The Death of Data Duplication: Inside Edelbyte's $70M Push for Sub-100ms Agentic Analytics
Former Azure Data Explorer architects secure $70M to challenge Snowflake and Databricks with federated in-place querying and native vector-graph convergence.
Published: 2026.11.12
Visit Official SiteFederated query execution across distributed source systems
Seed and Series A led by Sequoia and Quiet Capital
Higher throughput per compute dollar vs. legacy cloud warehouses
Autonomous software agents create query access patterns that break conventional data lakehouse economics. Unlike human business intelligence operators who submit scheduled batch queries, autonomous agents issue continuous, non-deterministic exploratory queries across tabular data, embeddings, and relationship graphs. Centralized warehouses force organizations to ingest, normalize, and duplicate petabytes of operational data before executing queries, compounding cloud egress tariffs and data staleness.
Edelbyte emerged from stealth with $70 million in combined Seed and Series A financing at a $500 million valuation to re-engineer this compute pipeline. Founded by former Microsoft engineers Avner Aharoni, Evgeney Ryzhyk, Alexander Sloutsky, and Yoni Leibowitz—the technical creators behind Azure Data Explorer (Kusto)—the platform targets multi-petabyte analytics workloads directly where raw data resides.
Sub-100ms Latency on Petabytes: How In-Place Query Federation Replaces ETL Pipelines
Conventional data architectures require rigid Extract, Transform, Load (ETL) pipelines to copy operational datastores into proprietary formats before query execution. This architecture introduces end-to-end data ingestion latency ranging from 15 minutes to several hours, rendering real-time retrieval-augmented generation (RAG) and low-latency agent loops inefficient. Furthermore, cross-cloud egress charges and compute duplication frequently comprise 30% to 45% of enterprise lakehouse bills.
Edelbyte bypasses centralized staging through distributed query federation. Instead of requiring centralized cluster ingestion, its execution engine pushes query logic down to the underlying storage engines where raw records reside. By analyzing execution patterns at the edge of the data layer, the platform automates secondary index generation, pruning partition scans before network transfer occurs.
Query execution path comparison for enterprise agentic workloads
- • Ingestion lag: 15 to 180 minutes pipeline overhead
- • Egress duplication: Full dataset replicated into proprietary storage
- • Single-model silos: Vector, graph, and relational engines require separate clusters
- • Sub-100ms query latency on multi-petabyte federated storage
- • Zero-copy architecture: Computes directly on source systems
- • Native unified model: Executes vectors, graph paths, and tabular queries in one pass
The underlying engine unifies relational tables, dense vector embeddings, and graph topologies within a shared execution plan. When an AI agent executes a reasoning step requiring hybrid search, standard architectures orchestrate calls across a vector database, a graph database, and a relational lakehouse. Edelbyte evaluates vector proximity, relational filters, and graph traversals within a single federated execution graph, enforcing sub-100 millisecond response latencies across multi-petabyte footprints.
Comparative Economics: Edelbyte vs. Snowflake and Lakehouse Architectures
Enterprise data engineering teams evaluate data platforms on cost per query, network egress overhead, and storage expansion coefficients. Traditional cloud warehouses bill compute via running virtual warehouse credits that stay active even during IO-wait states. When autonomous agents generate bursty, unpredictable query spikes, auto-scaling warehouses maintain idle compute headroom, inflating month-end bills.
In high-concurrency exploratory workloads, early design partner benchmarks indicate Edelbyte delivers up to ten times the query throughput within equivalent budgetary bounds compared to legacy warehouse fleets. This divergence expands as datasets scale beyond five petabytes.
| Architecture & Cost Vector | Edelbyte Federated Engine | Snowflake (Standard / Enterprise) | Databricks Lakehouse (Delta Engine) |
|---|---|---|---|
| Primary Data Access Model | Federated in-place compute + pushdown indexing | Centralized ingestion into micro-partitions | Object storage lakehouse (Parquet/Delta tables) |
| P95 Query Latency (Multi-PB) | <100ms (federated hybrid queries) | 1.2s – 8.5s (cached warehouse dependent) | 800ms – 5.0s (serverless SQL compute) |
| Vector & Graph Support | Native unified engine (vectors + graph + tables) | Vector search native; external integration for graph | Databricks Vector Search; separate GraphX compute |
| Storage Ingestion Requirement | Zero mandatory staging; reads external stores | Proprietary internal tables or Iceberg external | Delta Lake format standardization required |
| Data Transfer / Egress Overhead | Minimal (filtered query pushdown) | High (initial load + inter-cloud egress) | Moderate to High (inter-cloud data sync) |
| Index Maintenance Overhead | Autonomous adaptive generation | Automatic clustering (consumes billable credits) | Liquid clustering / Z-ordering maintenance jobs |
Estimated total monthly spend comparison across infrastructure layers
The economic divergence stems primarily from data movement suppression. When multi-petabyte datasets reside across object storage, transactional engines, and security logging lakes, streaming changes into a central warehouse drives compounding ingestion fees. By applying dynamic pushdown filtering, Edelbyte reduces network payload volume by up to 88% prior to serialization.
