Enterprise Data Platforms & Architectures

Designing data platforms that scale across teams, systems, and years of change.

Enterprise data platforms are rarely greenfield systems. They grow around existing databases, legacy applications, regulatory constraints, and organizational boundaries.

The challenge is not getting data in. The challenge is making data usable, shareable, and trustworthy across the enterprise — without forcing a full rewrite of everything that already exists.

Acosom works with enterprise architects and platform engineers to design data platforms that survive scale, politics, regulation, and constant change.

This expertise is about platforms, not pipelines.

What Enterprise Architects Gain

When data platforms are designed for organizational reality, not ideal theory.

Data Products Over Datasets

Transform ad-hoc datasets into intentional data products with clear semantics, explicit ownership, known consumers, and stable contracts. Data products become reusable enterprise assets.

Abstraction Layers for Flexibility

Separate data products from implementation details. Storage technologies can evolve independently, engines can be replaced without breaking consumers, and governance applies consistently.

Table Formats for Analytical Unity

Standardize analytical and historical representations using formats like Apache Iceberg and Paimon. Enable multi-engine access without coupling to source systems.

Incremental Modernization

Build around existing systems instead of replacing them. ERP platforms, operational databases, and legacy applications remain authoritative while the platform provides a coherent data layer.

Governance as Infrastructure

Ownership is explicit and enforced, access is policy-driven, usage depends on purpose and role, and lineage is built-in. Governance scales without manual heroics.

Multiple Consumption Patterns

Support analytical SQL access, dashboards, operational analytics, API-driven applications, and AI/ML feature consumption. The right way becomes the easy way.

Data Products in Enterprise Reality

In most enterprises, data already exists — often in many places.

Core systems such as ERP platforms, operational databases, and line-of-business applications are not going away. A modern data platform does not replace them; it builds a coherent data layer around them.

We help organizations move from ad-hoc datasets to intentional data products with clearly defined semantics, explicit ownership, known consumers, and stable contracts. A data product may be backed by an existing operational database, an analytical store, table-based datasets, or a combination of systems.

What matters is not where the data lives, but how it is structured, exposed, and governed.

Data Product Abstraction & Access Layers

Once data products exist, the next challenge is how they are accessed.

Separating Products from Implementation

The data product is the contract. The engine and storage are implementation details. Consumers should not need to know whether a data product is backed by Oracle, PostgreSQL, an analytical database, or tables using Apache Iceberg.

Multiple Access Patterns

Data products are exposed through APIs for applications, SQL access for analytics, read-optimized access for dashboards, and feature access for AI/ML. All consumers interact with the same logical product.

Independent Evolution

Storage technologies can evolve independently, engines can be replaced without breaking consumers, governance rules apply consistently, and data products become reusable enterprise assets. This enables incremental modernization, not disruptive rewrites.

Table Formats for Analytical & Historical Data Products

Table formats are applied deliberately and selectively for analytical use cases.

What Table Formats Are Used For

Table formats such as Apache Iceberg and Apache Paimon standardize analytical and historical representations. They enable data to be queried by multiple engines, retained for historical analysis, reused for AI/ML workloads, and governed consistently.

Complementing Existing Systems

Large enterprises rarely move all data into a single storage or engine. Operational systems like Oracle or PostgreSQL remain authoritative for transactional workloads. Table formats provide a structured analytical representation without replacing OLTP databases, search engines, or operational stores.

Analytical Unification

Operational and real-time systems handle live workloads. Derived data is materialized into table formats for historical and analytical access. This allows analytical use cases to evolve independently while operational systems stay focused.

Consumption & Access Patterns

A data platform is only successful if it is actually used.

Diverse Consumer Needs

Enterprise platforms support analytical and exploratory access via SQL, dashboards and reporting, operational analytics, application-level access through APIs, and AI/ML feature consumption. Different consumers have fundamentally different needs.

Architectural Concerns

Key concerns include latency versus throughput, concurrency and isolation, protecting core data from accidental misuse, and avoiding direct uncontrolled access to raw storage. A well-designed platform makes the right way the easy way.

Governance, Ownership & Organizational Scale

At scale, data platforms fail socially before they fail technically.

Governance that relies on manual processes, documentation alone, or tribal knowledge does not scale. We help organizations design governance as infrastructure where ownership is explicit and enforced, access is policy-driven, usage depends on purpose and role, and lineage and traceability are built-in.

Higher governance maturity does not imply full automation everywhere. In many enterprises, data product owners still report manually, approvals still involve humans, and exceptions remain part of reality.

The platform must support this maturity model, not deny it.

Technologies

Technologies support platform architecture — they do not define it.

Apache Iceberg

Table format for analytical data products. Enables schema evolution, time travel, and multi-engine access. Widely adopted for data lake architectures.

Apache Paimon

Streaming table format for real-time and batch use cases. Designed for integration with Flink and other processing engines. Supports both streaming and analytical workloads.

Trino

Distributed SQL query engine. Enables federated queries across multiple data sources. Used for analytical access to data products regardless of storage.

ClickHouse

Real-time analytical database. Columnar storage and fast aggregation for high-concurrency analytical workloads. Used for operational analytics and dashboards.

