Data lake tools: Top 8 vendors & how to evaluate them

Data lakes play a critical role in modern data architecture, enabling flexible storage and analysis of diverse data types. As demand for scalable, cloud-native infrastructure continues to rise, the global data lake market was estimated at $13.62 billion in 2023 and is projected to grow at a CAGR of 23.8% from 2024 to 2030.

With that growth comes a rapidly expanding landscape of tools—each offering different approaches to ingestion, storage, governance, and analytics. This article explores eight leading data lake solutions, highlighting their key strengths and how to evaluate them based on integration, scalability, governance, cost, and alignment with your data needs. It also looks at how event data pipelines like RudderStack can enhance your data lake by improving data quality and operational efficiency.

Main takeaways from this article:

• Data lakes provide flexible storage for diverse data types with schema-on-read capabilities
• Leading data lake vendors offer varying approaches to architecture, from cloud-native options to hybrid solutions
• Key evaluation criteria include scalability, integration capabilities, data governance features, and cost structure
• The right data lake solution must align with your existing infrastructure, use cases, and team capabilities
• Pairing your data lake with an effective event data pipeline ensures clean, structured data flows

Understanding data lake architecture

Before choosing a data lake tool, it’s useful to understand the architecture these tools are built to support. A data lake is designed to store large volumes of diverse data—structured, semi-structured, and unstructured—without forcing it into a fixed schema upfront. This flexibility allows organizations to work with raw data as needs evolve, making it easier to support analytics, machine learning, and operational use cases from a single source.

While each tool approaches implementation differently, most modern data lake architectures include key capabilities such as:

• Cloud-based object storage that scales with data volume and supports long-term retention
• Multi-source ingestion to bring in data from APIs, databases, applications, and event streams
• Compatibility with multiple query engines, enabling different teams to access the same data in different ways
• Metadata and access control layers to support organization, discoverability, and security
• Integration with downstream platforms for reporting, AI/ML, and operational activation

The tools in the following list are not just storage solutions—they’re built to help teams make data usable and accessible. Whether your priority is real-time performance, strong governance, or support for advanced analytics, these platforms reflect the current state of what data lake technology can offer.

Eight best data lake tools to consider in 2025

The data lake tools landscape continues to evolve rapidly, with solutions offering varying approaches to architecture, deployment, and functionality. Below are eight leading data lake solutions.

1. Amazon S3 (with AWS Lake Formation)

Amazon Simple Storage Service (S3) forms the foundation of many data lakes, with AWS Lake Formation adding capabilities that transform basic object storage into a comprehensive data lake solution. Its key features and advantages are as follows:

• Serverless architecture with virtually unlimited capacity to store data
• Integrated security and governance through AWS Lake Formation, providing fine-grained access controls
• Native integration with the broader AWS analytics ecosystem
• Cost-effective tiered storage options for optimizing expenses as data ages
• Mature ecosystem of third-party tools and connectors

Amazon's solution excels for organizations already invested in the AWS ecosystem. It offers scalability and rich integration capabilities suitable for small teams and enterprise deployments.

2. Microsoft Azure Data Lake Storage

Azure Data Lake Storage Gen2 combines the scalability of blob storage with hierarchical file system capabilities specifically designed for big data analytics. Here are the main features and benefits:

• Hierarchical namespace enabling efficient organization and operation on directories and files
• Seamless integration with Azure Synapse Analytics, Databricks, and Power BI
• Enhanced security with Azure Active Directory integration and role-based access control
• Optimized performance through data tiering and throughput scaling
• Comprehensive compliance capabilities meeting major regulatory standards

Microsoft's data lake solution provides particular value for organizations utilizing other Azure services. It offers strong enterprise features and governance capabilities suited to regulated industries.

3. Google Cloud Storage (with BigLake)

Google Cloud Storage is the foundation for data lakes in the Google Cloud ecosystem, while BigLake provides a unified interface for accessing data across storage systems. These are its top features and strengths:

• Global availability with automatic replication and edge caching
• Fine-grained access controls through Identity and Access Management (IAM)
• Strong data consistency guarantees for complex analytical workloads
• BigLake integration provides uniform governance across diverse data sources
• Seamless connectivity with BigQuery, Dataproc, and machine learning tools

Google's approach emphasizes simplicity, performance, and integration with its analytics services, making it particularly appealing for organizations requiring advanced machine learning capabilities.

