What is a data lake and why it matters for enterprise analytics
A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its native format. It supports analytics, machine learning, and large-scale data processing without requiring a predefined schema at ingestion. Unlike traditional data warehouses, which typically enforce a schema before data is written (schema-on-write), data lakes allow organizations to store data first and apply structure when querying it (schema-on-read).
A data lakehouse builds on the flexibility of a data lake by adding capabilities such as transactional consistency, data management, and governance—combining scalable, low-cost storage with features traditionally associated with data warehouses.
Regardless of which architecture you choose, telemetry management remains an important consideration. Cribl works alongside data lakes, warehouses, and lakehouses to route and shape telemetry for different destinations, helping organizations control data costs, maintain flexibility, and make data more accessible for analytics and AI.
Best Data Lake platform picks
We evaluated the platforms in this guide across six criteria: cloud alignment and ecosystem fit, native AI and ML integration, governance and compliance, cost predictability and storage optimization, performance and scalability for unstructured data, and hybrid and multi-cloud support.
The TL;DR picks below highlight where each platform is best suited to different enterprise needs. Use them as a starting point to narrow your options, then explore the full profiles to compare capabilities, trade-offs, and fit for your environment.
TL;DR Picks
Best for telemetry routing and cost optimization: Cribl
Best for AI- and ML-intensive data engineering: Databricks Lakehouse
Best for concurrent SQL analytics and cross-team data sharing: Snowflake
Best for AWS-first enterprises: Amazon S3 and AWS Lake Formation
Best for multi-cloud analytics on Google Cloud: Google Cloud Storage and BigLake
Best for Microsoft-centric organizations: Azure Data Lake Storage (ADLS)
Best for distributed on-premises storage: Apache Hadoop Distributed File System (HDFS)
Best for hybrid and air-gapped deployments: Cloudera Data Platform (CDP)
Best for integrated data and AI in regulated environments: IBM watsonx.data and IBM Cloud Pak
Best for open-source object storage: MinIO
Disclaimer: These “best” picks are editorial recommendations based on each platform’s capabilities and suitability for specific use cases. They are intended as a starting point for evaluation, not definitive rankings or a one-size-fits-all assessment. The right choice depends on your organization’s requirements, existing infrastructure, and priorities.
1. Cribl Lake
Where Cribl Fits in Modern Data Lake Management
Cribl is the data management layer that helps organizations control how telemetry moves across their data architecture—routing it to the right destination, shaping it for its intended use, and managing costs. As the AI Platform for Telemetry, Cribl sits between data sources and analytics platforms, giving teams greater control over what data they collect, how they process it, and where they send it.
Cribl Lake provides managed, cost-effective storage built for IT and security telemetry. Teams can retain telemetry for investigation and analysis without relying exclusively on more expensive analytics or SIEM storage. Combined with Cribl Search, Lake helps make retained data accessible for search and investigation by analysts and AI-powered workflows.
What sets Cribl apart
AI-ready telemetry: Route and shape telemetry to support analytics, investigations, and AI-driven workflows across IT and security operations.
Data portability and choice: Move telemetry across destinations, including Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage (GCS), using open formats where supported—without forcing your entire data architecture into a single vendor’s ecosystem.
Broad telemetry support: Collect and process logs, metrics, traces, and events from a wide range of sources, using integrations and configurable pipelines to fit your environment.
Cost optimization: Send high-value data to premium analytics platforms while routing other telemetry to lower-cost storage or alternative destinations based on its value and intended use.
Hybrid and multi-cloud flexibility: Support telemetry pipelines across cloud and self-managed environments, with routing and processing policies tailored to your architecture.
Best for: Enterprise IT and security teams managing high volumes of telemetry who need flexible routing, cost-effective long-term retention, and greater freedom to choose where data is stored, searched, and analyzed—without tying their entire telemetry strategy to a single analytics vendor.
2. Amazon S3 and Lake Formation
Features and capabilities
Amazon S3 is one of the most used object storage systems for data lake architectures, according to industry comparisons. It offers multiple storage tiers such as Standard, Intelligent Tiering, and Glacier, with eleven nines of durability.
AWS Lake Formation simplifies secure data lake management with centralized governance. It integrates natively with AWS services such as Athena, Redshift Spectrum, EMR, and Glue. Most analytics and ML tools support S3 connections out of the box.
Cloud lake platforms use pay-as-you-go pricing based on storage, compute, and egress.
Governance and security
Amazon S3 supports IAM-based access control and encryption in transit and at rest, according to industry analyses. Lake Formation provides a centralized permission model with fine-grained security and auditing.
