynamic rightsizing, and AI-driven recommendations to reduce waste and inefficiencies while improving performance. It delivers granular dashboards and reports tracking cost, usage, and performance trends, allocates costs to business units with custom tagging, and offers proactive alerts for credit consumption and cost spikes. Slingshot’s recommendation engine analyzes workloads to right-size warehouses, suggests schedule adjustments, and highlights inefficient queries with its Query Advisor to improve SQL performance. It supports automated optimization for Databricks jobs using machine learning models and enables federated management and governance with customizable workflows and controls.
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nao
nao is an AI-powered data IDE designed specifically for data teams, combining a code editor with native integration to your data warehouse so you can write, test, and maintain data-centric code with full context. It supports warehouses such as Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Once connected, nao replaces a traditional data-warehouse console by offering schema-aware SQL auto-completion, data previews, SQL worksheets, and the ability to switch easily between multiple warehouses. The core of nao is its AI agent, which has full awareness of your actual data schema, tables, columns, metadata, and your codebase or data-stack context. It can generate SQL queries or full data-transformation models (e.g., for dbt workflows), refactor code, add or update documentation, run data-quality checks and data-diff tests, and even surface insights or run exploratory analytics, all while respecting data structure and quality constraints.
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Formal
Formal is a protocol-aware reverse proxy that secures access to databases, APIs, infrastructure, and AI tools by enforcing least privilege at the wire-protocol level. Deployed as a single stateless binary in a VPC through Terraform, Kubernetes, or Docker, it sits between identities and resources without application changes, SDKs, or agents. Formal parses more than 15 protocols, including PostgreSQL, MySQL, MongoDB, Snowflake, SSH, Kubernetes, HTTP, MCP, S3, Redis, RDP, BigQuery, ClickHouse, and DynamoDB, allowing query-level decisions instead of generic network filtering. Policies can authenticate and authorize users, mask or filter fields, rewrite requests, block actions, require MFA, quarantine sessions, suspend access, or support impersonation across session, request, and response stages. Teams can secure AI agents and MCP servers by stripping PII before it reaches a model, blocking unauthorized tool calls, and auditing every action.
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