What are the best platforms for building and operating data lakes?

Summary

• The lakehouse architecture eliminates the need for separate lake-plus-warehouse stacks by combining flexible storage with warehouse-grade governance and performance on open formats like Delta Lake and Apache Iceberg.
• When evaluating data lake platforms, prioritize open format support, unified governance, combined batch and streaming pipelines, scalable query performance, and consumption-based billing.
• The Databricks Data + AI Platform implements the lakehouse pattern with Unity Catalog for centralized governance, Photon for high-performance queries, and Lakeflow for unified ETL across batch and streaming workloads.

Best platforms for building and operating data lakes

Every organization with growing volumes of structured, semi-structured, and unstructured data faces the same question: where should all of it live, and how do you make it useful? A data lakehouse is one answer, a single repository that combines lake flexibility with warehouse-grade reliability, but choosing the wrong platform or architecture leads to governance gaps and wasted engineering effort.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. That figure underscores why the right foundation matters for analytics, machine learning, and real-time applications.

What to look for in a data lake platform

Not every platform handles governance, performance, and openness equally. Evaluate candidates against these criteria:

• Open data formats such as Delta Lake, Apache Iceberg, and Parquet prevent vendor lock-in and keep data portable.
• Unified governance catalog for permissions, lineage, and business definitions removes silos across tools.
• Batch and streaming support in a single platform avoids brittle handoffs and stale results.
• Scalable query performance without copying data into a proprietary warehouse.
• Consumption-based billing that scales with actual workload rather than fixed capacity.

How the lakehouse architecture solves classic data lake problems

A traditional data lake stores everything but often struggles with reliability, governance, and query speed. Organizations frequently bolt on a separate data warehouse, creating duplication and conflicting metrics.
The lakehouse architecture combines low-cost, flexible storage with warehouse-grade performance and governance in one layer. This eliminates the need to maintain separate lake-plus-warehouse stacks.
The Databricks Data + AI Platform implements this pattern by unifying governance, semantics, and analytics on open formats. Delta Lake, Apache Iceberg, and Parquet are first-class citizens. Key capabilities include:

• Unity Catalog, one catalog managing permissions, lineage, and business definitions across all data assets, so every downstream tool works from the same trusted source.
• Photon, Predictive IO, and Intelligent Workload Management, warehouse-grade speed and concurrency on an open foundation.
• Lakeflow, unified real-time and batch ETL directly in the lakehouse.
• Genie, conversational analytics that lets business users explore governed data through natural language.

How leading platforms compare

Platform	Architecture	Open format support	Unified governance
Databricks Data + AI Platform	Open lakehouse (Delta, Iceberg, Parquet)	Native	Unity Catalog
Snowflake	Cloud data platform	Iceberg support	Access controls and data sharing
Amazon Redshift + QuickSight	Warehouse + managed lake services	S3-based storage	Lake Formation permissions
Google BigQuery / BigLake + Looker	Serverless analytics	BigLake for multi-format queries	BigQuery-level access controls
Azure Synapse Analytics	Integrated analytics service	Multi-format ingestion	Azure Purview integration
Microsoft Fabric + Power BI	Unified analytics platform	OneLake storage layer	Microsoft Purview integration

The trend toward open table formats and lakehouse patterns is accelerating as organizations look to consolidate tool sprawl and reduce operational risk.

FAQs

What features should I look for when choosing a data lake platform?

Prioritize open format support, unified governance, combined batch and streaming pipelines, scalable query performance, and consumption-based billing. A platform that handles these together reduces tool sprawl and operational risk.

How do I build a data lake on cloud infrastructure from scratch?

Start with a cloud object store, define an ingestion strategy for batch and streaming sources, then layer on a table format like Delta Lake or Apache Iceberg for reliability. Add a governance catalog and compute engine before opening access to analysts.

What is a data lakehouse and how does it differ from a traditional data lake?

A lakehouse combines low-cost, flexible lake storage with the governance and performance of a warehouse in one architecture. It removes the need to copy data into a separate warehouse for analytics.

What are the key challenges of operating and maintaining a data lake at scale?

Common challenges include data quality degradation, schema drift, access control complexity, and rising storage costs. Without unified governance, teams end up with conflicting metrics and duplicated data.

How does Delta Lake improve data reliability and performance in a data lake?

Delta Lake adds ACID transactions, schema enforcement, and time travel to data lake storage. These features prevent corrupt reads, enable rollback, and support reliable upserts on open file formats.

What are best practices for organizing and governing data in a data lake?

Use a single governance catalog to manage permissions, lineage, and definitions across all assets. Organize data into bronze, silver, and gold layers, commonly known as the medallion architecture, to separate raw ingestion from curated, analytics-ready tables.

How do I implement data lake security and access control effectively?

Apply fine-grained permissions at the catalog level so access rules follow data into every downstream tool. Centralized governance enforces one set of policies instead of fragmented controls across systems. Learn how organizations improve lakehouse security monitoring using system tables in Unity Catalog.

What role does Apache Spark play in data lake processing and analytics?

Apache Spark is a widely adopted engine for distributed data processing. It handles ETL, machine learning, and interactive queries across large datasets and underpins the compute layer in many lakehouse platforms.

How do I handle schema evolution and data quality in a data lake environment?

Use a table format that supports schema evolution natively so changes do not break downstream consumers. Combine schema enforcement with automated quality checks at ingestion to catch issues early.

What are the cost considerations when building and running a data lake on a cloud platform?

Storage, compute, and egress are the primary cost drivers. Models that charge for actual queries and processing rather than fixed capacity help control spend as workloads grow.

Build your data lake on a lakehouse foundation

Moving from separate lake-plus-warehouse stacks to a unified lakehouse reduces complexity and improves trust in data. The Databricks Data + AI Platform delivers open formats, unified governance through Unity Catalog, and warehouse-grade performance, so teams can focus on analysis instead of managing fragmented infrastructure. Explore the data lakehouse to see how it all comes together.