Ten Use Cases Where Object Storage Really Stands Out

What are the use cases where object storage really stands out vs the alternative block and file storage types? In my Sales Engineering role at Cloudian, I’ve helped hundreds of customers solve their storage challenges with object storage. Here are the ten most common object storage use cases I see today, plus ten more emerging cases in AI.

1. Cloud Storage Services: Many cloud storage providers and large enterprises utilize object storage for shared, S3-compatible storage services. It’s scalable, durable, and cost-effective, and users can store vast amounts of data without worrying about underlying infrastructure management. And because it supports the S3 API, it is compatible with applications that run in the public cloud.
2. Backup and Disaster Recovery: Object storage is well-suited for backup and disaster recovery purposes due to its high durability and availability. It ensures that data remains intact even in the event of hardware failures or disasters. Furthermore, data stored on an object storage platform can easily be made immutable, which means it’s protected from encryption by ransomware.
3. Archiving: Object storage provides an efficient means for long-term data archiving. It’s often used for storing historical records, regulatory compliance data, and other information that needs to be retained for extended periods.
4. Content Distribution and CDN: Content Delivery Networks (CDNs) leverage object storage to distribute content efficiently across geographically distributed servers. This helps improve the performance and reliability of delivering web content, videos, and other media to end-users.
5. Data Lakes: Object storage serves as a foundational component of data lakes, which are centralized repositories for storing structured and unstructured data at scale. Data lakes are commonly used for data analytics, machine learning, and other big data applications.
6. IoT Data Storage: With the proliferation of Internet of Things (IoT) devices, object storage provides a scalable and flexible solution for storing the vast amounts of data generated by sensors, devices, and applications.
7. Media and Entertainment: Object storage is extensively used in the media and entertainment industry for storing and managing large media files such as videos, images, and audio files. It enables easy access, distribution, and sharing of multimedia content.
8. Data Sharing and Collaboration: Object storage facilitates seamless data sharing and collaboration among teams and organizations. Users can access and collaborate on files stored in object storage repositories from anywhere with an internet connection.
9. Genomic Data Storage: Genomic research generates massive datasets that require scalable storage solutions. Object storage is well-suited for storing and managing genomic data efficiently.
10. Log and Event Data Storage: Object storage can be used to store log files and event data generated by applications, servers, and network devices. This data can then be analyzed for troubleshooting, performance monitoring, and security analysis purposes.

AI emerging as the next big use case

The most interesting, and daily growing usage trend in object storage use cases is how CRITICAL it is to AI. Object storage is fully compatible with the most widely-used tools, including PyTorch, Tensor Flow, Kafka, and Druid.  And it works with popular data observability and analytics tools like Cribl, Splunk and Dremio. Furthermore, object storage on prem has the advantage of keeping proprietary training data secure in your own data center.

Here are ten examples of object storage use cases in AI.

1. Data Storage for Training Data: AI and machine learning models require large volumes of data for training. Object storage provides a scalable and durable storage solution for storing diverse datasets, including unstructured, and semi-structured data.
2. Data Lakes for AI: Object storage serves as a foundational component of data lakes, which are central repositories for storing raw and processed data from various sources. Data lakes enable organizations to consolidate data for AI and analytics purposes, allowing for easier data exploration and model training.
3. Model Training and Versioning: AI model training involves iterative experimentation with different algorithms, hyperparameters, and datasets. Object storage stores training data, model checkpoints, and metadata, facilitating efficient model versioning and management.
4. Model Deployment and Serving: Once trained, AI models need to be deployed and served to process incoming data and generate predictions. Object storage can store model artifacts, configuration files, and associated resources required for model deployment in production environments.
5. Data Preprocessing Pipelines: Object storage stores intermediate data generated during data preprocessing pipelines, which prepare raw data for model training. This includes tasks such as data cleaning, feature engineering, and normalization.
6. Collaborative AI Projects: Object storage enables collaboration among data scientists and AI researchers working on shared projects. Teams can store and share datasets, notebooks, code, and experiment results in object storage repositories, facilitating collaboration and knowledge sharing.
7. AI Model Evaluation and Monitoring: Object storage stores evaluation metrics, performance logs, and monitoring data generated during AI model evaluation and deployment. This data helps monitor model performance, detect anomalies, and improve model accuracy over time.
8. Data Versioning and Lineage Tracking: Object storage stores data lineage information, tracking the origin and transformation history of datasets used in AI workflows. Data versioning ensures reproducibility and auditability of AI experiments and results.
9. AI-powered Content Management: In applications such as image recognition, natural language processing, and recommendation systems, object storage stores multimedia content, textual data, and user interactions used to train and deploy AI models for content management and personalization.
10. Scalable Infrastructure for AI Workloads: Object storage provides a scalable and flexible infrastructure for deploying AI workloads in cloud environments. Cloud-based object storage services offer high availability, durability, and performance, supporting elastic scaling of AI applications based on demand.

Overall, object storage plays a critical role in supporting the end-to-end AI lifecycle, from data ingestion and preprocessing to model training, deployment, and inference, while also facilitating collaboration, scalability, and reliability in AI projects.

If I can share more with you about Cloudian object storage or any of the specific usage topics around AI, please let me know. I am always happy to help.

Until next time,

The Vanimal

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