Stop full-table scans on lakehouses with spatial + time indexing

Problem

Large Delta Lake Spark queries often trigger full scans and high cloud bills. Multidimensional spatial + timestamp indexing prunes files up-front, cutting scanned data, query time, and compute cost dramatically.

Solution

Stop full-table scans on lakehouses with spatial + time indexing

Combine an index metadata moat (telemetry across customer lakes) with ML-assisted index suggestion and a low-friction integration into lakehouse engines (Spark/Delta/S3). Offer an open-core connector for immediate adoption plus a SaaS control plane for automated index lifecycle, usage analytics, and cross-customer query pruning heuristics to speed time-to-value and create defensibility tied to aggregated metadata.

Core Features

• •Delta Lake connector + Spark plugin -- a drop-in connector and lightweight Spark plugin that attaches OTree metadata to Delta table partitions so queries with spatial and timestamp predicates avoid full-table scans; benefit: immediate latency and compute cost reduction on existing Databricks/Spark clusters without rewriting ETL.
• •OTree index builder -- a distributed indexer that scans Delta files, builds spatial+time OTree shards, and stores index artifacts alongside data (S3/ADLS) with versioning; benefit: fast incremental indexing for TB-scale tables with safe rollbacks.
• •Predicate pushdown integration -- automatic translation of common Spark spatial (geospatial extents) and timestamp filters into index lookups before file-level reads; benefit: reduces I/O and Spark task count, cutting query runtime and cloud costs.
• •Cost/benefit estimator & query planner hints -- a light-weight estimator that simulates index selectivity for a given filter and surfaces expected speedup / cost savings and optional hints to Spark; benefit: helps engineers decide when to enable index-based plans and quantify ROI.
• •CLI + managed deployment scripts -- opinionated CLI for creating indexes, scheduling incremental refreshes, and deploying the plugin on Databricks/EMR, plus Terraform/Helm manifests; benefit: one-command setup using managed services so teams avoid complex infra work.
• •Observability & benchmarking dashboard -- basic metrics (index size, hit-rate, query speedups, bytes scanned) and canned benchmarks (e.g., 10TB sample) delivered as a lightweight UI or Supabase-backed dashboard; benefit: measurable proof of impact for stakeholders and quick troubleshooting.

Key Market Trends

• •lakehouse consolidation -- more organizations standardizing on Delta/Hudi so integrated indexing pays off across many workloads
• •cloud cost scrutiny -- finance/infra teams demanding query cost-cutting to control runaway billable compute
• •rise of geospatial & time-series analytics -- more workloads require multidimensional pruning beyond simple partitioning

Market Drivers

• •Compute cost pressure -- rising cloud spend forces teams to optimize scanned data and query duration, raising willingness to buy optimization tooling
• •Standardized storage formats -- ubiquity of Parquet/Delta/Hudi gives a consistent surface for index integration across clouds
• •Performance SLAs for analytics -- BI and ML teams need predictable query latency which file pruning supports

Risk Factors

• ⚠Engine-level changes -- deep changes in core engines (e.g., Databricks introducing new native indexing) could reduce third-party relevance
• ⚠Integration complexity -- heterogeneous lake formats, catalog setups, and security models increase integration effort and slow adoption
• ⚠Low switching cost for competitors -- indexing approaches are replicable, so defensibility must come from data moats and enterprise integrations

Revenue Projections

Annual Revenue Projections

Unit Economics

Path to Revenue Milestones

Ideal Customer Profile

Mid-market to large enterprises running Delta Lake / Spark workloads (data platform engineers, analytics infra teams, and ML feature-store owners) that run multi-TB to 100sTB lakehouses and pay significant compute costs

Market Segments

Acquisition Channels

Customer Journey

Growth Strategies

Technology Stack

Team Requirements

Risk Mitigation

• ⚠API cost scaling (AI/codegen tools) -- mitigation: limit expensive model calls to CI/test-time (codegen, docs), cache and vet generated code, adopt cheaper models for routine tasks and implement quota/rate-limits.
• ⚠Model deprecation or provider changes -- mitigation: abstract any model usage behind a provider interface, maintain multi-model support (Claude/Gemini/Codex), and avoid business logic that depends on model-specific behaviors.
• ⚠Vendor lock-in (Databricks / cloud) -- mitigation: keep index metadata and artifacts in open/portable formats on object storage (S3/ADLS), provide connectors/adapters for alternative engines, and avoid deep reliance on proprietary platform APIs.
• ⚠Incorrect query plans / regressions -- mitigation: comprehensive regression test suite & benchmark suite that runs against representative datasets (including the 10TB sample), continuous integration for query planning changes, and staged rollouts with canary tests.
• ⚠Operational risk of index freshness vs correctness -- mitigation: provide safe fallbacks (ability to disable index-based planning per query), visibility into index staleness, and configurable refresh policies (time-based, event-based, manual) with transactional metadata so users can roll back.