Top Big Data Storage Solutions & How to Choose the Right One in 2025

Millions of data points are generated daily across SaaS apps, websites, and IoT devices. For businesses, this creates both an opportunity and a challenge. How do you store, manage, and use all that information?

Traditional databases can’t keep up with this scale, speed, and variety. That’s why businesses turn to big data storage solutions. These platforms aren’t only for holding data. They enable pattern analysis, fuel AI models, and support instant decision-making.

In this guide, we’ll look at what big data storage really means, explore the leading solutions, and help you choose the best fit for your business.

What Is Big Data Storage?

Big data storage systems handle large amounts of information across formats and speeds.

Unlike traditional databases that focus on structured rows and columns, these solutions support structured data, such as sales records in SQL tables, and unstructured data, like images, videos, and audio files. They also support semi-structured data, such as JSON logs or XML files from applications.

Modern storage platforms use distributed architectures. They spread data across multiple servers, process queries in parallel, and scale horizontally as your data grows. This approach enables powerful big data computing to handle petabytes of information while maintaining query performance.

The key difference lies in the three V’s, namely volume, velocity, and variety. Volume refers to size measured in terabytes or petabytes. Velocity is how quickly new data arrives and requires processing. Variety encompasses different data types and formats within the same system.

Why Do Businesses Need Big Data Storage Solutions?

Without a purpose-built storage solution, performance degrades, costs spiral, and compliance becomes unmanageable. Here’s why you need big data storage solutions.

1. Faster analytics and reporting

• Insights drive growth, but analytics fail when storage can’t handle scale. SaaS firms tracking millions of user interactions, e-commerce platforms analyzing orders, and healthtech systems handling diagnostic records all face sluggish queries and delayed dashboards. 
• Big data storage solutions ensure analytics deliver insights at the speed at which decisions are made.

2. Real-time decision-making

• Delays can affect both revenue and trust. When your business relies on instant responses, such as fraud detection, recommendation engines, and operational monitoring, the underlying architecture matters. 
• Systems built for real-time ingestion, like those using Kafka big data streams, support high-volume applications in fintech and healthtech without creating bottlenecks.

3. Scalable machine learning workloads

• Machine learning models need vast amounts of data for training and accurate predictions. Proper storage allows bulk reads, structured access, and compatibility with big data pipelines. 
• Lakehouse or warehouse setups reduce data duplication and support ML workloads directly on raw or curated datasets. 

4. Compliance and archival

• Regulated industries, like finance and healthcare, must maintain secure and auditable data records. Storage systems enforce retention policies, access controls, and provenance tracking automatically. 
• Object storage provides affordable long-term archival and ensures records meet regulatory requirements across jurisdictions.

Types of Big Data Storage Architectures

Now that we understand the business drivers of big data storage, let’s explore the different storage architectures. Each storage architecture addresses distinct problems, and most modern data strategies combine multiple systems to manage workloads.

Here are the four types commonly used in a modern big data technology stack.

Feature	Data warehouses	Data lakes	Lakehouses	NoSQL systems
Data type	Structured	All types	All types	Semi-structured
Schema	Schema-on-write	Schema-on-read	Hybrid	Flexible schema
Best for	BI, reporting	Data exploration, ML	Unified analytics	Real-time apps

1. Data warehouses

• Data warehouses focus on structured data and power fast analytical queries at scale. They use columnar storage to compress information and deliver quick aggregations. This makes them ideal for dashboards, financial reporting, and sales analytics.
• Platforms like Snowflake, Amazon Redshift, and Google BigQuery are popular choices, with pricing models that combine storage costs and compute charges for every query run.

2. Data lakes

• Data lakes take a different approach by storing raw, unstructured, and semi-structured data in its original form. They support everything from log files to videos at affordable rates, which makes them useful for data science, archival, and exploratory analytics.
• Services such as Amazon S3 or Azure Data Lake make it easy to scale, but without careful cataloging and governance, lakes can quickly become disorganized and difficult to navigate. Most of the expense here comes not from storage itself but from processing and retrieving the data.

3. Lakehouses

• Lakehouses bridge the gap between lakes and warehouses. They support structured analytics alongside unstructured data exploration. This allows teams to run both business intelligence and machine learning on a single platform.
• Tools like Databricks Delta Lake and Apache Iceberg offer advanced features, such as ACID transactions, schema evolution, and historical time travel queries. The pricing models combine moderate storage fees with variable compute charges that depend on workload complexity. This makes them flexible, but it is often a resource-intensive choice.