The tradeoff: Amazon S3 pricing depends on storage, API calls, egress, and region, requiring detailed TCO modeling. Teams that ingest telemetry directly into S3 without filtering often absorb unnecessary storage and compute costs at scale. Governance also remains tightly coupled to the AWS ecosystem, creating friction for multi-cloud or hybrid environments. Cribl Stream integrates with S3 to transform and filter data before storage, reducing cost and keeping your data portable across destinations beyond AWS.
3. Google Cloud Storage and BigLake
Multi-cloud use and analytics integration
Google Cloud Storage offers consistent performance and integrated analytics through BigQuery, according to industry comparisons. Google BigLake supports multi-cloud analytics and open file formats.
It encrypts data by default and uses IAM for access control. Google Cloud provides a $300 trial credit, with production services following usage-based pricing.
The tradeoff: While BigLake extends analytics across clouds, governance and access controls remain anchored to Google's IAM model, which can complicate cross-platform security enforcement. Egress costs and BigQuery compute charges can escalate quickly for high-volume telemetry workloads. Cribl provides GCP integrations that let you filter, enrich, and route telemetry before it reaches GCS or BigQuery, reducing query costs and ensuring only the right data lands in the right place. See examples of searching Google Cloud Storage with Cribl.
4. Azure Data Lake Storage (ADLS)
Microsoft integration and hierarchical storage
Azure Data Lake Storage offers a hierarchical namespace for enterprise analytics, according to industry analyses. It integrates with Synapse Analytics and Power BI for reporting.
The hierarchical naming allows file-like operations at object-storage scale, simplifying migration from Hadoop environments. Integration with Azure Active Directory and role-based access control provides enterprise security. Connectivity extends across Synapse, Power BI, Azure ML, and Purview.
The tradeoff: ADLS is purpose-built for the Microsoft ecosystem, which is a strength for Azure-first organizations but a limitation for teams running multi-cloud or hybrid environments. Costs depend on storage tier (Hot, Cool, Archive) and data egress, which affects total ownership, and telemetry-heavy workloads can accumulate charges quickly without upstream filtering. Cribl's vendor-agnostic architecture complements ADLS by routing only relevant, processed telemetry into Azure, reducing egress and storage costs while keeping data portable across non-Microsoft destinations.
5. Databricks Lakehouse (Delta Lake)
Unified analytics with ACID transactions
Databricks Lakehouse uses Delta Lake for ACID transactions and reliability. It supports unified batch and streaming analytics and is optimized for Apache Spark.
A lakehouse combines scalable storage with transactional consistency and governance, supporting BI and ML on one platform.
Databricks and Delta Lake are often used for machine learning workloads. Features such as Unity Catalog, collaborative notebooks, and MLflow integration make it useful for data engineering and science teams.
The tradeoff: Higher pricing and required Spark expertise are significant trade-offs compared to basic object storage. For IT and security teams, Databricks is optimized for data science workflows rather than telemetry management, meaning raw log and event data often requires substantial preprocessing before it delivers value. Cribl addresses this gap by shaping, enriching, and routing telemetry upstream of Databricks, so data arrives clean, structured, and ready for analysis without expensive compute cycles spent on transformation inside the platform.
6. Snowflake Data Cloud
Cloud architecture with data sharing
Snowflake separates storage and compute so each scales independently. It supports structured and semi-structured data and provides features such as time travel and zero-copy clones.
Snowflake supports data sharing, collaboration, and analytics. Role-based access, encryption, and audit logging simplify compliance.
The tradeoff: Credit-based pricing can increase costs significantly for variable or high-volume telemetry workloads, and Snowflake's proprietary storage format limits portability if you need to migrate or route data elsewhere. It offers less customization than open-source tools, and security teams managing large log volumes may find the cost-per-query model difficult to predict. Cribl reduces Snowflake spend by filtering and compressing telemetry before ingestion, ensuring only high-value data consumes credits while lower-priority data routes to cost-effective storage tiers.
7. Apache Hadoop Distributed File System (HDFS)
Open-source on-premises and hybrid storage
HDFS is a distributed file system for large-scale data storage. It is flexible but requires specialized setup and expertise.
Benefits include no licensing fees and control over hardware and data location, useful for regulated industries with on-premises needs. Its ecosystem, including Hive, Spark, and YARN, supports large batch workloads.
The tradeoff: High operational overhead, lack of native ACID support, and the need for specialized Hadoop expertise make HDFS a costly investment in engineering resources. Cloud and lakehouse options have reduced its new deployments, and teams often struggle to build real-time search or AI-ready pipelines on top of it without significant custom development. Cribl's hybrid and multi-cloud architecture can sit alongside HDFS environments, routing telemetry to modern cloud destinations in parallel, giving teams a practical migration path without abandoning existing on-premises infrastructure.
8. Cloudera Data Platform (CDP)
Hybrid deployment and governance with SDX
Cloudera Data Platform provides hybrid and multi-cloud deployment with Shared Data Experience (SDX) governance. SDX unifies security and governance across environments with lineage, classification, and access control.
