Introduction
Lakehouse platforms combine the low-cost, flexible storage of a data lake with the reliability, governance, and performance patterns people expect from a data warehouse. In simple terms, they let teams store many kinds of data in one place and still run fast analytics, reporting, and machine learning workloads without copying data into multiple systems. This matters because organizations want fewer pipelines, fewer duplicate datasets, and faster time from raw data to trusted insights. Common use cases include unified BI and reporting, real-time and batch analytics on the same data, feature stores for machine learning, data sharing across teams, and governed self-service analytics. When you evaluate a lakehouse platform, focus on table formats, query performance, workload isolation, data governance, security controls, interoperability, ingestion and transformation patterns, scalability, operational complexity, and total cost.
Best for: data engineering teams, analytics engineering teams, platform teams, and data leaders who want a unified architecture for analytics and machine learning across large datasets.
Not ideal for: very small teams with simple reporting needs, organizations that only run a single BI workload, or teams that lack data operations maturity and need a fully guided, low-ops warehouse-only approach.
Key Trends in Lakehouse Platforms
- Open table formats becoming central for interoperability and avoiding lock-in
- Separation of storage and compute to scale cost-effectively
- Multi-engine access patterns where different query engines share the same tables
- Stronger governance features like fine-grained access control and lineage
- More real-time ingestion patterns to support operational analytics
- Built-in quality checks, observability, and automated data management tasks
- Broader support for machine learning workflows alongside BI workloads
- Data sharing and collaboration becoming a first-class requirement
- Increased focus on workload isolation and predictable performance
- More emphasis on cost controls, usage visibility, and efficient caching strategies
How We Selected These Tools (Methodology)
- Chose widely recognized lakehouse platforms and foundational lakehouse technologies
- Prioritized support for open table formats and strong interoperability patterns
- Evaluated core capabilities for ingestion, storage, query, governance, and sharing
- Considered scalability across small, mid-sized, and very large datasets
- Looked for strong ecosystem signals including integrations and community activity
- Included both managed and self-managed options to cover different operating models
- Weighted performance, reliability, and operational features that matter in production
- Used a comparative scoring model rather than vendor claims or marketing language
Top 10 Lakehouse Platforms Tools
1) Databricks Lakehouse Platform
A widely adopted lakehouse platform that unifies data engineering, analytics, and machine learning on shared data with strong governance and performance features. Best for organizations that want one platform to support multiple data workloads at scale.
Key Features
- Unified environment for ETL, analytics, and machine learning workflows
- Lakehouse table management and optimization capabilities (format support varies by setup)
- Strong governance and access control features for shared data environments
- Workload scaling patterns for mixed teams and mixed compute needs
- Performance optimization features such as caching and query acceleration patterns
- Collaboration features for notebooks, jobs, and shared datasets
- Integrations with many ingestion and BI tools (varies by ecosystem)
Pros
- Strong end-to-end capability across engineering, analytics, and ML
- Mature ecosystem and broad adoption in many industries
Cons
- Can become complex to operate without good platform discipline
- Costs can rise if usage and compute policies are not controlled
Platforms / Deployment
- Cloud / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Commonly connects to ingestion tools, BI layers, catalogs, and external engines depending on the architecture.
- Data ingestion integrations: Varies / N/A
- BI tool connectivity: Varies / N/A
- Catalog and governance integrations: Varies / N/A
- APIs and automation hooks: Varies / N/A
- Open table format interoperability: Varies / N/A
Support & Community
Strong documentation and enterprise support options; community is large and active, with many practical implementation patterns.
2) Snowflake
A cloud data platform known for strong governance, performance, and ease of use for analytics workloads. Often used in lakehouse-style architectures when organizations combine shared storage patterns with highly managed compute and governance.
