What is a modern data platform?

6 minute read

What is a modern data platform? A modern data platform is a unified suite of tools and services that capture, store, process and serve trusted data for analytics, BI and AI. If you searched "What is a modern data platform?", that phrase points to a platform that turns raw streams and ERP records into reliable metrics so teams can make faster decisions and scale AI use cases. Typical outcomes include near-real-time KPIs, fewer manual reports and improved alignment between IT and domain teams, which helps production planning and supply chain responsiveness.

Legacy technology stacks cause predictable problems: siloed warehouses, expensive scaling, brittle ETL, delayed insights and governance gaps that erode trust. Manufacturing, food and beverage, retail and distribution firms often hit these limits as volumes and stakeholder demand grow. The sections below describe core architecture patterns, vendor trade-offs and a practical roadmap for running a pilot and measuring impact.

Key takeaways

Unified data toolkit. Combine ingestion, storage and processing to deliver trusted data for analytics, BI and AI. Add a semantic layer so KPIs remain consistent across reports and models.
Ingestion first. Map sources and choose batch or streaming by SLA and use case, then pilot a single-domain pipeline to prove value fast. Start with the highest-impact source and measure latency and completeness during the pilot.
Metadata and governance. Implement catalogs, lineage and clear ownership so datasets are discoverable and auditable. Automated tests and ownership reduce firefighting and increase confidence.
Architecture fit. Select lakehouse, data mesh or fabric based on team skills, domain boundaries and workloads. Pick the pattern that matches how your organisation operates rather than chasing the latest trend.
Practical roadmap. Follow assessment, a short POC, phased migration and operational runbooks, and define success metrics before the POC.

What a modern data platform is and why it matters

A modern data platform captures, stores, processes and delivers reliable datasets that support both operational and analytical processes. The business value lies in clearer decisions and faster reactions – based on solid data foundations instead of fragile scripts and Excel files.

When organisations begin to see data as a product, planners and analysts get consistent data sources for daily decisions and models.

Older data landscapes often create clear problems that slow down the business. Link these problems to concrete platform capabilities so that stakeholders see how modernisation actually solves business challenges, for example by replacing slow ERP reports with streaming ingestion, a transactional lakehouse and a semantic layer.

Below are common problems and how a platform can solve them:

Data silos. Isolated sources make comparisons between teams difficult. A unified storage layer combined with a semantic layer provides consistent definitions and a common version of key metrics, reducing duplicate work and reporting chaos.
High costs when scaling. When storage and compute are tightly coupled, costs rise quickly as data grows. By separating storage and compute, you can scale them independently and control costs with cloud object storage and flexible query engines.
Fragile ETL. Manual pipelines break when upstream systems change. Event-driven ingestion and versioned transactional formats enable replaying data and recovery from errors, reducing downtime.
Delayed insights. Long batch cycles mean teams work with outdated data. Low-latency processing and streaming transformations provide near real-time KPIs and alerts so the business can act faster.
Lack of governance. Absence of metadata and lineage reduces trust in datasets. Catalogues and federated governance rules document ownership, lineage and access, supporting audits and compliance.

CIOs, data architects and IT managers within industry, retail, food & beverage and distribution should prioritise modernisation when handling dozens of systems or thousands of SKUs. Low-risk pilots such as domain-based reporting, a lakehouse proof of concept or an ERP-to-analytics connector can validate assumptions and reduce migration risk. Use the pilots to gather metrics that demonstrate business impact before scaling further.

Core components: ingestion, storage and processing

Ingestion must support both batch and streaming as they meet different SLAs and use cases. Batch suits large periodic loads such as ERP exports and historical reconciliations, while streaming is needed for real-time signals from IoT devices, application logs and change data capture from transactional databases.

Tools like Debezium or AWS DMS are often used for CDC, while Kafka, MSK or Kinesis handle high throughput. SaaS connectors simplify onboarding of third-party APIs. For a practical overview of modern ingestion methods and architecture patterns, see Fivetran's guide to modern data architecture.

When it comes to storage, you should start with durable object storage like S3 and then add a transactional layer for ACID guarantees, versioning and time travel. Implement Delta Lake, Iceberg or Hudi to enable safe concurrent writes, schema enforcement and reliable rollbacks.

This makes storage cost-effective while supporting analytics and reproducible experiments. Decide whether most of your queries will run in a warehouse for fast SQL performance, or in a lakehouse if you need ML support and large historical scans.

Processing often follows an ELT-first model: land raw data first, then transform close to the compute engine running queries or models. Use streaming transformations for enrichment and alerts, and orchestration tools like Airflow, Glue or Step Functions for scheduling, retries and lineage.

Version control pipelines, automate tests and data quality checks, and collect operational metrics so transformations remain safe and auditable.

These three layers form the core of the platform. Metadata and governance then make datasets searchable, traceable and ready for downstream users.

Metadata, governance, and serving: trust and access

Metadata acts as the control plane for data products. Catalogs document datasets, reveal lineage, and display schemas so teams can find and trust data without manual checks. Tools like Unity Catalog, AWS Glue, and Purview centralise these functions and also show usage statistics that help consumers assess the suitability of datasets. For a concise overview of the fundamental principles that govern modern architectures, see the six modern principles of data architecture.

Schema enforcement and controlled schema evolution reduce the risk of downstream failures and ongoing incidents. By enforcing schema at write time where possible and using versioned transactional storage formats, you get controlled evolution and time travel. Supplement this with automated validation tests at ingestion and simple contract checks before production deployment.

Governance scales best when central guardrails are combined with clear domain ownership and role-based access control. Central teams publish policies and tools, while domain teams build and operate data products according to these rules.

Start with access control, encryption in transit and at rest, and lineage-based compliance checks to establish a baseline level of trust.

