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The ten vendors in the July 15, 2022 VentureBeat roundup were AWS, Cloudera, Databricks, Domo, Google Cloud, Hewlett Packard Enterprise, IBM, Microsoft Azure, Oracle, and Snowflake. The order was an editorial presentation—not a verified market-share ranking, benchmark, or independently scored “best” list. Because product names, deployments, and prices have changed, use this article as a historical 2022 shortlist and a framework for evaluating current alternatives.
What a data lake solution includes
A data lake stores large volumes of structured, semi-structured, and unstructured data, commonly in object storage or distributed file systems. It is designed to preserve raw data for later analytics, engineering, reporting, and machine learning.
A data warehouse generally emphasizes curated, structured data and predictable SQL analytics. A lakehouse combines lake-style storage flexibility with warehouse-style tables, transactions, governance, and performance. Object storage alone is not a complete data-lake operating model: organizations also need ingestion, processing, cataloging, identity, security, quality controls, lifecycle policies, and monitoring.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe 2022 list mixes several categories. AWS, Azure, and Google Cloud provide composable infrastructure and analytics services; Databricks and Snowflake provide managed data platforms; Cloudera, IBM, and HPE address hybrid or enterprise environments; Domo emphasizes business analytics; and Oracle is most relevant to Oracle-centered estates.
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How to compare data-lake vendors
- Storage: Object storage, HDFS, proprietary storage, or external storage; support for Parquet and open table formats such as Iceberg, Delta Lake, or Hudi.
- Processing: SQL, Spark, batch, streaming, notebooks, and machine learning.
- Governance: Catalogs, lineage, discovery, ownership, row- and column-level controls, quality, and auditing.
- Security: Identity integration, encryption, private networking, key management, compliance, and tenant isolation.
- Interoperability: APIs, connectors, external tables, open formats, catalog portability, and migration options.
- Deployment: Public cloud, managed SaaS, hybrid, on-premises, or customer-managed infrastructure.
- Performance: Partitioning, file compaction, caching, indexing, query acceleration, and workload isolation.
- Total cost: Storage, compute, requests, retrieval, networking, replication, governance, support, licensing, and personnel.
- Operational burden: How many services must be configured, upgraded, monitored, and secured.
The 10 vendors in the 2022 roundup
1. Amazon Web Services
In 2022, AWS represented a cloud data-lake foundation built around Amazon S3, with services such as Glue Data Catalog, Athena, EMR, Glue, Redshift, IAM, logging, and encryption layered around it.
Best fit: Organizations already standardized on AWS, need highly scalable object storage, or want broad choice among analytics services.
Strengths: Extensive ecosystem, flexible architecture, mature security controls, and support for many processing engines.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesMain caution: AWS is not one simple data-lake product. Storage, requests, retrieval, processing, replication, transfer, and management services can all contribute to the bill. See AWS S3 pricing.
2. Cloudera
Cloudera represented the hybrid, enterprise, and Hadoop-oriented segment. Its 2022 positioning emphasized secure handling of multiple data types, enterprise support, and SDX governance. Current Cloudera materials describe cloud-native services across AWS, Azure, and Google Cloud, as well as on-premises deployments using Cloudera Base, Apache Ozone, third-party storage, and governance technologies.
Best fit: Regulated organizations requiring hybrid or on-premises control, Hadoop/Spark continuity, or governed multi-cloud operations.
Main caution: It can require more administration and licensing management than a cloud-native object-storage architecture. Cloudera’s current pricing page shows consumption examples such as $0.07 per CCU for Data Engineering Core and $0.20 per CCU for Data Engineering All-Purpose; these are indicative and configuration-dependent, not universal project quotes. See Cloudera pricing.
The Tool Desk
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Databricks represented the lakehouse model: cloud object storage combined with data engineering, SQL, streaming, machine learning, governance, and table management. Delta Lake was a central part of its 2022 positioning.
Rank #2
- 【Advanced Home Data & Media Hub】For advanced home users who need phone backup, file storage, and centralized data management. Centralize family photos, 4K videos, movies, computer backups, and personal files in one place while running multiple apps for home entertainment and everyday data management. Suitable for households with growing digital libraries and multiple NAS use cases.