Apache Spark

Unified analytics engine for large-scale data processing. Supports batch, streaming, SQL, and ML workloads. Widely used for data transformation and ETL.

Apache Flink

Stream processing and batch engine. Used for table-based batch processing and hybrid workloads. Integrates with table formats for analytical use cases.

Oracle Database

Enterprise operational database. Remains authoritative source of record for transactional systems. Data products often exposed from existing Oracle systems.

PostgreSQL

Open-source relational database. Used for operational workloads and as source for data products. Often integrated into analytical platforms.

Apache Superset

Open-source data visualization platform. Used for operational dashboards and exploratory analytics. Connects to diverse data sources.

Power BI

Microsoft business intelligence platform. Used for enterprise reporting and dashboards. Integrates with data products through standard interfaces.

Collibra

Data governance and catalog platform. Manages data product ownership, lineage, and policies. Used for enterprise-scale governance.

Kubernetes

Container orchestration platform. Used for deploying and managing data platform infrastructure. Enables scalable and resilient deployments.

How This Expertise Is Applied

This expertise underpins initiatives such as enterprise analytics platforms, AI and ML data foundations, regulated data sharing across teams and regions, and gradual modernization around existing systems.

It complements — without overlapping — our other expertise areas: Streaming & Event-Driven Systems, Data & AI Governance in Regulated Environments, and Private & On-Prem AI Platforms.

Frequently Asked Questions

How do you approach platforms with many existing systems?

We start by understanding what already exists and why it exists.

Our approach:

Map existing data sources and their organizational purpose
Identify authoritative systems that should not be replaced
Design an abstraction layer that exposes data products consistently
Enable incremental migration without forcing rewrites
Respect operational boundaries and team ownership

The goal is not to replace everything. The goal is to make existing data usable across the enterprise.

When should we use table formats like Apache Iceberg?

Table formats are valuable for analytical and historical use cases, not as universal storage.

Use table formats when:

Data needs to be queried by multiple analytical engines
Historical analysis and auditing are required
AI/ML workloads need reproducible datasets
Schema evolution must be controlled and documented
Multiple teams need shared access to analytical data

Do not force table formats when:

Operational systems are already authoritative
Real-time transactional workloads are the primary use case
Specialized engines (search, graph, etc.) are more appropriate

We apply table formats deliberately, not dogmatically.

How do you handle governance in large organizations?

Governance must be infrastructure, not documentation.

Our approach:

Make ownership explicit and enforced in the platform
Implement policy-driven access control
Build lineage and traceability into the system
Support the organization’s actual maturity model
Accept that humans remain part of approval processes

Higher maturity does not mean full automation. It means the platform supports governance requirements without manual heroics.

What if our data is spread across many different systems?

This is normal for large enterprises.

Our approach:

Introduce an abstraction layer for data products
Use federated query engines like Trino for unified access
Materialize data into analytical stores only when needed
Respect the purpose of existing systems
Enable gradual modernization without disruption

The platform makes diverse systems usable, not uniform.

How do you balance centralized governance with team autonomy?

This tension is inherent to enterprise platforms.

Our approach:

Define clear ownership boundaries
Centralize policy enforcement, not data ownership
Enable self-service consumption within guardrails
Make governance violations visible, not blocked
Design platforms that support distributed ownership

Successful platforms enforce compliance without blocking teams.

Can you help with platforms that are already in production?

Yes. Most of our work involves improving existing platforms.

Common improvement areas:

Introducing data product abstractions over ad-hoc datasets
Adding governance layers to existing systems
Enabling multi-engine access through abstraction
Migrating to table formats without disrupting consumers
Refactoring for organizational scale
Documenting implicit knowledge

We evolve platforms incrementally without requiring full rewrites.

Building data platforms that must work across teams, systems, and years? Let’s talk about your architectural challenges.

Discuss Your Data Platform

Enterprise Data Platforms & Architectures

What Enterprise Architects Gain

Data Products Over Datasets

Abstraction Layers for Flexibility

Table Formats for Analytical Unity

Incremental Modernization

Governance as Infrastructure

Multiple Consumption Patterns

Data Products in Enterprise Reality

Data Product Abstraction & Access Layers

Separating Products from Implementation

Multiple Access Patterns

Independent Evolution

Table Formats for Analytical & Historical Data Products

What Table Formats Are Used For

Complementing Existing Systems

Analytical Unification

Consumption & Access Patterns

Diverse Consumer Needs

Architectural Concerns

Governance, Ownership & Organizational Scale

Technologies

Apache Iceberg

Apache Paimon

Trino

ClickHouse

Apache Spark

Apache Flink

Oracle Database

PostgreSQL

Apache Superset

Power BI

Collibra

Kubernetes

How This Expertise Is Applied

Frequently Asked Questions

How do you approach platforms with many existing systems?

When should we use table formats like Apache Iceberg?

How do you handle governance in large organizations?

What if our data is spread across many different systems?

How do you balance centralized governance with team autonomy?

Can you help with platforms that are already in production?

Building data platforms that must work across teams, systems, and years? Let’s talk about your architectural challenges.

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