Key Features
- Strong SQL analytics experience and workload management patterns
- Data sharing and collaboration features for cross-team access
- Governance features such as access controls and auditing patterns
- Elastic scaling for mixed workloads (based on configuration)
- Support for semi-structured data analytics patterns
- Ecosystem integrations for ingestion, transformation, and BI tools
- Operational features that simplify administration for many teams
Pros
- Strong usability for analytics teams and consistent query experience
- Mature governance and data sharing patterns
Cons
- Architecture choices can increase cost if not monitored closely
- Some lakehouse interoperability depends on specific design patterns
Platforms / Deployment
- Cloud
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Snowflake often sits at the center of analytics stacks with many connectors and tooling options.
- Ingestion and ELT integrations: Varies / N/A
- BI and semantic layer integrations: Varies / N/A
- Data governance tooling connections: Varies / N/A
- APIs and automation: Varies / N/A
- External table and interoperability patterns: Varies / N/A
Support & Community
Strong documentation, many training resources, and broad market adoption; support tiers vary by plan.
3) Google BigQuery
A cloud-native analytics platform designed for large-scale SQL analytics with minimal operational overhead. Often used in lakehouse patterns when combined with open formats and shared storage architectures.
Key Features
- Serverless-style scaling for analytics workloads (usage dependent)
- Strong performance for large analytical queries with managed optimization
- Support for structured and semi-structured analytics patterns
- Integrations with ingestion, transformation, and BI tooling
- Built-in operational features for monitoring and job management
- Strong ecosystem within its cloud environment (varies by setup)
- Workload management patterns for multi-team environments
Pros
- Low operational overhead and strong scalability for analytics
- Good fit for teams that prioritize speed of setup and managed operations
Cons
- Lakehouse interoperability depends on architecture and format choices
- Costs can be hard to predict without governance and usage controls
Platforms / Deployment
- Cloud
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
BigQuery integrates widely with data ingestion and analytics tooling, especially in its ecosystem.
- Ingestion and streaming integrations: Varies / N/A
- BI tool integrations: Varies / N/A
- Catalog and governance integrations: Varies / N/A
- APIs and automation: Varies / N/A
- Open format interoperability patterns: Varies / N/A
Support & Community
Strong documentation and broad community adoption; enterprise support depends on the cloud contract.
4) Amazon Redshift
A data warehouse platform that supports lakehouse-style usage when combined with shared storage patterns and open table formats. Often chosen by organizations that build analytics stacks in the same cloud ecosystem.
Key Features
- Managed data warehouse capabilities for analytical SQL workloads
- Scaling patterns for multi-team analytics environments (configuration dependent)
- Support for querying data in shared storage patterns (architecture dependent)
- Integrations with ingestion and orchestration tools in its ecosystem
- Operational monitoring and performance tuning features (varies)
- Security features suitable for enterprise analytics stacks (varies)
- Compatibility patterns for common BI and transformation tooling
Pros
- Strong fit for teams already standardized on its cloud ecosystem
- Mature operational and performance options for warehouse-style workloads
Cons
- Lakehouse flexibility depends on how you design storage and formats
- Tuning and cost control require strong operational discipline
Platforms / Deployment
- Cloud
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Redshift integrates well with ingestion and analytics tools in its ecosystem and supports broader connectivity patterns.
- Ingestion and orchestration integrations: Varies / N/A
- BI tool connectivity: Varies / N/A
- Governance tooling integrations: Varies / N/A
- APIs and automation hooks: Varies / N/A
- Shared storage query patterns: Varies / N/A
Support & Community
Large community and extensive documentation; enterprise support depends on the cloud support agreement.
5) Microsoft Fabric
A unified analytics platform designed to bring ingestion, transformation, storage, and analytics together. Often used as a lakehouse-style solution for organizations that prefer an integrated experience with strong BI alignment.
Key Features
- Integrated environment for data engineering and analytics workflows
- Lakehouse-style storage and analytics patterns (architecture dependent)
- Strong alignment with business reporting and semantic modeling workflows
- Governance and security patterns for enterprise data access (varies)
- Orchestration and pipeline features for managed data flows
- Workload collaboration features for cross-functional teams
- Ecosystem integrations across its platform tools (varies)
Pros
- Integrated experience that can reduce tool sprawl for many teams
- Strong fit for organizations aligned with its BI and analytics ecosystem
Cons
- Platform maturity and feature depth can vary by workload area
- Some interoperability patterns depend on specific platform design choices
Platforms / Deployment
- Cloud
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Fabric commonly integrates with BI layers, ingestion tools, and governance patterns in its ecosystem.
