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Best Big Data Storage Solutions Of 2024

What are Big Data Storage Solutions?

Big data storage solutions are the heavy-duty filing cabinets of the digital age, designed to handle massive and ever-growing datasets. Traditional storage buckles under the pressure of social media feeds, sensor data, and complex log files. Big data storage tackles this by offering scalable and cost-effective ways to store this information. Benefits include the ability to analyze vast amounts of data for hidden patterns, improve operational efficiency (think targeted marketing campaigns), and gain a competitive edge through data-driven decisions. Core functionalities involve storing structured, semi-structured, and unstructured data across distributed systems. Emerging features leverage object storage for scalability and cloud-based solutions for increased flexibility and accessibility. Big data solutions empower businesses of all sizes, especially those in data-driven industries like finance, healthcare, and retail. While initial setup costs can be high, big data storage unlocks valuable insights that can transform businesses. In essence, it's the foundation for turning avalanches of data into actionable intelligence.

What Are The Key Benefits of Big Data Storage Solutions?

  • Store & Manage Massive Datasets Efficiently
  • Unlock Hidden Insights & Data-Driven Decisions
  • Improved Operational Efficiency & Performance
  • Scalable Storage for Growing Data Volumes
  • Handle Complex & Diverse Data Formats
  • Enhanced Analytics Capabilities & Business Intelligence
  • Competitive Advantage Through Data-Driven Strategies
  • Reduced Storage Costs & Optimized Resource Utilization
  • Future-Proof Infrastructure for Data Growth
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Overall

Based on the latest available data collected by SelectHub for 5 solutions, we determined the following solutions are the best Big Data Storage Solutions overall:

Hadoop
Hadoop
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Why We Picked Hadoop

Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it.

So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

Pros & Cons

  • Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
  • Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
  • Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
  • Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
  • Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
  • Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
  • Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
  • Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.

Key Features

  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
Cloudera
Cloudera
Start Price
$833
Per User, Annually
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Deployment
Cloud On-Premise
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Why We Picked Cloudera

Is Cloudera the answer to your data management woes, or is it just a bunch of hot air?

User reviews from the past year paint a mixed picture of Cloudera. While some users praise its flexibility and ability to handle large datasets, others find it cumbersome and expensive. Cloudera's hybrid cloud approach, allowing users to deploy on-premises or in the cloud, is a major selling point for many. However, some users find the platform's complexity a barrier to entry, especially for those without extensive experience in data management. Cloudera's integration with other tools, such as Apache Hadoop, is a key differentiator, but some users report issues with compatibility and performance.

Cloudera is best suited for large enterprises with complex data needs and a dedicated team of data engineers. Its robust features and scalability make it a powerful tool for organizations that require a comprehensive data management solution. However, smaller businesses or those with limited technical resources may find Cloudera's complexity and cost prohibitive.

Pros & Cons

  • Scalability: Cloudera can handle massive datasets and complex queries, making it suitable for large-scale data analysis and reporting.
  • Security: Cloudera offers robust security features, including data encryption and access control, ensuring sensitive data is protected.
  • Performance: Cloudera's optimized architecture and distributed processing capabilities deliver fast query execution and efficient data processing.
  • Integration: Cloudera integrates seamlessly with various data sources and tools, enabling users to connect and analyze data from different systems.
  • Community Support: Cloudera has a large and active community, providing access to resources, support, and best practices.
  • Steep Learning Curve: New users often find Cloudera's interface and complex architecture challenging to navigate, requiring significant time and effort to master. This can be especially problematic for teams with limited technical expertise.
  • Costly Implementation: Cloudera's pricing model can be expensive, particularly for large deployments. The cost of hardware, software licenses, and ongoing support can be a significant barrier for some organizations.
  • Limited Scalability: While Cloudera offers scalability, some users have reported challenges scaling their deployments to meet rapidly growing data volumes. This can lead to performance bottlenecks and slow query execution times.
  • Complex Management: Managing a Cloudera cluster can be complex, requiring specialized skills and knowledge. This can be a burden for organizations with limited IT resources.

