Seemore Data

Product Introduction

1. Definition: Seemore Data is an AI-powered Snowflake FinOps and Data Observability platform designed to automate data infrastructure cost management and performance tuning. It functions as an intelligent layer above cloud data warehouses, specifically the Snowflake Data Cloud, providing real-time visibility, automated resource right-sizing, and proactive anomaly detection to ensure data engineering efficiency.

2. Core Value Proposition: The platform exists to eliminate "Snowflake bill shock" and maximize the ROI of data assets. By bridging the gap between data utilization and infrastructure expenditure, Seemore Data allows organizations to scale their data mesh and pipeline operations without linear cost increases. Its primary objective is to achieve at least a 20% reduction in Snowflake credits through autonomous warehouse optimization, usage-based pipeline analysis, and granular cost attribution.

Main Features

1. Autonomous Warehouse Optimization (SmartPulse): This feature utilizes machine learning to continuously monitor warehouse workloads and adjust compute resources in real-time. Unlike native static auto-suspend settings, SmartPulse employs vertical scaling (right-sizing instance types) and horizontal scaling (concurrency management) based on predictive workload patterns. It automatically detects idle time and misconfigurations, enforcing aggressive shutdown policies that prevent unnecessary credit consumption without sacrificing query performance or meeting SLAs.

2. Deep Lineage & Column-Level Visibility: Seemore Data provides a multi-dimensional, end-to-end view of the entire data stack, from ingestion sources to BI dashboards. This technical mapping goes beyond simple table-level views to provide column-level lineage. It tracks the specific cost, frequency, and duration of every data asset and node. By integrating with the Snowflake Query Profile, it identifies "data waste"—columns or tables that are processed and stored but never consumed by end-users or downstream applications.

3. Proactive AI Agent & Root Cause Anomaly Detection: The platform features an "AI Lineage Sherpa" that monitors for cost spikes, rogue queries, and performance degradation. When an anomaly is detected, the AI agent performs an automated root-cause analysis, tracing the issue back to the specific job, user, or upstream code change. It provides actionable remediation guides and can push real-time alerts to Slack or Microsoft Teams, allowing data teams to intervene before inefficiencies impact the monthly budget.

4. AI Auto-Clustering Audit & Management: For organizations handling massive datasets, Seemore Data provides an efficiency audit of existing clustered tables. It uses AI to determine if Snowflake’s automatic clustering is providing a performance benefit proportional to its cost. It identifies tables that require re-clustering based on changing query patterns and table growth, ensuring that credit spend on background maintenance tasks is optimized for maximum query acceleration.

Problems Solved

1. Unpredictable Snowflake Expenditure: Data leaders often face "black box" billing where it is difficult to attribute costs to specific departments or projects. Seemore Data solves this through Domain Budget Management, which allows users to set clear budgets and KPIs per warehouse, project, or domain, enabling precise FinOps chargeback and showback models.

2. Manual Troubleshooting and Maintenance Overload: Data engineers often spend hours manually investigating slow queries or broken pipelines. Seemore Data automates asset discovery and impact analysis, reducing the time required for root-cause investigation from days to minutes through its interactive AI assistant and indexed query history.

3. Target Audience:

• Chief Data Officers (CDOs): Focused on communicating the ROI of data products and ensuring architectural scalability.
• Data Engineering Managers: Looking to act as a "team multiplier" by automating tedious optimization tasks.
• FinOps & Cloud Financial Analysts: Seeking granular cost attribution and budget enforcement within Snowflake environments.
• Data Architects: Aiming to design more efficient pipelines and monitor data mesh implementations.

4. Use Cases:

• Cloud Migration & Modernization: Ensuring that newly migrated workloads in Snowflake are optimized from day one.
• Data Mesh Governance: Attributing costs and performance metrics across decentralized data domains.
• SLA Compliance: Identifying and fixing slow-running jobs that threaten data delivery deadlines for BI consumers.
• Cost Reduction Initiatives: Executing a rapid "efficiency audit" to identify immediate 20%+ savings on Snowflake credits.

Unique Advantages

1. Performance-Based Guarantee: Seemore Data offers a unique market-leading guarantee: if the platform cannot identify and facilitate a 20% reduction in Snowflake costs, the company pays the customer $5,000. This demonstrates a high degree of confidence in their AI-driven optimization algorithms.

2. Usage-Based vs. Query-Based Optimization: While many tools look strictly at query performance, Seemore Data analyzes "actual usage." This means it identifies data that is being processed but not actually "consumed" by any user or dashboard, allowing for the elimination of redundant data pipelines that other tools might simply try to "speed up."

3. Non-Disruptive Onboarding: The platform integrates securely with Snowflake via standard roles and permissions without requiring code changes, proxy installations, or architecture overhauls. This allow for "read-only" initial audits that deliver actionable insights within minutes of connection.

Frequently Asked Questions (FAQ)

1. How does Seemore Data achieve Snowflake cost reduction without impacting performance? Seemore Data uses SmartPulse technology to right-size compute resources. It identifies warehouses that are over-provisioned for their specific workloads (e.g., using a Large warehouse for a task that fits in a Small) and optimizes auto-suspend and multi-cluster settings. By reducing "spillage" to disk and managing queue delays, it often improves performance while simultaneously lowering costs.

2. What is column-level lineage and why is it important for FinOps? Column-level lineage tracks the flow of specific data points from source to destination. For FinOps, this is critical because it identifies "dead weight" in the data warehouse. If a pipeline is processing 100 columns but only 5 are ever used in a BI tool, Seemore Data highlights this discrepancy, allowing engineers to prune the pipeline and save on processing and storage costs.

3. Is Seemore Data SOC 2 and HIPAA compliant? Yes, Seemore Data is built for enterprise-grade security and maintains compliance with SOC 2, HIPAA, GDPR, and PCI standards. The platform connects to Snowflake using secure, limited-privilege roles, ensuring that sensitive data remains protected while metadata is analyzed for optimization.