- BI and semantic model ecosystem: Varies / N/A
- Data ingestion connectors: Varies / N/A
- Governance integrations: Varies / N/A
- APIs and automation: Varies / N/A
- Open format access patterns: Varies / N/A
Support & Community
Strong enterprise backing and growing community; support options depend on licensing and agreements.
6) Dremio
A lakehouse query and data acceleration platform designed for fast analytics on data lake storage. Often used by teams that want open interoperability and multiple engine access to shared datasets.
Key Features
- SQL query layer for data lakes with acceleration features (setup dependent)
- Supports open table formats and shared dataset access patterns
- Workload management features for concurrent analytics usage
- Semantic layer style features for curated datasets (varies by use)
- Integrations with BI tools and external compute engines (varies)
- Helps reduce data copies by querying data in place (architecture dependent)
- Performance optimization patterns through reflections or caching features (varies)
Pros
- Strong for open lakehouse architectures with multi-tool access
- Can improve query performance on lake data without heavy duplication
Cons
- Requires careful architecture planning to get consistent performance
- Some advanced governance needs depend on surrounding ecosystem tools
Platforms / Deployment
- Cloud / Self-hosted / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Dremio typically integrates with data lake storage, BI tools, and open table ecosystems.
- BI tool integrations: Varies / N/A
- Storage integrations: Varies / N/A
- Open table format interoperability: Varies / N/A
- APIs and connectors: Varies / N/A
- Orchestration tool integrations: Varies / N/A
Support & Community
Active community and documentation; enterprise support tiers vary by plan.
7) Starburst
A platform built around distributed SQL querying across multiple data sources, commonly used in lakehouse architectures for unified access to data in lakes and warehouses. Best for teams that want federated analytics and open ecosystem alignment.
Key Features
- Distributed SQL engine patterns for querying data across systems
- Strong fit for data lake query workloads with open table formats (setup dependent)
- Federated query capability for combining multiple data sources
- Workload scaling features for multi-team analytics usage
- Integrations with BI tools and data catalogs (varies)
- Governance patterns through policies and connectors (varies)
- Extensible connector ecosystem for many storage and databases
Pros
- Strong for federated analytics across multiple systems
- Fits open architectures where interoperability is important
Cons
- Performance tuning requires architecture discipline and good data layout
- Governance depth can depend on external catalog and policy tooling
Platforms / Deployment
- Cloud / Self-hosted / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Starburst is commonly used with catalogs, lake storage, and BI layers through a connector-driven architecture.
- Connector ecosystem for storage and databases: Varies / N/A
- BI integrations: Varies / N/A
- Catalog and governance integrations: Varies / N/A
- APIs and automation: Varies / N/A
- Open table formats access: Varies / N/A
Support & Community
Strong documentation and a growing community; enterprise support options vary by agreement.
8) Cloudera Data Platform
An enterprise data platform that supports lakehouse-like architectures through integrated storage, governance, and analytics patterns. Often used by organizations with strong security requirements and established enterprise data operations.
Key Features
- Integrated data services for ingestion, processing, and analytics
- Governance and security tooling suitable for enterprise controls (setup dependent)
- Supports hybrid operating models across environments (architecture dependent)
- Tools for data engineering and operational reliability (varies)
- Workload management for shared analytics environments (varies)
- Integration patterns for open table formats and engines (varies)
- Strong focus on enterprise operations and data lifecycle management
Pros
- Strong for enterprises needing governance, control, and hybrid operations
- Mature platform approach for large organizations with complex needs
Cons
- Can be complex to operate without experienced platform teams
- Some capabilities may overlap with tools you already have in the stack
Platforms / Deployment
- Cloud / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Not publicly stated
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Cloudera commonly integrates through enterprise connectors, governance tooling, and engine interoperability patterns.