Key Features

  • Data Science Workbench: Through a unified workflow, collaboratively experiment with data, share research between teams and get straight to production without having to recode. Create and deploy custom machine learning models and reproduce them confidently and consistently.
  • Real-Time Streaming Analytics: With edge-to-enterprise governance, Cloudera DataFlow continuously ingests, prioritizes and analyzes data for actionable insights in real-time. Develop workflows to move data from on-premises to the cloud or vice-versa, and monitor edge applications and streaming sources.
  • Machine Learning: Enable enterprise data science in the cloud with self-service access to governed data. Deploys machine learning workspaces with adjustable auto-suspending resource consumption guardrails that can provide end-to-end machine learning tools in one cohesive environment.
  • Data Warehouse: Merges data from unstructured, structured and edge sources. The auto-scaling data warehouse returns queries almost instantly and has an optimized infrastructure that moves workloads across platforms to prepare vast amounts of data for analysis.
  • Operational Database: The operational database promises both high concurrency and low latency, processing large loads of data simultaneously without delay. It can extract real-time insights and enable scalable data-driven applications. 
  • Open-Source Platform: Access the Apache-based source code for the program and make adjustments, customizations and updates as desired. 
  • Data Security and Governance: Reduce risk by setting data security and governance policies. The Cloudera Shared Data Experience (SDX) then automatically enforces these protocols across the entire platform, ensuring sensitive information consistently remains secure without disruption to business processes.
  • Hybrid Deployment: Leverage the deployment flexibility and accessibility to work on data wherever it lives. Read and write directly to cloud or on-premises storage environments. With a hybrid cloud-based architecture, choose between a PaaS offering or opt for more control via IaaS, private cloud, multi-cloud or on-premises deployment.
Hortonworks
Hortonworks
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Key Features

  • Multi-Workload Processing: The product is able to handle multiple workloads and other taxing processes such as detailed analysis and report generation — all in parallel processes. 
  • Real-Time Processing: Users can take advantage of processing in real time, without having to wait for their data to finish compiling. 
  • Batch Processing: Batch processing is the processing of large quantities of data in large batches, significantly cutting down the time it takes to process information. 
  • Data Governance: Controlling, managing and distributing data are essential to a modern analytics solution. The software provides a suite of management features for users to take advantage of.  
  • Dataflow: Dataflow is an all-in-one data crunching feature that streams data and insights in real-time. It delivers actionable intelligence and curated data as it’s being processed. 
Vertica
Vertica
Start Price
$3.19
Per Hour, Usage-Based
Company Size
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Deployment
Cloud On-Premise
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Why We Picked Vertica

Vertica Analytics is a big data relational database that provides batch as well as streaming analytics to enterprises. Citing a robust, distributed architecture with massively parallel processing (MPP), all users who review data processing say that it performs extremely fast computing with I/O optimization, and columnar storage makes it ideal for reporting. Approximately 72% of the users who review performance say that it is a reliable tool with high availability and virtually no downtime, with K-safety protocol in place for efficient fault tolerance. Citing its feature set, around 56% of the users say that they are satisfied with its elastic scalability, rich analytical functions and excellent clustering technology.
On the flip side, almost 50% of the users who mention technical and community support say that it is inadequate and possibly contributes to the platform’s steep learning curve. All users who review its cost say that the solution is expensive, with restrictive data storage limits.
In summary, Vertica is a big data and analytics platform that provides streaming analytics with lightning-fast query speeds, machine learning and forecast capabilities.

Pros & Cons

  • Data Processing: All users who mention computing say that the tool’s columnar storage and parallel processing enable faster querying.
  • Performance: Almost 72% of the users who review performance say the platform is robust and reliable with high availability.
  • Functionality: Around 56% of the users who review functionality say that it is feature-rich and performs as expected.
  • Cost: All users who mention cost say that data storage limits can be restrictive and the tool is expensive.
  • Community Support: Citing lack of technical community support, approximately 50% of the users say that it makes adoption difficult.