It is suited for regulated sectors such as finance, healthcare, and government.
The tradeoff: Licensing and complexity are higher than cloud-only alternatives, and Cloudera's governance model, while powerful, is tightly coupled to its own platform stack. Teams that need to route telemetry outside the Cloudera ecosystem often encounter friction. Cribl complements CDP by handling telemetry ingestion and routing upstream, reducing the volume of data that enters Cloudera's licensed environment and extending governance-friendly data flows to non-Cloudera destinations without disruption.
9. IBM Cloud Pak and watsonx.data
Integrated data and AI for regulated environments
IBM watsonx.data offers an open data lakehouse deployable on IBM Cloud or AWS. It integrates with IBM's AI tools, including watsonx.ai and watsonx.governance.
It uses open formats like Iceberg and Parquet to reduce lock-in. Purpose-built compute engines optimize cost. Governance tools support regulated industries.
The tradeoff: Its ecosystem is smaller than AWS, Azure, or GCP, and organizations outside the IBM infrastructure footprint may find integration complexity outweighs the benefits. AI and data workflows are tightly coupled to IBM's toolchain, which limits flexibility for teams running best-of-breed security or observability stacks. Cribl's vendor-agnostic platform bridges this gap, routing telemetry to watsonx.data where IBM's AI capabilities add value while simultaneously sending the same data to other analytics or SIEM destinations, preserving flexibility without data loss.
10. MinIO object storage
Open-source S3-compatible storage
MinIO is open-source, high-performance object storage that can be self-hosted, cloud-based, or hybrid, according to industry analyses. It integrates with Kubernetes and is commonly used for AI and analytics workloads.
S3 API compatibility supports direct transfer of existing workflows. Kubernetes deployment suits cloud-native and edge environments. Self-hosting removes egress costs for high-throughput workloads.
The tradeoff: MinIO requires in-house infrastructure management and lacks the managed governance, search, and access control layers found in cloud platforms. Teams with strong DevOps expertise can make it work, but security and IT teams managing telemetry at scale will find that MinIO alone does not provide the pipeline intelligence, data reduction, or AI-ready search capabilities needed for modern SecOps. Cribl fills that gap by sitting upstream of MinIO, filtering and enriching telemetry before it lands, and enabling replay and search across stored data without requiring custom tooling.
Criteria for selecting a data lake platform
Choosing the right data lake platform means evaluating more than just features. Organizations should evaluate how well a platform supports their data volumes, workloads, security requirements, and budget over time. Key considerations include scalability, performance, governance, integrations, and total cost of ownership.
Storage scalability and cost: Can the platform scale to petabytes while supporting cost-effective storage and retention across different stages of the data lifecycle?
Query and compute architecture: Does it separate storage from compute? Which query engines and processing frameworks does it support? If lakehouse capabilities are required, how are transactions handled?
Security and compliance: Does it provide encryption, role-based access controls, audit logging, and the certifications or compliance capabilities required by your organization?
ACID transactions and schema evolution: Does the platform support reliable concurrent reads and writes, schema changes, and consistent data across batch and streaming workloads? These capabilities often depend on the table format and processing engine, not object storage alone.
Ecosystem integrations: Does it integrate with your existing BI tools, analytics platforms, machine learning frameworks, data sources, and infrastructure?
Total cost of ownership: What are the combined costs of storage, compute, data transfer and egress, processing, and ongoing operations—not just the advertised storage price?
Deployment flexibility: Does the platform support the cloud, on-premises, or hybrid deployment model your organization needs to meet operational and regulatory requirements?
Common data lake management challenges include poor data quality, complex integrations, scalability bottlenecks, and difficulty controlling costs. Data routing and pipeline optimization also matter: what data you collect, how you process it, and where you send it can significantly affect downstream performance and cost. Evaluating these upstream data management capabilities alongside storage and query features helps organizations build a data architecture that can scale without unnecessary complexity or expense.
Pricing and cost
Many cloud lakes follow usage-based pricing models. Pre-filtering data with Cribl Stream reduces downstream storage and compute expenses regardless of which platform you choose.
Pros and cons
Cloud object stores (S3, GCS, ADLS) offer scalability and integration with analytics tools but require careful cost control. They often need separate compute layers.
Lakehouse platforms (Databricks, Snowflake) offer transactions, streaming, and ML support but are costlier and require more expertise.
Enterprise and hybrid tools (Cloudera, IBM) focus on governance and compliance but have higher costs and complexity.
Open-source solutions (HDFS, MinIO, Iceberg) reduce costs and lock-in but need stronger technical teams.
Cribl reduces weaknesses by cutting data volume before it lands, parking it in low-cost governed object storage, and keeping it easily searchable and replayable across any analytics, SIEM, or lakehouse platform.