- Ingestion and processing integrations: Varies / N/A
- BI and analytics tool connectivity: Varies / N/A
- Catalog and policy tooling: Varies / N/A
- Open ecosystem integrations: Varies / N/A
- APIs and automation: Varies / N/A
Support & Community
Enterprise-grade support options and established documentation; community strength varies by product area.
9) Apache Iceberg
An open table format and table management layer used to build lakehouse architectures with multiple query engines. Best for teams that want open interoperability and strong table reliability features.
Key Features
- Open table format designed for reliable analytics on lake storage
- Schema evolution patterns for long-lived datasets
- Partition evolution to improve performance without constant rewrites
- ACID-style table behaviors through format design patterns (implementation dependent)
- Snapshot and time travel capabilities (engine dependent)
- Multi-engine access patterns for shared data tables
- Works with many storage systems and compute engines (varies)
Pros
- Strong interoperability and avoids heavy platform lock-in
- Table reliability features support robust analytics pipelines
Cons
- Requires engine and catalog decisions to become a full platform
- Operational setup varies and can be complex across multiple tools
Platforms / Deployment
- Self-hosted / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / N/A
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Iceberg is a foundation layer that integrates through engines, catalogs, and storage ecosystems.
- Query engine support: Varies / N/A
- Catalog integrations: Varies / N/A
- Storage integrations: Varies / N/A
- Orchestration tool integrations: Varies / N/A
- APIs and tooling: Varies / N/A
Support & Community
Strong open-source community and growing ecosystem; enterprise support depends on vendors providing managed distributions.
10) Delta Lake
An open table format and storage layer approach used to build lakehouse architectures with reliable table behaviors. Commonly used in platforms that support transactional analytics patterns on lake storage.
Key Features
- Table reliability features designed for analytics workloads on lake storage
- ACID-style behaviors through transaction log patterns (implementation dependent)
- Schema enforcement and evolution patterns for cleaner pipelines
- Time travel features for auditing and recovery workflows (engine dependent)
- Performance optimization patterns through data layout strategies (varies)
- Works with multiple compute engines depending on ecosystem setup
- Useful for building a consistent table layer for mixed workloads
Pros
- Strong table reliability and recovery patterns for analytics pipelines
- Widely used in lakehouse implementations and ecosystem tooling
Cons
- Full value depends on surrounding platform and operational tooling
- Interoperability varies based on engine support and catalog choices
Platforms / Deployment
- Self-hosted / Hybrid
Security & Compliance
- SSO/SAML, MFA, encryption, audit logs, RBAC: Varies / N/A
- SOC 2, ISO 27001, GDPR, HIPAA: Not publicly stated
Integrations & Ecosystem
Delta Lake integrates through compute engines, catalogs, and storage layers used in lakehouse stacks.
- Engine support and connectors: Varies / N/A
- Catalog and governance tooling: Varies / N/A
- Orchestration and pipeline tools: Varies / N/A
- Storage ecosystem compatibility: Varies / N/A
- APIs and automation patterns: Varies / N/A
Support & Community
Strong community and broad adoption; enterprise support depends on the platform and vendors you run it with.