Key Features

  • Streaming Analytics: Connects to Apache Kafka for IoT data analysis in real time. Analyzes and manages large volumes of data from IoT devices such as machine and sensor data for buildings, vehicles, medical systems, smart devices and wearables. 
  • Machine Learning: Get automated insights and deliverables through machine learning modules that automatically digest and parse large data portions. ML modules are built into its core — no need to pay for them or install them separately. 
  • Software Only: Work with a robust software interface with dedicated IT resources. All data warehousing, storage and processing infrastructure is hosted offsite. 
  • Fast SQL Databases: Store and retrieve data through highly scalable and speedy SQL databases. 
  • Massively Parallel Processing: Get increased speed and scalability at larger scales by running two processes side-by-side through massively parallel processing. 
  • Columnar Storage: Read only the most important sets of data first through columnar storage that greatly speeds up data retrieval. 
Actian
Actian
Start Price
$500
Monthly
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Why We Picked Actian

Actian, a Big Data Storage Solutions software, has garnered mixed reviews in the past year. Users praise its scalability, flexibility, and cost-effectiveness. They appreciate its ability to handle large datasets and its compatibility with various data sources. However, some users have reported performance issues, particularly with complex queries, and have expressed concerns about its documentation and technical support.

Compared to competitors like Cloudera and Hortonworks, Actian is seen as a more affordable and user-friendly option. Its intuitive interface and pre-built templates make it accessible to users with varying technical expertise. However, Cloudera and Hortonworks offer more comprehensive features and support for advanced analytics, making them better suited for large-scale, data-intensive applications.

Actian is an ideal choice for organizations looking for a cost-effective and easy-to-use Big Data Storage solution. Its scalability and flexibility make it suitable for businesses of all sizes. However, organizations with complex data analysis requirements or those seeking advanced analytics capabilities may need to consider more feature-rich alternatives.

Pros & Cons

  • Scalability and Performance: Actian's distributed architecture enables users to easily scale their data storage and processing capabilities to meet growing business demands, delivering high performance and low latency for complex analytics and data-intensive workloads.
  • Flexibility and Extensibility: Actian provides a flexible and extensible platform that allows users to integrate with a wide range of data sources, tools, and applications, making it easy to customize and adapt to specific business needs.
  • Security and Compliance: Actian offers robust security features such as encryption, access control, and audit trails, ensuring the protection of sensitive data and compliance with industry regulations.
  • Cost-Effectiveness: Actian's subscription-based pricing model and optimized resource utilization help businesses reduce their IT costs while still accessing powerful data storage and analytics capabilities.
  • Technical Support: Actian provides comprehensive technical support to assist users with installation, configuration, and ongoing maintenance, ensuring a smooth and efficient experience.
  • Difficult to Use Interface: Many users have complained that the interface for Actian is not intuitive and can be difficult to navigate, especially for those who are not familiar with Big Data Storage Solutions.
  • Limited Scalability: Actian has been criticized for its limited scalability, which can be a major issue for businesses that need to handle large amounts of data.
  • Lack of Support: Users have also reported that Actian's customer support is lacking, which can be a major inconvenience when troubleshooting issues.
  • High Cost: Actian is often more expensive than other Big Data Storage Solutions, which can be a deterrent for businesses that are on a budget.
  • Frequent Bugs: Users have also complained about frequent bugs and glitches in Actian, which can lead to data loss or corruption.

