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Data-Driven Results with Open-Source Flexibility
89%
of organizations seek open-source data platforms to eliminate silos and foster interoperability across analytics, AI, and operational workloads
72%
report faster data product development cycles using modular, open-source components tailored to domain-specific needs
3x
faster adoption of AI/ML use cases when leveraging unified, schema-flexible data layers built with open standards and APIs
95%
achieve improved cost efficiency and platform extensibility with community-supported tools and no vendor licensing overhead
Platform Highlights
XenonStack’s Open-Source Data Platform empowers organizations with a unified, composable architecture that seamlessly integrates data lakes, warehouses, and streams
01
Unify batch pipelines, streaming events, and unstructured assets under a single, extensible architecture
02
Use community-backed standards like Iceberg, Kafka, and Trino to ensure interoperability, transparency, and vendor independence
03
Deliver low-latency insights through streaming frameworks and on-demand querying across massive datasets
04
Support distributed deployments across cloud, hybrid, and edge setups—centrally governed and fully observable
Principles Behind Our Platform Approach
Modular Infrastructure
Mix and match open-source tools to create a tailored ecosystem without vendor lock-in
Metadata-First Thinking
Govern your data proactively with lineage tracking, data catalogs, and schema versioning
Developer-Driven Operations
Enable data engineers with APIs, declarative configs, and GitOps-style automation
Transparent Resource Consumption
Gain visibility into compute and storage costs, usage trends, and optimization opportunities
Cloud Integrations & Ecosystem Compatibility
Google Cloud Native Stack
Integrate with BigQuery, GCS, and Vertex AI using open connectors and Kubernetes-native orchestration
AWS-Compatible Architecture
Utilize Glue, Athena, and Lake Formation with support for Iceberg, S3, and open table formats
Azure-Enabled Deployments
Run data workloads on ADLS, Synapse, and AKS using hybrid-compatible open data tooling
Use Cases for Open Data Platforms
Streaming Analytics
Real-Time Pipelines with Kafka and Flink
Monitor, analyze, and act on streaming data for fraud detection, logistics, and IoT
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AI and ML Readiness
Unified Data for Model Training and Serving
Create consistent, trusted datasets for building and deploying machine learning models at scale
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Governance and Security
Metadata-Driven Data Compliance
Enforce access controls, audit trails, and data classifications across all layers
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Business Intelligence
Query Anything, Visualize Anywhere
Deliver governed self-service analytics with universal access across BI and reporting tools
How We Deliver Data Platform Modernization
Composable by Default
Build a solution stack that fits your current needs and evolves with your data maturity
Hybrid-Ready Architecture
Run your platform seamlessly across public cloud, private cloud, and edge compute environments
Insight-Driven Observability
Visualize system health, pipeline freshness, and query performance in real time
Security as a Foundation
Adopt end-to-end security practices—from encrypted storage to fine-grained identity and access policies
Ops at Dev Speed
Empower teams with Git-based workflows, declarative configurations, and CI/CD for data infrastructure
Competencies
Benefits of Open-Source Data Platforms
Open-Source Data Platforms offer organizations flexibility, transparency, and cost-efficiency in managing and analyzing data at scale
Cost Optimization Redefined
Scale freely with community-driven, license-free software
Flexibility Redefined
Mix and match open-source components to build the ideal stack
Transparency Redefined
Access source code and configuration — no vendor black boxes
Innovation Redefined
Rapidly adopt new tools and techniques with the thriving OSS ecosystem
Cloud Native Data Platform - Data Warehouse and Data Lake
Explore how cloud-native architectures unify Data Warehouse and Data Lake capabilities to deliver scalable, cost-efficient, and real-time data platforms optimized for AI, analytics, and operational agility
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Big Data Platform: Introduction, Key Features
Understand the core components and advantages of a modern Big Data Platform — including scalability, fault tolerance, multi-source integration, and support for batch and stream processing
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