Comparison Table (Top 10)
| Tool Name | Best For | Platform(s) Supported | Deployment (Cloud/Self-hosted/Hybrid) | Standout Feature | Public Rating |
|---|---|---|---|---|---|
| Databricks Lakehouse Platform | Unified engineering, analytics, and ML | Varies / N/A | Cloud / Hybrid | End-to-end lakehouse workflows | N/A |
| Snowflake | Governed analytics and data sharing | Varies / N/A | Cloud | Strong sharing and usability | N/A |
| Google BigQuery | Managed large-scale analytics | Varies / N/A | Cloud | Low-ops scaling for SQL analytics | N/A |
| Amazon Redshift | Warehouse-led lakehouse patterns | Varies / N/A | Cloud | Ecosystem-aligned analytics stack | N/A |
| Microsoft Fabric | Integrated analytics with BI alignment | Varies / N/A | Cloud | Unified experience across workloads | N/A |
| Dremio | Fast analytics on lake storage | Varies / N/A | Cloud / Self-hosted / Hybrid | Acceleration for lake queries | N/A |
| Starburst | Federated analytics across sources | Varies / N/A | Cloud / Self-hosted / Hybrid | Distributed SQL across systems | N/A |
| Cloudera Data Platform | Enterprise governance and hybrid ops | Varies / N/A | Cloud / Hybrid | Enterprise operations and controls | N/A |
| Apache Iceberg | Open table format foundation | Varies / N/A | Self-hosted / Hybrid | Reliable open table layer | N/A |
| Delta Lake | Transactional table layer on lakes | Varies / N/A | Self-hosted / Hybrid | Table reliability and time travel | N/A |
Evaluation & Scoring of Lakehouse Platforms
Weights: Core features 25%, Ease 15%, Integrations 15%, Security 10%, Performance 10%, Support 10%, Value 15%.
| Tool Name | Core (25%) | Ease (15%) | Integrations (15%) | Security (10%) | Performance (10%) | Support (10%) | Value (15%) | Weighted Total (0–10) |
|---|---|---|---|---|---|---|---|---|
| Databricks Lakehouse Platform | 9.0 | 7.5 | 9.0 | 7.0 | 8.5 | 8.0 | 7.0 | 8.13 |
| Snowflake | 8.5 | 8.5 | 8.5 | 7.5 | 8.5 | 8.0 | 7.0 | 8.15 |
| Google BigQuery | 8.0 | 8.5 | 8.0 | 7.0 | 8.5 | 8.0 | 7.5 | 8.02 |
| Amazon Redshift | 7.8 | 7.5 | 8.0 | 7.0 | 8.0 | 7.5 | 7.0 | 7.63 |
| Microsoft Fabric | 7.8 | 8.0 | 8.0 | 7.0 | 7.5 | 7.5 | 7.5 | 7.75 |
| Dremio | 7.8 | 7.5 | 8.2 | 6.5 | 8.0 | 7.2 | 7.8 | 7.72 |
| Starburst | 7.8 | 7.2 | 8.5 | 6.8 | 8.0 | 7.2 | 7.2 | 7.66 |
| Cloudera Data Platform | 7.8 | 6.8 | 7.8 | 7.5 | 7.8 | 7.5 | 6.8 | 7.43 |
| Apache Iceberg | 7.5 | 6.8 | 8.5 | 6.2 | 7.8 | 7.0 | 9.0 | 7.63 |
| Delta Lake | 7.5 | 7.0 | 8.0 | 6.2 | 7.8 | 7.0 | 8.5 | 7.60 |
How to interpret the scores:
- The totals compare options within this list, not the entire market.
- Higher scores generally indicate broader fit across more scenarios.
- Open table formats can score high on value, but may require more operational work.
- Managed platforms can score high on ease, but cost control becomes essential.
- Use the scoring as a shortlist guide, then validate with a pilot using your real workloads.
Which Lakehouse Platform Tool Is Right for You?
Solo / Freelancer
If you are learning or building small projects, start with an approach that keeps operations simple. Open table formats like Apache Iceberg or Delta Lake can work, but they usually need extra tooling choices. For many individuals, a managed analytics service can be simpler, but cost can be unpredictable without controls.
SMB
Small teams typically need fast time-to-value. Microsoft Fabric can fit well if your reporting and BI workflows are central. Google BigQuery can be strong when you want minimal operational overhead. If you need a platform that supports engineering plus analytics plus ML, Databricks Lakehouse Platform can be a good fit, but you should set strict usage policies early.
Mid-Market
Mid-market teams often run mixed workloads and need predictable performance. Snowflake is often strong for governed analytics and sharing. Databricks Lakehouse Platform is strong when engineering and ML are equally important. If you want open interoperability and multiple engines, Dremio or Starburst can be useful, but only if you invest in table design and governance discipline.