Key Features

  • Data Warehouse: Avalanche, its data warehouse service, deploys on-premises and in the cloud, including AWS, Azure and Google Cloud, enabling self-paced migration of enterprise applications and data. 
    • Scalability: Scales seamlessly across data volumes, query complexity and concurrent users. Continues to perform analytical queries while the database is updated without a drop in performance, allowing up to 64 concurrent users out-of-the-box. 
    • Columnar Database: Scales out to multiple nodes and petabytes of data through massively parallel processing (MPP). Its underlying database engine, Vector processes hundreds of tuples of data by leveraging SIMD support in x86 CPUs. Speeds up performance and reduces data footprint by compressing data up to 4-6 times. 
    • Edge Computing: Query data distributed across multiple sources in one go with federated queries and get results in the query source itself to lower costs and reduce time to insight. 
  • Integrations: Connect to any data source across on-premise, in the cloud, and hybrid environments through its UniversalConnect technology. Rapidly connects two applications and expedites mapping and data transformations through PointConnect. 
  • Zen Embedded Database: Provides reliable data across enterprise applications, onsite as well as remote, including IoT applications. Move data seamlessly between any operating system, other versions or PostgreSQL database products — with no ETL overhead. Enables edge computing by deploying with minimal effort across Windows, Linux, Mac OS, iOS and Android devices. 
    • NoSQL Object Database: In addition to SQL access for reporting, query and transactions, it offers NoSQL access for data-intensive application performance and local analytics support. 
    • Architecture: The Zen database family is built on a single, modular architecture that scales seamlessly from single-user client to enterprise-grade servers. Add-on packages for auditing, replications and multi-instance synchronization further support various office networks, including remote workers and IoT and mobile devices. 
  • Operational Analytics: Provides robust online transaction processing through a high-performing analytics engine coupled with a stable enterprise RDBMS. Integrates with Ingres to provide an end-to-end solution for designing and implementing integrations. 
  • DataCloud Backup: Automatically transfer backup data resulting from Ingres/Actian X database checkpoints and journals to the cloud. Scales according to backup requirements and handles increasing workloads through load balancing and automatic provisioning of servers. 

COMPARE THE BEST Big Data Storage Solutions

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SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Big Data Storage Solutions, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
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Free Trial
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Hadoop
Undisclosed
No
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Hadoop
Cloudera
$833
Per User, Annually
Yes
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Cloud On-Premise
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Cloudera
Hortonworks
Still gathering data
No
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Hortonworks
Vertica
$3.19
Per Hour, Usage-Based
Yes
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Vertica
Actian
$500
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Actian

All Big Data Storage Solutions (5 found)

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Hadoop

by Apache Software Foundation
Hadoop
Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more. Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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Cloudera

by Cloudera, Inc.
Cloudera
Cloudera is a multi-environment analytics platform powered by integrated open source technologies that help users glean actionable business insights from their data, wherever it lives. With an enterprise data cloud, it puts data management at analysts’ fingertips, with the scalability and elasticity to manage any workload. It offers users transparency into the whole data lifecycle and the flexibility of customization through its open architecture. It is available on an annual subscription basis with three offerings: CDP Data Center, Enterprise Data Hub and HDP Enterprise Plus. Each edition offers different components and pricing varies based on computing power, storage space and number of nodes. The company merged with Hortonworks in 2019 to provide a comprehensive, end-to-end hybrid and multi-cloud offering.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$500 - $1,000
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Hortonworks

by Hortonworks Inc.
Hortonworks
Hortonworks Data Platform is an open-source data analysis and collection product from Hortonworks. It is designed to meet the needs of small, medium and large enterprises that are trying to take advantage of big data. The company was acquired by Cloudera in 2019 for $5.2 billion. HDP has a number of features that help it process large enterprise-level volumes, including multi-workload processing, batch processing, real-time processing, governance and more.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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Vertica

by Micro Focus - HM - LB
Vertica
Vertica is an analytics and data exploration platform designed to ingest massive quantities of data, parse it, and then return business insights as reports and interactive graphics. Elastically scalable, it provides batch as well as streaming analytics with massively parallel processing, ANSI-compliant SQL querying and ACID transactions. Deployable in the cloud, on-premise, on Apache Hadoop and as a hybrid model, its resource manager enables concurrent job runs with reduced CPU and memory usage and data compression for storage optimization. A serverless setup and advanced data trawling techniques help users store and access their data with ease.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Actian

by Actian Corporation
Actian
Actian is a cloud data management platform that enables data integration with fully managed warehousing, transformation and analytics for enterprises. It integrates with various cloud-based and on-premise technologies and services. With massively parallel processing and data compression on the back end, its embedded analytical engine works in tandem with a robust RDBMS, complementing its computing with built-in user-defined functions to process online transactions at scale.Part of the Cloud Security Alliance, the vendor consistently updates their best practices to ensure the security of its cloud services. It offers a 30-day free trial. Pricing is available on request.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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