The serving layer should reflect user needs: semantic layers and curated metrics for analysts, REST or Graph APIs for applications, as well as reverse ETL for operational systems. Measure adoption, latency and accuracy for each serving level to understand where value is created.

These metrics determine whether the semantic layer, APIs or reverse ETL actually deliver the desired business impact.

Architecture patterns: lakehouse, data mesh, data fabric, and event-driven

Choose a pattern that matches business goals, data gravity, and the team's skills. The options below cover common choices and when they work best in practice.

Lakehouse: uniting lake and warehouse

Lakehouse is based on a shared storage layer with transactional metadata so that BI, reporting, and ML can work on the same raw and curated datasets. Formats like Delta and Iceberg add ACID guarantees, schema enforcement, and time travel, enabling reliable backfills and reproducible experiments.

For many teams, lakehouse reduces cost and complexity by avoiding copies between separate lake and warehouse silos.

Data mesh: domain-driven data products

Data mesh treats domains as product teams that own and publish datasets with clear contracts and SLAs, supported by a self-service platform. Federated governance shifts the focus of central teams from owning all data to enabling standards, quality controls, and interoperability.

Choose mesh when domains are large and independent, and evolve towards this as product culture and tools mature.

Data fabric and hybrid overlays

Data fabric is a metadata-driven overlay that integrates distributed sources without heavy data migration, enabling discovery, virtualisation, and policy enforcement across cloud and legacy systems.

Fabric fits hybrid landscapes or situations where data copying becomes too expensive, and can complement a lakehouse when consolidated, queryable storage is needed. Use fabric for discovery and policy enforcement, but rely on lakehouse for robust analytics and ML workloads.

Event-driven and lambda-like hybrids

Event-driven architectures stream changes in system state through pub-sub systems to create low-latency pipelines and scalable processing. Combine streaming with periodic batch backfills to ensure complete history and data consistency.

Decoupled producers and consumers increase robustness and enable independent scaling and recovery.

These patterns help you align architecture with business needs and team capacity. Choose a first pattern for the pilot and then adapt as operational maturity and ownership models develop.

Vendor and cloud choice: comparison between Databricks, Snowflake, BigQuery, Microsoft Fabric and AWS

Differences between vendors are essentially about which types of workloads they are best suited for and the strengths of the team. Databricks is strong for ML-first engineering with open table formats and advanced data science workflows. Snowflake focuses on SQL warehousing and simple multi-cloud operation. BigQuery offers serverless scale for ad hoc analysis.

Microsoft Fabric integrates closely with Power BI. For guidance when choosing between lakehouse and traditional warehouse, see Microsoft's decision guide on lakehouse vs warehouse.

Choose a platform based on clear criteria: volume and latency needs, AI maturity, existing cloud choices and the team's expertise. Run a proof of concept over 60–90 days that reflects a real production workflow and validate governance, metadata and TCO assumptions.

Compare pricing models such as pay-per-query, compute credits or provisioned clusters, and build a simple TCO model based on the expected query mix and forecasted data growth.

Focus on whether the vendor supports your data and AI goals, the right balance between control and managed services, as well as useful tools for metadata and governance. Use the POC to test team onboarding, data ownership processes and operational tasks before deciding on full migration.

For an in-depth review of strengths, architectural choices, and cost models between these options, read our article on the best modern data platforms 2026, where we compare the platforms based on real workloads, governance requirements, and total cost of ownership.

Roadmap for modernisation: plan, migrate and operate

Start with a clear step-by-step roadmap so teams know what to do this week and next. The plan below covers assessment, strategy, a small pilot, phased migration and controlled cutover – with a concrete checklist item per phase to get started quickly. For organisations wanting to work systematically with the entire value chain from data to decision, a clear Data Intelligence strategy is crucial.

Assessment and data audit. Inventory sources, schemas and data quality. Checklist: run a 48-hour snapshot of schema and lineage for your three most important ERP tables to validate ingestion and metadata.
Strategy and target architecture. Define domains, governance and cost targets. Checklist: create a two-page target diagram showing storage, processing and serving layers for one domain.
Pilot a high-value use case. Choose a quick win like reporting or forecasting. Checklist: deliver a self-service dashboard from ERP to BI within a week using an isolated dev account. If you need help prioritising projects, start by identifying and prioritising AI and ML use cases for your organisation.
Phased migration with parallel operation. Move teams domain by domain and run parallel systems. Checklist: conduct a four-week parallel run for a critical report and compare results daily.
Cutover and optimisation. Switch production systems with rollback plans and post-cutover validation. Checklist: plan a weekend cutover with automated tests and a 48-hour support rotation.
Measure results with KPIs linked to business impact. Track goals such as TCO reduction, improved time-to-insight, query latency SLOs and self-service adoption. For leadership: report monthly one business outcome, one operational metric and one platform metric with trend lines.

Reference architectures differ between cloud providers. A typical AWS stack combines S3 for storage, Glue or Unity Catalog for metadata, Kinesis or MSK for streaming ingestion, Databricks or Glue/EMR for processing, as well as Athena or Redshift for serving and SageMaker for ML workflows.

Operational best practices include multi-account landing zones, infrastructure as code, automated data testing and centralised observability to keep the platform secure and maintainable. For guidance on architectural choices for lakehouse-based ML workflows, see AWS notes on navigating lakehouse options with SageMaker (AWS blog: lakehouse and SageMaker).

Elvenite offers ERP integrations, customised data models and managed services for Infor CloudSuite M3 customers, as well as short assessments and one-day pilots to map ERP sources to a lakehouse or mesh design. See an example of our approach in how we built a scalable BI architecture for Intersnack, and explore digital solutions for your industry to see how we tailor services per sector.