The Modern Data Company builds DataOS, a data-centric operating system equipped with comprehensive tooling and services to help Fortune 500-scale enterprise data teams streamline data management and analytics workflows.
Modern states DataOS delivers marked business gains: 8x faster application deployment, 90% quicker data activation, 10x faster decision-making and 50% lower data operational costs. Designed for large enterprises, the platform integrates with existing IT environments and unifies disparate data, business context, governance and data discoverability via reusable, outcome-focused data products.
Technically, DataOS adopts a distributed cloud-native microservices architecture. Each embedded service operates independently to deliver a bounded business capability, with dedicated microservices governing core data functions including data cataloging, access control and data security.
It integrates end-to-end capabilities for data ingestion, processing, storage and analytics, alongside built-in governance and monitoring tools to ensure secure, compliant and standardized data operations. The platform offers modular building blocks for teams to develop customized data products, services and applications, enabling self-service operations for data engineers, business users and domain teams across the enterprise.
DataOS features a multi-tiered architectural structure, detailed as follows:
Cloud Kernel: A cloud-agnostic IaaS abstraction layer spanning AWS, Azure and GCP. Its containerized services automate node pool provisioning and lifecycle control.
Core Kernel: Acts as the foundational OS kernel, managing core functions such as CPU scheduling, memory allocation and application I/O ports. It includes dedicated drivers for cross-service communication and enforces bidirectional access control for inbound and outbound traffic.
User Space: A multi-tenant operational environment for data developers, split into independent user and system layers that interface with the Core Kernel. Users deploy, configure and manage all DataOS resources within this layer.
The Core Kernel hosts six key functional services:
Heimdall: Centralized governance engine governing all DataOS access control policies.
Metis: Unified metadata manager that aggregates and organizes technical, operational and business metadata from diverse sources, presenting structured data resource insights to developers via GUI.
Minerva: High-performance federated query engine for cross-source data querying.
Poros: Workflow and resource orchestration service.
Gateway: Runs atop Minerva to manage cluster operations, user authentication via Heimdall integration, and real-time data policy enforcement.
Caretaker: Collects, stores and archives runtime metrics including pod status and compute node statistics, retaining historical operational data in blob storage.
All services store independent data and state in dedicated databases, adopting zero-trust security principles for standardized REST API cross-service communication.
DataOS leverages connectors, integrations and abstraction layers to ingest, discover, catalog and activate cross-environment data without mandatory data migration or stack replacement. It supports a wide range of enterprise data sources:
Cloud data warehouses including Snowflake, Google BigQuery and Amazon Redshift;
Lakehouses and data lakes such as Databricks Unity Catalog, Delta Lake, Iceberg, Apache Hive, S3, GCP and Azure Data Lake;
Relational and NoSQL databases including SQL Server, PostgreSQL, MySQL and Oracle;
Streaming platforms like Kafka and real-time event pipelines;
Enterprise SaaS and application systems including ERP and CRM via APIs and connectors;
Legacy data catalogs, BI tools and compute engines.
The platform executes cross-source querying and data activation without full data replication. It supports on-premises, multi-cloud and hybrid deployments, with built-in AI readiness to prepare contextualized data for LLMs, agentic workflows and advanced analytics.
Comment
Modern has iteratively refined DataOS with rigorous, sustained development in recent years, forming a mature, high-caliber enterprise software stack. Its core clients are Fortune 500 enterprises across financial services, manufacturing, retail/CPG and public sector industries, with a strategic partnership with Carahsoft to expand U.S. government market coverage.
DataOS excels at standardizing fragmented enterprise data stacks with scattered analytics tools, ETL pipelines and application systems. It rationalizes complex data workflows via reusable data products, overlaying new capabilities on existing infrastructure rather than conducting full-scale replacement. The firm plans an official product launch, with extensive industry use cases, documentation and technical resources available on its website for reference.
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Founded in Silicon Valley in 2018 as Rubik.ai, the company was co-established by initial CEO Srujan Akula and current CTO/CPO Animesh Kumar. Saurabh Gupta was promoted from Chief Revenue and Strategy Officer to CEO and President in March 2024, while Akula shifted focus to product strategy and execution. The firm’s funding details remain undisclosed, with Pitchbook recording an undisclosed early-stage VC round from two investors in March 2022.
Its seven-plus years of continuous iteration implies robust, long-term capital backing and extensive design validation with enterprise clients.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
Website: www.qianxingdata.com/www.storagesserver.com
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