4. Databricks lakehouse (built on Delta Lake)

Databricks offers a unified “lakehouse” architecture that combines the scalability of a data lake with the performance and reliability of a data warehouse. At the core of this architecture is Delta Lake, Databricks’ open-source storage layer, which brings enterprise-grade reliability to data lakes.

Databricks offers a "lakehouse" architecture that combines the best elements of data lakes and data warehouses, built on its Delta Lake open-source storage layer.

Key features and benefits include:

• ACID transaction support for reliable data operations
• Schema enforcement and evolution to ensure consistency
• Unified batch and streaming data processing for real-time use cases
• Versioning and time travel capabilities for auditability and reproducibility
• Optimized performance through intelligent indexing and caching
• Advanced collaborative features for data engineering, science, and analytics teams

Databricks delivers substantial value for organizations focused on scalable data science and machine learning workflows. It offers a comprehensive environment for managing the entire data lifecycle with reliability, flexibility, and speed.

5. Snowflake

Snowflake is a cloud-based data warehousing platform that supports data lake capabilities, bridging lakes and data warehouses. Here’s what it can do and why it’s helpful:

• Separation of storage and computing for independent resource scaling
• Support for structured and semi-structured data with automatic schema detection
• Zero-copy cloning for efficient development and testing
• Time travel and secure data-sharing capabilities
• Straightforward pricing model based on actual usage

Snowflake offers particular advantages for organizations seeking to blend data warehouse performance with data lake flexibility, though with a different architectural approach than traditional data lake implementations.

6. Cloudera Data Platform

Cloudera Data Platform (CDP) provides a comprehensive data management solution with robust data lake capabilities available in both cloud and on-premises deployments. Below are the main points that make it stand out:

• Hybrid cloud flexibility supporting multi-cloud and on-premises environments
• Unified security and governance through Shared Data Experience (SDX)
• Integrated streaming data processing with Cloudera DataFlow
• Enterprise-grade security and services
• Comprehensive data lifecycle management from ingestion to archiving

Cloudera's solution is particularly suited to organizations with complex regulatory requirements or those maintaining significant on-premises infrastructure alongside cloud deployments.

7. Dremio

Dremio provides a data lake engine through its proprietary technologies that accelerates queries against data lakes. Here are the top reasons to use it:

• Query acceleration through Data Reflections technology
• Semantic layer enabling self-service analytics for data analysts
• Support for multiple data sources beyond the data lake
• Cloud-native architecture optimized for performance and cost
• SQL interface for familiar access patterns

Dremio delivers exceptional value for organizations prioritizing query performance and business user accessibility for their data lake implementations.

8. Teradata VantageCloud Lake

Teradata VantageCloud Lake is the company’s next-generation, cloud-native platform designed for modern analytics and data lakehouse workloads. It combines Teradata’s proven analytics engine with flexible, object store–based storage and decoupled compute, enabling scalable performance across structured and unstructured data. Here’s what it can do and why it’s helpful:

• Integration of structured and unstructured data analysis
• Advanced analytics functions, including machine learning
• Enterprise-grade scalability and reliability
• Comprehensive data governance features
• Hybrid deployment options across cloud and on-premises environments

VantageCloud Lake is ideal for large enterprises modernizing their data infrastructure, particularly those with existing Teradata investments, offering a path to adopt lakehouse architecture without rebuilding from scratch.

Core features to look for in data lake solutions

Choosing the right data lake tool means looking beyond storage. The features listed below are essential for building a solution that can scale, stay secure, and support the way your teams work—now and as your data needs grow.

Architecture & scalability

The design of a data lake plays a key role in how well it can grow with your business and adjust to new needs. A good solution should handle large amounts of data, manage different types of analysis, and use cloud services efficiently.

Integration capabilities

To be truly useful, a data lake should easily connect to both data sources and analysis tools. It needs strong data intake systems for both real-time and batch data, standard connectors like APIs and JDBC/ODBC, and built-in links to standard business systems.

Data governance & security

Strong governance and security are necessary since data lakes often hold sensitive information. This includes detailed access control at every level (like database, table, or column), encryption of data both when stored and when moving, and tracking of who accessed what and when.