- 【Built for Creators, Media Servers & Advanced Apps】Powered by the Intel N100 Quad-Core CPU, 8GB DDR5 RAM, 2.5GbE networking, and dual M.2 NVMe slots, DXP2800 handles large files and heavier workloads with ease. Run Docker, virtual machines, and media server applications compatible with Plex—ideal for content creators, tech enthusiasts, and advanced home users managing 4K videos, RAW photos, personal media libraries, and multiple NAS apps.
- 【Up to 80TB for Growing Digital Libraries】 Supports up to 80TB of storage using two HDD bays and two M.2 NVMe SSD slots for family photos, movies, RAW photos, 4K videos, work files, and device backups. AI photo management supports recognition of people, objects, scenes, and locations, album organization, and duplicate photo detection. HDDs and SSDs are not included.
- 【AI-powered Home Surveillance】Turn DXP2800 into a centralized home surveillance hub by connecting compatible network cameras and storing recordings locally on your NAS. AI-powered features include Face Recognition, People Detection, and Pet Detection, helping advanced home users review important events more efficiently while managing home surveillance and personal data in one place.
- 【One data Center Across Your Devices】Keep files from desktops, laptops, phones, tablets, and other devices together instead of scattered across cloud accounts and external drives. Access, back up, organize, and share data across Windows, macOS, Android, iOS, web browsers, and compatible smart TVs—ideal for creators and advanced home users working across multiple devices.
Best fit: Engineering- and machine-learning-heavy teams that want one managed environment for Spark, SQL, notebooks, streaming, and AI workloads.
Main caution: Databricks is not merely cheap storage. Compute and platform consumption can dominate costs, so teams need cluster or serverless policies, workload optimization, scheduling, and FinOps controls. Pricing varies by cloud, workload, SKU, and deployment; see Databricks pricing documentation.
4. Domo
Domo was included as a cloud analytics and business-data platform that could augment an existing data lake. It is more focused on dashboards, business users, ingestion, transformation, access controls, and operational analytics than on providing a foundational object store.
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Main caution: Domo should not be compared directly with S3, Azure Data Lake Storage, or Google Cloud Storage as though they were equivalent products. It may be a poor fit for inexpensive raw-data retention or highly customized data engineering.
5. Google Cloud
Google Cloud’s data-lake ecosystem includes Cloud Storage, BigQuery, Dataproc or managed Spark, Dataplex, and machine-learning services. The 2022 roundup emphasized managed analysis of large datasets, Spark and Hadoop migration, data science, and cost controls.
Best fit: Organizations already using BigQuery, Google’s data and AI services, or managed Spark workflows.
Main caution: Cloud Storage, BigQuery, processing, governance, and data movement are separate cost and architecture decisions. Consult the Google Cloud pricing index rather than treating storage price as total lake cost.
Rank #3
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- Subscription-Free Personal Cloud – Store, back up, and manage all your videos, music, and photos and access them anytime without paying any monthly fees.
- Storage Purpose-Built for Data Security – A NAS designed to keep your data safe, the LS200 features a closed system to reduce vulnerabilities from 3rd party apps and SSL encryption for secure file transfers.
- Back Up Multiple Computers & Devices – NAS Navigator management utility and PC backup software included. NAS Navigator 2 for macOS 15 and earlier. You can set up automated backups of data on your computers.
6. Hewlett Packard Enterprise
HPE represented a hybrid infrastructure and services approach through GreenLake and related data-fabric capabilities. The 2022 description involved software, hardware, and HPE Pointnext services intended to simplify enterprise and Hadoop-oriented deployments.
Best fit: Enterprises with substantial on-premises infrastructure, sovereignty requirements, or a preference for infrastructure delivered as a service.
Main caution: Procurement is typically more complex and quote-based than starting with a self-service public-cloud service. Costs depend on capacity, hardware, support, geography, software, and professional services.
7. IBM
IBM’s 2022 entry emphasized cloud data lakes, automated integration, virtualization, and embedded governance for sectors such as financial services and healthcare. The current product context is different: IBM now positions watsonx.data as a hybrid, open data lakehouse for AI and analytics.
IBM says watsonx.data can run as a managed multi-cloud service on IBM Cloud, AWS, or on-premises. Its documentation describes Presto, Spark, and Milvus engines, IBM Cloud Object Storage or S3-compatible storage, and resource-unit metering.
Best fit: Regulated enterprises, IBM customers, and organizations seeking governed hybrid analytics and AI.