Best practices for migrating to a modern data lake
Planning and assessment
Migration begins with careful planning:
Inventory all data sources.
Assess data quality and lineage, identifying duplicates or sensitive data.
Define your target architecture (cloud-native, lakehouse, hybrid).
Estimate total costs for each platform.
Create a phased migration plan starting with low-risk, high-value datasets.
Test with a proof-of-concept to confirm performance, costs, and operations before committing.
Governance and security
Governance and security should be built in from the start. Key practices include central metadata management, access control, encryption, lineage tracking, and audit logging. Align with frameworks such as SOC 2, HIPAA, GDPR, or FedRAMP.
Use tools like Cribl Stream to redact or mask sensitive data on ingest. Learn more about security data lakes here.
Integration with analytics and ML tools
Test connectivity with target tools such as Spark, Presto, Trino, Power BI, Tableau, Looker, Jupyter, and MLflow during proof-of-concept. Use open formats like Parquet, ORC, and Iceberg to avoid format lock-in. Cribl Stream can adjust data formats during ingestion, simplifying ETL. See [data pipeline reliability](https://cribl.io/blog/building-data-pipelines-for-reliability/) and [augmenting data lakes with Cribl](https://cribl.io/blog/how-to-augment-an-existing-data-lake-with-exabeam-and-cribl-stream/).
Choosing the right data lake for your organization
Balancing scale, cost, governance, and analytics
Choose based on your needs:
For large scale and low ops: S3, GCS, ADLS
For ACID, ML, and real-time analytics: Databricks, Snowflake, or Delta+Iceberg
For compliance-focused or hybrid environments: Cloudera, IBM
For low-cost, flexible setups: HDFS, MinIO, Iceberg
For optimizing data flows: Cribl Stream
Evaluating ecosystems and integrations
Evaluate platform ecosystem size, community activity, and long-term support. Check BI, ML, ETL, and governance integrations. Community adoption of formats like Iceberg, Delta, and Hudi is a good indicator of platform maturity.
Cribl’s flexibility allows integration with all major cloud and analytics environments.
Proof-of-concept and TCO
Run a proof-of-concept:
Use typical workloads, including batch, streaming, and ML.
Measure total ownership cost across 30–60 days.
Assess setup, monitoring, and support requirements.
Testing and analyzing TCO before committing is a consistent best practice across every platform in this guide. Cribl Stream can route identical data streams to multiple platforms for side-by-side comparison during evaluation.
Your data lake decision should not lock your telemetry strategy in
The platforms in this guide solve different problems. AWS, Google Cloud, and Azure provide scale and ecosystem fit. Databricks and Snowflake provide ACID transactions and ML tooling. Cloudera and IBM emphasize governance for regulated environments. HDFS and MinIO give control over your own infrastructure. Whichever lake you choose will likely outlast one more shift in your analytics stack, your SIEM, or your AI strategy.
Treat telemetry management as a separate layer from storage. Cribl Stream collects, shapes, and routes telemetry from any source to any destination, so the lake underneath can change without rebuilding every pipeline feeding it. Cribl Lake provides cost-effective, open-format retention built for IT and security telemetry, and Cribl Search lets analysts and AI-driven workflows query that data in place, whether it lives in Cribl Lake, Amazon S3, Azure Blob, Google Cloud Storage, or another supported destination.
Cribl is designed to let you decide what to collect, how to process it, where to send it, and which platform analyzes it, without betting your entire telemetry strategy on a single vendor's roadmap. Whatever data lake platform ends up at the center of your architecture, Cribl helps keep that choice reversible.
Data Lake Platforms for Enterprise Analytics FAQs
What is the difference between a data lake, data warehouse, and lakehouse?
A data lake stores raw data of any type. A data warehouse stores structured data optimized for BI queries. A lakehouse merges both by combining scalable storage with transactional governance.
Which data lake platforms work best for real-time analytics and machine learning?
Platforms unifying batch and streaming workloads with ML tools, such as Databricks and Snowflake, are best for real-time use. Cribl Stream helps optimize ingestion and shape data for these platforms.
How do I manage governance and security?
Effective data lake governance includes access controls, encryption, audit logging, metadata management, and data retention policies. With Cribl Lake, organizations can retain IT and security telemetry while maintaining control over their data and how it is accessed. For BYOS deployments, organizations also manage storage-level security and access controls through their cloud storage provider. Data routing and processing tools can complement these controls by shaping or filtering data before it reaches the lake.
What factors affect data lake storage costs?
Costs come from storage tier rates, compute for querying, egress fees, API calls, and unnecessary ingest of low-value data.
How do data lakes integrate with BI and analytics tools?
Most connect through SQL interfaces, open file or table formats like Parquet or Iceberg, or native connectors to BI tools such as Power BI and Tableau. Ingestion tools ensure data arrives in compatible formats.