4. NoSQL systems

• NoSQL systems specialize in high-speed, variable-structure data generated by applications. They excel in real-time use cases, such as eCommerce shopping carts, IoT sensor streams, or online gaming activity, where milliseconds matter.
• Options like MongoDB, Cassandra, and Redis provide the scalability and flexible schemas needed for these scenarios. However, they are not designed for extensive analytics, so it’s usually paired with warehouses or lakes for more complex queries.

Hevo simplifies this entire process by providing no-code pipelines that connect to all these architectures. This flexibility lets you start with one architecture and evolve to another as your needs change, without rebuilding workflows.

Top Big Data Storage Solutions in 2025

Now that you’re familiar with big data storage and its architecture, let’s explore the top solutions you can consider.

1. Amazon S3

Amazon S3 is a cloud-native object storage service that offers virtually unlimited capacity, high durability, and easy integration across AWS. It supports data lakes, analytics, backups, ML datasets, and archival workloads. It is a good choice for teams seeking decoupled storage and compute, with built-in lifecycle policies.

It uses a pay-as-you-go pricing model based on storage volume, requests, retrieval, and transfer, with no upfront fees or long-term commitments. The pricing starts at $0.023 per GB per month for the first 50 TB.

Pros

• Scales from gigabytes to petabytes without rearchitecting.
• Native integration with most analytics and ETL tools.
• 99.999999999% durability and 99.99% object availability annually.

Cons

• Data transfer retrieval fees can increase overall spend.
• Governance and access management add operational overhead.
• Query performance requires external compute engines.
  

2. Databricks

Databricks is a unified analytics platform that pioneered the lakehouse architecture. It’s built on Delta Lake and supports data engineering, data science, and business analytics workflows on a single platform. Enterprises that want collaborative workflows, scalable compute, and integration across cloud platforms find it particularly useful.

Databricks uses a pay-as-you-go model, charging per Databrick Unit (DBU) per hour. Prices start at $0.15 per DBU.

Pros

• Supports both batch and real-time streaming.
• Supports ACID transactions and ensures data consistency.
• Automatic scaling adjusts compute resources to workload demand.

Cons

• Can become expensive for continuous large-scale workloads.
• The learning curve can be steep for new users.
• Some features, like Unity Catalog and Databricks Lakehouse Platform, are proprietary.
  

3. ClickHouse

ClickHouse is a high-performance and column-oriented SQL database management system designed for online analytical processing (OLAP). It supports distributed query processing across multiple servers and enables horizontal scalability and fault tolerance. It’s available as both open-source and fully managed cloud services and integrates with AWS, GCP, and Azure.

ClickHouse offers a tiered pricing model based on compute and storage usage. The Basic plan starts at $0.2181 per compute unit per hour and $25.30 per TB per month for storage. However, this varies from region to region.

Pros

• High query performance through vectorized execution and parallel processing.
• Efficient data compression reduces storage needs.
• Native support for time-series data analysis.

Cons

• Requires expertise in tuning for optimal performance.
• Steep learning curve for teams new to columnar databases.
• Limited support for complex transactions compared to traditional tools.
  

4. Snowflake

Snowflake is a cloud-native data warehouse that allows storage and compute to scale independently. It enables independent scaling for performance and cost optimization. It supports multi-cloud deployment across tools like Azure and offers features such as zero-copy cloning, time travel, and a data marketplace. This helps you experiment safely, recover historical data, and access external datasets for analytics.

Snowflake’s consumption-based pricing model starts at $2 per credit-hour based on warehouse size, with additional charges for storage and data transfer. The pricing varies by region.

Pros

• Supports data sharing across organizations.
• No infrastructure management required.
• Reliable ecosystem with third-party databases.

Cons

• Compute-intensive workloads can increase costs quickly.
• External data marketplace may require additional management.
• Complex transaction handling is not its primary strength.
  

5. Amazon Redshift

Amazon Redshift is a fully managed data warehouse optimized for AWS. It offers columnar storage and parallel processing for fast analytical queries. Redshift Spectrum extends queries directly to S3 data lakes without transformation. It is ideal for teams requiring scalable analytics, hybrid warehouse-lake queries, and easy integration with AWS tools, such as EMR, Glue, and SageMaker.

Redshift offers two deployment options. Prices start at $0.543 per hour for Provisioned clusters and $1.50 per hour for Serverless.

Pros

• Its massive parallel processing (MPP) technology accelerates large data processing.
• Robust security features.
• Offers automatic backups and replication for high availability.

Cons

• Does not support multi-cloud environments.
• Tight integration with the AWS ecosystem can create vendor lock-in.
• Requires maintenance for non-S3 data sources.
  

6. BigQuery

BigQuery is Google Cloud’s serverless data warehouse that handles multi-terabyte datasets. It offers real-time streaming and integrates easily with GCP services. You can run SQL-based analytics, build predictive models, and gain insights without managing compute resources or scaling.