Main caution: Plan names, deployment choices, support, and billing can be difficult to compare. IBM’s pricing page says displayed prices are indicative, may vary by country, exclude taxes, and do not necessarily include every support charge. See IBM watsonx.data pricing and service documentation.
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8. Microsoft Azure
The 2022 Azure entry described integrated storage, processing, analytics, and Microsoft infrastructure. The modern product reference is generally Azure Data Lake Storage Gen2, built on Azure Blob Storage and connected to services such as Microsoft Fabric, Synapse, Power BI, and Microsoft identity and governance tools.
Rank #4
- Value NAS with RAID for centralized storage and backup for all your devices. Check out the LS 700 for enhanced features, cloud capabilities, macOS 26, and up to 7x faster performance than the LS 200.
- Connect the LinkStation to your router and enjoy shared network storage for your devices. The NAS is compatible with Windows and macOS*, and Buffalo's US-based support is on-hand 24/7 for installation walkthroughs. *Only for macOS 15 (Sequoia) and earlier. For macOS 26, check out our LS 700 series.
- Subscription-Free Personal Cloud – Store, back up, and manage all your videos, music, and photos and access them anytime without paying any monthly fees.
- Storage Purpose-Built for Data Security – A NAS designed to keep your data safe, the LS200 features a closed system to reduce vulnerabilities from 3rd party apps and SSL encryption for secure file transfers.
- Back Up Multiple Computers & Devices – NAS Navigator management utility and PC backup software included. NAS Navigator 2 for macOS 15 and earlier. You can set up automated backups of data on your computers.
Best fit: Organizations already using Microsoft 365, Power BI, Azure, Entra ID, Fabric, or Synapse.
Main caution: Azure Data Lake Storage is not a single all-inclusive product with one flat price. Storage, analytics, networking, licensing, and support must be modeled together. See Azure Data Lake Storage pricing.
9. Oracle
In 2022, Oracle Big Data Service was described as a Hadoop-based data-lake option, including Cloudera Enterprise components, machine-learning support, and integration with Oracle environments.
Best fit: Enterprises already dependent on Oracle databases, applications, infrastructure, or cloud relationships.
Main caution: Oracle’s product portfolio and naming have evolved since 2022. Buyers should verify the current service catalog, packaging, availability, and pricing rather than assume that the 2022 Big Data Service description remains unchanged. Portability may also require careful review where Oracle-specific dependencies are involved.
10. Snowflake
Snowflake was described in 2022 as a secure, collaborative cloud data platform with fast querying and a broad partner ecosystem. Today it is better understood as a managed data and AI platform supporting warehouse, lake, lakehouse, sharing, and application patterns.
Best fit: SQL-heavy analytics, governed data sharing, multi-team access, and organizations that prefer a managed platform.
Main caution: Snowflake is not equivalent to low-cost object storage. Compute, queries, transformations, features, and storage are separate considerations. It supports AWS, Azure, and Google Cloud; see Snowflake’s cloud-platform documentation, pricing information, and its consumption tables.
Best Value
- Your Personal Streaming Server - Build your own Netflix-style media library and stream 4K movies, shows and photos to any device without monthly fees
- Create Your Own Cloud - Store your entire photo, video and music collection; access from anywhere with fast 282 MB/s transfer speeds
- Creator-Grade Backup Solution - Protect your irreplaceable content with automated backups to cloud services, external drives and remote NAS
- Multi-Layered Data Protection - Combine RAID redundancy, automated backups and snapshot technology to prevent data loss from any cause
- Smart Home Surveillance - Support up to 30 IP cameras with AI detection, instant alerts and secure remote monitoring
Comparison table
The following is a practical comparison framework, not a validated 2022 ranking.