Performance & querying

The practical utility of a data lake depends on its ability to query and analyze data efficiently. To improve performance, it uses techniques like query optimization, indexing, partitioning, and caching to speed up frequent searches.

Cost structure

The cost of using a data lake can vary greatly depending on the solution. Key factors include how storage is priced, whether data can be compressed to save space, and how much it costs to use computing resources and scale them.

Advanced analytics

Modern data lakes should offer advanced analytics features to handle complex tasks. These include training and using machine learning models, supporting popular data science tools and languages, enabling real-time analysis, and providing graph and geospatial analytics capabilities.

How to evaluate data lake vendors for your needs

To make an informed decision and evaluate data lake vendors, consider the following key factors that will fit within your organization.

Align with your cloud infrastructure

Your existing cloud strategy is crucial when selecting a data lake. Cloud-native solutions like AWS Lake Formation, Azure Data Lake Storage, or Google Cloud Storage offer smooth integration with their respective ecosystems, helping to reduce complexity and cost. However, cloud-agnostic options provide more flexibility for multi-cloud or hybrid deployments.

Match to your use cases

When choosing a data lake solution, focus on your specific needs. For real-time analytics, prioritize solutions with strong streaming features. If you're developing machine learning models, ensure easy access to large datasets. For regulatory compliance, select options with robust governance tools.

Assess your team's maturity and resources

Highly skilled data engineering teams may prefer DIY solutions with open-source components, while smaller teams benefit from managed services that reduce operational complexity. Organizations new to data lakes may prioritize simplicity and guided implementation, whereas enterprise teams might need advanced administration and governance tools.

Verify compatibility with event pipelines

Ensuring compatibility between your data lake and event data upstream pipelines is crucial for maintaining data quality. The data flowing into your lake must be clean, structured, and consistent for practical analysis. Event data pipelines play a vital role by providing the proper structure, metadata, and quality controls.

How RudderStack powers your data management strategy

RudderStack improves data lake implementations by providing a reliable, secure event data pipeline that delivers quality data to your storage layer. As an event stream processor, RudderStack helps businesses collect and use customer data with full privacy controls while addressing several critical data lake challenges:

1. Data quality assurance: RudderStack validates and structures event data before lake ingestion, reducing downstream processing requirements.
2. Real-time capabilities: Beyond batch processing, RudderStack enables real-time event streaming into data lakes, supporting time-sensitive use cases.
3. Comprehensive data collection: RudderStack captures data from web, mobile, server-side, and cloud sources with minimal development effort.
4. Privacy and compliance: Built-in controls help maintain regulatory compliance and data protection standards throughout the data lifecycle.
5. Reduced integration complexity: RudderStack's pre-built connectors simplify bringing data from multiple sources into your lake environment.

Build a future-ready data lake stack

A well-architected data lake should support a range of use cases—from reporting to real-time analytics—while maintaining control over data quality and access. Achieving this requires more than storage; it depends on reliable data ingestion, strong governance, and the ability to scale with your organization.

RudderStack helps teams meet these demands by delivering clean, privacy-conscious event data in real time. When paired with a modern data lake, it ensures your analytics environment is both accurate and responsive.

If you're building or modernizing your data infrastructure, explore how RudderStack can support your goals. Try it for free or request a demo today.

FAQs

What problems does a data lake solve?

Data lakes provide cost-effective storage for vast amounts of diverse data in native format, eliminating the need for upfront schema design. This flexibility preserves potentially valuable data while deferring processing decisions.

How are data warehouses different from data lakes?

Data warehouses store structured, processed data optimized for specific analytical queries using predefined schemas (schema-on-write), focusing on business metrics and dimensional analysis. Data lakes store raw, unprocessed data in native format, applying schema only when data is read (schema-on-read).

Is Snowflake a data lake or warehouse?

Snowflake began as a cloud data warehouse but has evolved to incorporate data lake functionality, positioning itself in the emerging "lakehouse" category. It maintains warehouse performance and structure while handling semi-structured data, separating storage from computing, and integrating with external data sources like lakes.

What is the difference between ETL and data lake?

ELT (Extract, Load, Transform) is a process that extracts data from sources, transforms it according to business rules, and loads it into a target destination, typically involving substantial upfront structuring. A data lake is a storage repository holding raw, unprocessed data in native format until needed for analysis, which modern architectures often implement.