Enterprise
Enterprises typically need governance, workload isolation, and repeatability. Cloudera Data Platform can fit when hybrid operations and enterprise controls are key. Databricks Lakehouse Platform and Snowflake are common anchors for large-scale analytics stacks, but you must plan for cost governance, access policies, and a clear operating model.
Budget vs Premium
Budget-focused architectures often start with open table formats like Apache Iceberg and Delta Lake, but they require careful engine, catalog, and operations decisions. Premium approaches lean toward managed platforms that reduce operational burden but require strong cost controls and usage governance.
Feature Depth vs Ease of Use
If you prioritize ease, managed options like Google BigQuery, Snowflake, and Microsoft Fabric can reduce operational friction. If you prioritize flexibility and ecosystem freedom, open table formats and query layers like Dremio and Starburst can be compelling, but they require more architecture effort.
Integrations & Scalability
If you rely on multiple query engines and want shared tables, prioritize open table formats and interoperability. If you need scale across many teams, focus on workload isolation, governance, and monitoring. For large scale, also verify performance on your real join patterns, file sizes, partition strategy, and concurrency.
Security & Compliance Needs
Security expectations usually include strong access control, auditing, encryption, and identity integration. Where details are not publicly stated, treat them as unknown and validate through formal vendor review. For open table formats, security and governance often come from your surrounding catalog, storage controls, and access management layer.
Frequently Asked Questions (FAQs)
1. What is a lakehouse platform in simple terms?
It is a way to store large datasets like a lake but still manage and query them with warehouse-style reliability, performance, and governance patterns.
2. Do I need an open table format for a lakehouse?
Not always, but open table formats help interoperability and reduce lock-in. They also improve reliability features like schema evolution and snapshot-based access patterns.
3. Which is easier to run: a managed platform or a build-your-own lakehouse?
Managed platforms are usually easier to operate day-to-day, while build-your-own approaches can be more flexible but require more engineering and governance effort.
4. What is the most common mistake teams make with lakehouse projects?
They skip governance and data modeling discipline, then performance and cost become unpredictable. Another common issue is copying data across too many systems.
5. How do lakehouse platforms control performance for many users?
They rely on workload isolation patterns, caching, optimized table layouts, and compute scaling approaches. The exact methods vary by tool and architecture.
6. Is a lakehouse only for big data teams?
No, but it helps most when you have multiple data consumers, multiple workloads, and the need to manage many datasets consistently.
7. How do I reduce cost in a lakehouse environment?
Standardize table formats, reduce duplicate copies, enforce usage policies, monitor heavy queries, and optimize data layout. Cost control must be part of daily operations.
8. Can I use multiple query engines on the same data?
Yes, that is a common goal of lakehouse designs. However, success depends on table formats, catalogs, and consistent data layout and governance rules.
9. What should I validate in a pilot before choosing a platform?
Test ingestion, transformation, governance controls, query concurrency, key dashboards, ML feature workloads, and total cost under realistic usage.
10. How do I decide between Databricks Lakehouse Platform and Snowflake?
If you need a unified platform spanning engineering, analytics, and ML, Databricks Lakehouse Platform is often strong. If you prioritize governed analytics, sharing, and a consistent SQL experience, Snowflake can be a strong fit. The best choice depends on your workload mix and operating model.
Conclusion
Lakehouse platforms are a practical answer to a common data problem: teams want one trusted place for data that supports both analytics and machine learning without endless copies and fragile pipelines. The right choice depends on your workload mix, operating maturity, governance needs, and cost tolerance. Managed platforms like Snowflake, Google BigQuery, and Microsoft Fabric can reduce operational effort, but you must actively manage usage and spending. Platforms like Databricks Lakehouse Platform can deliver strong end-to-end capability for engineering, analytics, and ML, but require disciplined platform practices. Open table foundations like Apache Iceberg and Delta Lake can improve interoperability and long-term flexibility, but need stronger architecture decisions around engines, catalogs, and governance. Shortlist two or three options, run a small pilot, and validate performance, governance, and cost before standardizing.