| Vendor | 2022 positioning | Deployment | Analytics model | Best fit | Main caution |
|---|---|---|---|---|---|
| AWS | Cloud lake foundation | Public cloud | Composable AWS services | AWS-standardized enterprises | Billing and architecture complexity |
| Cloudera | Hybrid enterprise platform | Cloud and on-premises | Engineering, analytics, AI | Regulated hybrid estates | Platform and licensing overhead |
| Databricks | Lakehouse | Managed cloud | SQL, Spark, ML, streaming | Engineering and ML teams | Compute-spend management |
| Domo | Analytics and lake augmentation | Cloud | BI and business analytics | Business-user analytics | Not a pure storage platform |
| Google Cloud | Managed cloud ecosystem | Public cloud | BigQuery, Spark, data and AI services | Google-centered estates | Many services and cost dimensions |
| HPE | Hybrid infrastructure and services | Hybrid and on-premises | Enterprise data services | Sovereignty and hybrid needs | Procurement complexity |
| IBM | Governed hybrid lakehouse | Cloud, multi-cloud, on-premises | Presto, Spark, AI | Regulated IBM-oriented buyers | Deployment and pricing complexity |
| Azure | Microsoft-integrated lake | Public cloud and hybrid | Azure analytics ecosystem | Microsoft-standardized estates | Cross-service licensing complexity |
| Oracle | Oracle-centric big-data services | Cloud and enterprise | Big data and machine learning | Oracle-heavy organizations | Verify current product status |
| Snowflake | Managed cloud data platform | Managed cloud | SQL, sharing, data applications | SQL and collaboration workloads | Consumption can rise quickly |
Which vendor is best for your organization?
There is no universally best choice. A practical shortlist usually follows the organization’s existing ecosystem and workload:
- AWS-native: AWS provides the most natural composable foundation.
- Microsoft-native: Azure Data Lake Storage and the surrounding Azure and Microsoft analytics stack are the logical starting point.
- Google-native: Google Cloud is compelling where BigQuery, Cloud Storage, and Google’s data and AI services are already central.
- Lakehouse engineering and ML: Databricks is designed for teams combining Spark, SQL, streaming, notebooks, and machine learning.
- Governed SQL analytics and sharing: Snowflake is better suited than raw object storage when managed SQL access and collaboration are priorities.
- Hybrid or on-premises governance: Cloudera, HPE, and IBM deserve closer evaluation.
- Business-user analytics: Domo may be appropriate when dashboards and adoption matter more than building a deeply customized engineering platform.
- Oracle-centered environments: Oracle is most compelling when existing databases, applications, and skills reduce switching costs.
How much does a data lake cost?
Storage is only one line item. A realistic estimate should include:
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- Hot, cool, cold, and archive storage;
- API requests and metadata operations;
- Query, Spark, notebook, streaming, and machine-learning compute;
- Ingestion and transformation;
- Cold-data retrieval;
- Cross-region replication and disaster recovery;
- Internet and cross-cloud egress;
- Catalog, governance, observability, security, and key management;
- Enterprise support, consulting, and professional services; and
- People needed to operate, secure, and optimize the platform.
A vendor cannot credibly be called “cheapest” without a defined workload, region, retention period, query volume, concurrency, contract, and egress pattern. AWS explicitly identifies storage, requests, retrieval, transfer, replication, and related management or analytics features as possible S3 charges. Azure notes that pricing can vary by agreement, purchase date, currency, and configuration. Databricks and Snowflake require particular care because compute and platform consumption can outweigh storage costs.
Portability and open formats
Ask whether data remains usable outside the vendor’s preferred engine. Important considerations include Parquet files, Apache Iceberg, Delta Lake, Apache Hudi, external tables, open APIs, catalog portability, data egress, migration tooling, and whether security and governance policies survive a platform change.
Open formats reduce migration friction, but they do not eliminate lock-in. Workflows, proprietary features, metadata, identity policies, performance optimizations, and network architecture may still require substantial redevelopment.
Common data-lake mistakes
- Creating a “data swamp” without cataloging, ownership, quality, or discovery.
- Keeping every raw file indefinitely without lifecycle and deletion policies.
- Allowing excessive small files, poor partitioning, or unoptimized tables to degrade performance.
- Choosing based only on storage price.
- Underestimating identity, key management, private networking, audit, and compliance.
- Allowing uncontrolled replication, cross-region movement, or egress.
- Building a lake before identifying its consumers and workloads.
- Failing to define an exit strategy and test migration assumptions.
- Operating compute without budgets, quotas, tagging, scheduling, or workload governance.
Bottom line
The 2022 roundup is useful as a historical shortlist, but its order should not be treated as an independently verified ranking. Hyperscalers are strongest for composable cloud foundations; Databricks and Snowflake are stronger when managed lakehouse or analytics capabilities are the priority; Cloudera, HPE, and IBM are more relevant to hybrid, governed, or regulated environments; Domo is primarily an analytics and lake-augmentation platform; and Oracle is most compelling where Oracle already anchors the enterprise. Select on workload, deployment constraints, governance, portability, operational capacity, and total cost—not on storage price or the word “top.”
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
