The way organizations manage their data infrastructure is undergoing a significant shift. More and more companies are recognizing the advantages of decoupling storage and compute, which leads to better performance, cost savings, and scalability. This trend is driven by the increasing complexity of AI and ML workloads, which require flexible, high-performing systems.

Ali Ghodsi, CEO of Databricks, is a prominent advocate for this shift. In a recent talk, he emphasized the importance of organizations taking control of their own data. He encouraged companies to stop relying on vendors like Snowflake and Databricks to manage their data and instead use data lakes built on object storage. The benefits? More control, lower costs, and the ability to scale data infrastructure to meet growing demands.

Ghodsi’s message is part of a broader movement of vendors building, selling and advocating for more cost-efficient and flexible data architectures. Traditional systems, where storage and compute are tightly integrated, have proved inadequate for handling the massive data volumes and processing needs of AI and ML. More than ever, vendors like Databricks, are heavily investing in compute and leaving storage to best in class object storage software. The peak realization of this strategy is in Modern datalakes, often called lakehouses. Modern datalakes combine the flexibility of data lakes with the performance of data warehouses.

This decoupling of storage and compute, championed by vendors like Databricks, marks a pivotal shift in data architecture, enabling organizations to build highly flexible and scalable data infrastructures that are ready to meet the demands of AI and ML workloads while maximizing control and minimizing costs.

Decoupling: Why It’s a Game-Changer

Across the industry, many are recognizing that the monolithic systems of the past simply don’t cut it anymore. Modern datalakes, powered by object storage like MinIO, are emerging as the standard for future-facing infrastructure. This shift isn’t just about saving money—though it does that, too—it’s about positioning organizations to handle the data demands of tomorrow while working with the systems, models and tools of AI/ML today.

In a world where data is growing exponentially and AI/ML workloads are becoming more prevalent, the need for flexible, cost-effective infrastructure is paramount. Traditional data platforms, like Hadoop, often integrated storage and compute, which sounds efficient in theory but leads to inefficiencies in practice. With these traditional architectures, you end up paying for compute resources that sit idle, or storage that’s underutilized. 

With a decoupled architecture, you can scale storage and compute independently. For AI and machine learning, this is a huge advantage: massive datasets can be stored efficiently, and compute resources can be allocated dynamically for model training, data processing, or analysis.

Building a Modern Datalake with Object Storage Anywhere

If you’re looking to build a modern datalake that can handle the demands of AI and ML, high-performance object storage is essential. MinIO, for example, offers AIStor that’s optimized for large-scale data. By using a system like MinIO, organizations can ensure their modern datalake is highly scalable, reliable, and performant—three things that are critical when working with large AI/ML datasets.

MinIO can deployed on-prem, on private clouds, on public clouds, in colos, on the edge or wherever your workloads require. All on easily acquired commercial hardware. This is where the magic of the modern datalake really comes into play: you can leverage your object store for data lakes while enjoying the performance advantages of a data warehouse anywhere you need to all without being locked into expensive proprietary solutions from data hungry vendors looking to create artificial walled gardens to trap you in.

In practical terms, this means your data scientists and machine learning engineers can query and access massive amounts of data for training models directly from the object store wherever that data needs to be. This is what it means to truly control your own data.

Securing the Future

As organizations rethink their data architectures, ensuring the security of vast amounts of data is more important than ever. MinIO Key Management Server (KMS) offers a scalable, highly available solution for managing billions of cryptographic keys, which are crucial for encrypting data at the object level. The KMS integrates seamlessly with hardware security modules (HSMs) and cloud-based HSMs, providing a strong foundation of trust for encryption operations, whether in the cloud, on-premises, or at the edge.

MinIO also supports multi-tenancy, allowing organizations to isolate different teams or departments through encryption enclaves, ensuring that sensitive data is protected and that compliance with regulatory requirements, like GDPR and HIPAA, is maintained. Coupled with identity and access management (IAM), server-side encryption, and audit logging, MinIO ensures your data is safeguarded at every layer of the modern datalake architecture

Architecting the Future

To remain competitive in the AI and ML era, organizations must rethink their data strategies. The lakehouse model is rapidly becoming the gold standard for modern, scalable data environments. By adopting a flexible, high-performance storage solution instead of blithely handing over their data to vendors, businesses can ensure they are equipped to handle the data demands of today and the challenges of tomorrow. Let us know what you’re building at hello@min.io or on our Slack channel.