BigQuery charges $6.25 per terabyte of data scanned for on-demand queries, with the first  TB free each month.

Pros

• Automatic data encryption ensures security without manual configuration.
• Time-partitioned tables improve query performance.
• Supports geospatial analytics for location-based insights.

Cons

• Vendor lock-in can be a concern due to integration with the Google Cloud ecosystem.
• Costs are unpredictable and escalate with frequent large queries.
• Limited control over the underlying infrastructure for fine-tuned optimization.
  

7. MongoDB

MongoDB is a leading NoSQL database offering flexible and document-oriented data storage. It allows applications to store complex JSON-like documents without rigid schemas, making it ideal for modern web, mobile, and AI-driven applications.

Its managed Atlas service handles infrastructure, scaling, and backups. This helps you focus on development while benefiting from global availability and high-performance querying.

MongoDB’s pricing begins at $0.08 per hour for dedicated clusters, with pay-as-you-go scaling.

Pros

• Supports multi-document ACID transactions for complex operations.
• Flexible indexing improves query performance on large datasets.
• Built-in sharding allows horizontal scaling across clusters.

Cons

• Aggregation pipelines can become complex for deep analytics.
• Full-text search and advanced features may require additional configuration.
• Lack of relational features like SQL joins.
  

8. Apache Cassandra

Apache Cassandra is a NoSQL database built for fault tolerance. It supports multi-region deployments with zero downtime and ensures applications stay online even during failures. Its consistent model allows low-latency writes locally and also propagates changes asynchronously to other regions.

Cassandra is best for operational workloads rather than complex analytical queries.

It is open-source and free to use, but you pay for the underlying infrastructure. Managed cloud options like Azure Managed Instance charge based on instance-based compute and storage.

Pros

• Handles millions of writes per second with distributed clusters.
• Linear horizontal scaling lets you add nodes without downtime.
• Active community and commercial distributions through tools like DataStax extend capabilities.

Cons

• Operational overhead can be high due to manual cluster management.
• Weaker performance on analytical queries compared to traditional data warehouses.
• Query language (CQL) resembles SQL but has important limitations.

How to Choose the Right Big Data Storage Solution

Picking the best storage system comes down to matching real needs with real trade-offs. 

The following is a practical framework you can apply to narrow down your choices.

1. Volume and velocity of data

• Begin with your data scale and speed. Petabyte-level workloads or high-frequency streams need architectures that handle constant input. Smaller datasets or periodic reporting often fit well in warehouses. 
• Always plan for three to five years of growth since expansion rates can change pricing and performance needs dramatically.

2. Query performance needs

• The right storage option depends on how your teams use data. Executives expect near-instant dashboards, analysts run complex joins, and real-time applications demand millisecond lookups. 
• So, choose whether you want warehouses for structured analytics, NoSQL for high-speed interactions, or lakehouses for both without compromise on flexibility.

3. Cost considerations

• Cost surprises often come from areas beyond storage. Cloud providers bill for compute cycles, network transfers, and even regional replication. Comparing warehouses, lakes, and lakehouses on long-term usage patterns gives a more accurate picture of total spend.

4. Integration and skills

• Choose platforms that align with existing tools and team skills. Redshift may feel natural for AWS-heavy companies, while Databricks suits Spark-trained teams. 

Additionally, consider how big data integration will fit into your chosen platform to avoid vendor lock-in.

Role of Data Integration in Big Data Storage

Raw data in storage systems becomes useful only when they are consistent, accessible, and ready for analysis. Data integration unifies structured, semi-structured, and unstructured data from multiple sources, ensuring clean and reliable datasets.

Integration also supports governance and addresses big data security challenges. You can enforce data quality rules, track lineage, and monitor usage across warehouses, lakes, or lakehouses. Batch and streaming pipelines deliver timely data. This prevents delays in high-volume workloads.

Hevo is an excellent example of this. It automates the ingestion from 150+ sources into warehouses, lakes, lakehouses, and NoSQL systems. Hevo also handles schema changes, applies necessary transformations, and continuously monitors pipeline health.

By keeping your data analytics-ready, Hevo eliminates the pain of slow queries, fragmented datasets, and expensive manual integration. This ensures your storage systems remain fully utilized and reliable.

Get Started With Big Data Storage Solutions

The final verdict is that choosing the right storage platform depends on your use case, performance needs, and budget. Modern architectures like lakehouses and the separation of storage and big data compute provide flexibility.

However, true value comes from integration and usability, ensuring data remains clean, reliable, and ready to use. Platforms like Hevo automate this process so you can focus on insights instead of engineering.

Want to build scalable, no-code data pipelines that connect to your storage platform?

Book a free demo today!

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