Operational Friction: Autonomous Index Tuning and Schema-Free Graph Processing
The primary barrier to enterprise-wide data federation has historically been performance degradation caused by unoptimized network scans. Federation solutions often collapse when join operations require moving unindexed tables across network backplanes. To sustain sub-100ms response targets, Edelbyte automates index generation directly from observed application query profiles.
The engine observes agent access trajectories, identifying repeated filtering keys, vector dimensions, and relationship traversals without manual DBA intervention. It generates compact, localized index partitions that live alongside source datasets. When an agent requests a contextual graph traversal across related operational entities, the engine avoids brute-force table scans by traversing pre-compiled edge indices.
Execution stages from agent prompt to federated sub-100ms response
AI agent dispatches hybrid query containing vector embeddings and tabular constraints.
Engine resolves storage locations and splits execution plans across distributed systems.
Localized secondary indexes filter unneeded partitions prior to network transit.
In-memory join merges graph edges, vectors, and records into sub-100ms response payload.
This architecture introduces specific technical tradeoffs. Systems that run deep analytical transforms (heavy write-side aggregate updates and long-running OLAP batch jobs) do not benefit as significantly from federated index pushdown as read-heavy interactive workloads. Organizations must evaluate the operational boundaries of this stack:
- Metadata Synchronization Overhead: Running federated queries across disparate transactional engines requires continuous schema tracking. If upstream operational teams alter table schemas without contract governance, federated query planners risk plan invalidation.
- Source System Resource Contention: Pushing filter logic down to production transactional databases can trigger CPU and memory spikes on operational nodes. Infrastructure teams must enforce strict resource governor caps on federated endpoints.
- Cold Storage Access Penalties: Queries touching archival storage classes (such as S3 Glacier or cold blob tiers) will violate sub-100ms SLA thresholds due to fundamental physical retrieval limitations.
Enterprise Procurement Framework: Evaluating In-Place Federation vs. Centralized Warehousing
Engineering leadership must separate exploratory AI agent infrastructure from core financial reporting warehouses. Migrating an entire enterprise data ecosystem away from established vendors such as Snowflake or Databricks introduces unnecessary migration friction. Edelbyte should be evaluated based on the concurrency profile and latency requirements of real-time operational workloads.
Eliminates ETL lag, unifies vector/graph queries, and avoids data duplication.
Leverages deep batch optimization, mature compliance tooling, and fixed reporting schemas.
Organizations Positioned for Immediate Operational Advantage
- Real-Time AI Agent and Agentic Workflow Teams: Engineering groups deploying RAG architectures that require simultaneous vector similarity searches, graph relationship checks, and structured record lookups under 100 milliseconds.
- Cybersecurity Telemetry and SOC Operations: Organizations managing petabyte-scale security event streams (similar to architectures pioneered by Azure Sentinel and Kusto) where moving log data to external warehouses incurs prohibitive transit latency and egress fees.
- Distributed Multi-Cloud Enterprises: Organizations whose core data assets are fragmented across multiple cloud providers and on-premises stores, where centralized consolidation projects have stalled due to data gravity and egress cost constraints.
Organizations That Should Maintain Incumbent Architectures
- Standard Internal BI and Scheduled Dashboard Operators: Teams whose analytical workloads consist primarily of scheduled daily or weekly reporting aggregations running on standard relational schemas.
- Single-Cloud Environments with Established Zero-Copy Pipelines: Organizations that have already standardized on a single cloud vendor and leverage centralized catalogs with low storage overhead and predictable credit consumption.
- Workloads Dependent on Heavy Write-Time Transformations: Pipelines centered on complex, multi-stage dbt or Spark transform operations that require centralized, immutable table states rather than interactive ad-hoc query federation.