Streaming Data Platforms

A modern streaming data platform — designed, built, and operated on your infrastructure.

Enterprises need reliable real-time data pipelines, streaming analytics, and SQL-based transformations, but cloud costs, compliance rules, and skill shortages often limit adoption.

Acosom designs and implements streaming data platforms and data engineering solutions that run on-premise or in hybrid environments, enabling multiple teams to build and deploy streaming applications, SQL jobs, and data products — without vendor lock-in and without unpredictable cloud bills.

This is not a product.
This is a custom, scalable platform architecture, built with open technologies like Apache Flink, Kafka, Paimon, Iceberg, ClickHouse, and Kubernetes — fully tailored to your business and infrastructure.

What Your Organization Gains

We help you build the internal capabilities, data engineering solutions, and infrastructure that transform data operations.

A Central Streaming Platform Team (Center of Excellence)

We help you establish a team that owns:

Platform operations & scalability
Streaming application onboarding
Data contracts & schema governance
Best practices for Flink, Kafka & SQL pipelines
Cross-team support and enablement

This becomes your internal data streaming capability — aligned with data mesh and modern platform engineering practices.

A Unified Event Model & Schema Governance

We create the structured foundation that most enterprises lack:

Central schema registry & event catalog
Standards for domain-driven event design
Schema evolution workflows
Automated CI/CD validation
Data contracts between producing and consuming teams

This unlocks consistent real-time analytics, high-quality ML features, and reliable business metrics.

A Multi-Team, Guarded Self-Service Platform

Multiple teams can safely deploy:

Flink streaming jobs
SQL-based transformations
Batch + streaming hybrid pipelines
Data ingestion flows
Domain data products

All without needing expertise in Flink internals, Kubernetes, or operator patterns.

Success Story

From Fragmented Tools to Unified Platform

A European insurance company struggled with 15+ different data teams using incompatible tools and creating duplicate pipelines. We designed and implemented a self-service streaming platform that consolidated their Kafka and Flink workloads onto a unified infrastructure.

Result: 70% reduction in infrastructure costs, 3x faster time-to-production for new streaming applications, and a central platform team supporting 200+ streaming jobs across all business units.

Book a Free Consultation

Why Self-Service Data & Streaming Platforms Matter

Self-service capabilities are becoming essential for modern enterprises looking to scale real-time analytics and data-driven operations. These are the outcomes organizations search for when looking for self-service data platforms, real-time analytics platforms, or Flink/Kafka consulting.

Faster Delivery of Streaming Applications

Teams deploy pipelines independently through governed CI/CD workflows, accelerating time-to-value for analytics and operational insights.

Operational Reliability & Governance Built-In

Unified schema catalogs, data contracts, lineage, and observability ensure consistency and compliance across all data flows.

Cost Efficiency and Freedom from Cloud Lock-In

Your platform runs efficiently on-premise or hybrid, avoiding unpredictable cloud billing while maintaining full control over your data.

Consistent Domain Language

A shared event model aligns analytics, ML, reporting, and operational decision-making across the entire organization.

Future-Proof Foundation for AI & ML

Clean, governed real-time data feeds downstream AI systems and on-prem LLMs, enabling advanced feature engineering and model training.

Enterprise-Grade Scalability

Built on proven open-source technologies with horizontal scaling capabilities to handle growing data volumes and user demands.

What We Architect & Implement

From self-service streaming to enterprise-grade observability, we build the complete platform stack.

Self-Service Streaming & Analytics

Automated Flink job lifecycle (submit, scale, rollback)
SQL runner integrated into CI/CD
Multi-tenancy & resource isolation
Deployment templates & guardrails
Domain-driven job structures

Unified Storage & Lakehouse Layer

Based on open table formats and high-performance engines:

Apache Paimon / Apache Iceberg
ClickHouse for real-time analytics
MinIO or S3-compatible object stores
PostgreSQL for metadata or smaller datasets

This supports data lakehouse, streaming ETL, and real-time analytical queries.

Observability & Reliability

Enterprise-grade monitoring:

Grafana + Prometheus dashboards
Loki / ELK logs and trace views
SLA-driven alerting
High availability architectures
Backup & disaster recovery strategies

Your platform becomes predictable, observable, and safe.

Governance, Compliance & Security

Aligned with DACH requirements in banking, insurance, healthcare, and energy:

Schema and contract governance
Access control & RBAC
Audit logging & lineage
Data quality validation
Environment separation

Perfect for organizations concerned about AI governance, data mesh governance, or regulatory compliance.

Event-Driven Architecture & CDC

Build real-time data flows with event-driven patterns:

Change Data Capture (CDC) from databases
Event streaming & routing patterns
Event sourcing implementations
Domain events & business event modeling
Integration with legacy systems

Connect all your data sources into a unified streaming data platform.

CI/CD & DevOps for Data

Automated deployment pipelines for data teams:

GitOps workflows for Flink jobs
Automated testing & validation
Environment promotion strategies
Infrastructure as Code (IaC)
Rollback & recovery procedures

Empower teams to deploy confidently and independently.

Technology Stack

We use open, industry-standard technologies — no vendor lock-in.

Apache Flink

Streaming & Processing with Kubernetes Operator, OpenShift, Flink SQL & table ecosystem, and event-time processing capabilities.

Apache Kafka

Messaging & Ingestion with Change Data Capture (CDC) pipelines and custom ingestion flows for reliable event streaming.

Apache Paimon / Iceberg

Modern table formats for streaming data lakes, enabling high-performance queries on real-time and historical data with schema evolution support.

Qdrant

High-performance vector database for semantic search and AI applications, enabling fast similarity search and recommendation systems.

Kubernetes / OpenShift

Platform Engineering with GitOps workflows, automated CI/CD pipelines, and enterprise-grade container orchestration.

Observability Stack

Prometheus, Grafana, Loki, and ELK for comprehensive monitoring, alerting, and troubleshooting capabilities.

Managed Operations (Optional)

Acosom can operate your platform under enterprise SLAs for long-term stability and continuous optimization.

SLA-Based Support

8/5, 12/5, or 24/7 monitoring and support tailored to your operational requirements with guaranteed response times.

Continuous Optimization

Job troubleshooting, performance optimization, patch & upgrade cycles, and capacity forecasting for your platform.

Security & Compliance

Platform security checks, compliance validation, and adherence to regulatory requirements including GDPR and industry-specific regulations.

Strategic Roadmap

Ongoing platform evolution, technology evaluation, and alignment with business objectives to ensure long-term success.

Frequently Asked Questions

What is a self-service data platform?

A self-service data platform enables multiple teams to independently build, deploy, and manage streaming applications, SQL jobs, and data pipelines without requiring deep expertise in infrastructure operations. It provides standardized templates, automated CI/CD workflows, and guardrails that ensure reliability and governance while accelerating time-to-value for data products.

Why choose on-premise or hybrid over cloud-only?

On-premise and hybrid deployments offer several advantages: complete data sovereignty for compliance with regulations like GDPR, predictable costs without cloud billing surprises, ability to leverage existing infrastructure investments, and full control over security and data residency. This is especially important for DACH enterprises in banking, insurance, healthcare, and energy sectors.

What technologies do you use to build these platforms?

We build platforms using proven open-source technologies: Apache Flink for stream processing, Apache Kafka for messaging, Apache Paimon and Iceberg for lakehouse storage, ClickHouse for real-time analytics, Kubernetes for orchestration, and comprehensive observability stacks (Prometheus, Grafana, Loki, ELK). This ensures no vendor lock-in and gives you full control.

How long does it take to implement such a platform?

Implementation timelines vary based on scope and complexity, but a minimum viable platform typically takes 3-6 months. This includes architecture design, core infrastructure setup, schema governance implementation, and onboarding of the first teams. Full platform maturity with comprehensive self-service capabilities typically requires 9-12 months with iterative improvements.

Can you integrate with our existing systems?

Yes, we specialize in hybrid environments. We can integrate with existing databases via Change Data Capture (CDC), connect to legacy systems through custom connectors, and work alongside your current cloud and on-premise infrastructure. Our platforms are designed to coexist with existing systems during gradual migration.

Do you provide ongoing support after implementation?

Yes, we offer flexible managed operations with enterprise SLAs (8/5, 12/5, or 24/7 support). This includes platform monitoring, troubleshooting, performance optimization, security updates, capacity planning, and continuous evolution of your platform as your needs grow.

Let’s design your self-service data & streaming platform — tailored to your infrastructure.

Book a free architecture review