CIO Influence Interview with Tomer Shiran, Founder and Chief Product Officer of Dremio

Tomer Shiran, Founder and Chief Product Officer of Dremio, talks about key considerations in managing data across diverse infrastructures and the challenges organizations face in migrating data warehouses to the cloud and more in this interview:

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Hi Tomer, please share the story behind the founding of Dremio.

Dremio was born out of a clear need I observed during my years in the big data and analytics space. When I was at MapR, it became evident that while enterprises were generating massive amounts of data, the ability to access, analyze, and make data-driven decisions remained cumbersome and slow. Traditional data platforms often locked data away, creating bottlenecks for the teams that needed real-time insights. Dremio was founded with a simple yet powerful mission: to unlock the potential of data for everyone. We envisioned a platform that democratized data access, where users could query data directly from its source at lightning speed without having to rely on data warehousing or complex ETL processes. This vision has driven us to where we are today—enabling organizations to be more agile and innovative with their data.

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As Founder, and Chief Product Officer, how do you ensure Dremio’s product roadmap aligns with the evolving needs of modern enterprises, especially in the realms of AI and self-service analytics?

As Chief Product Officer, I focus on staying close to our customers and the trends shaping industries. Dremio’s roadmap is driven by three key factors: feedback from enterprise customers, the latest advancements in AI and machine learning, and the growing demand for self-service analytics. We continually evaluate how organizations are using data and where their pain points lie. The rise of AI has reshaped how businesses approach data; enterprises now need faster, more agile ways to harness their data for AI-driven insights. By integrating AI capabilities into our platform and ensuring we offer self-service tools for every type of user, we allow businesses to be more innovative without compromising governance or security.

With the growing adoption of data mesh architectures, how does Dremio facilitate the implementation and management of such architectures within large organizations?

Data mesh architectures represent a shift from centralized data lakes or warehouses toward a more decentralized model, where different teams own and manage their own data. Dremio is perfectly suited for this paradigm.  Unlike other offerings, our platform’s core architecture enables data to remain distributed, eliminating the need to move data into a central repository. Dremio allows enterprises to easily discover, query, and govern data across these distributed domains, ensuring each business unit can operate independently while still maintaining visibility and control over the entire organization’s data landscape. Additionally, with features like the semantic layer and data reflections, Dremio ensures consistency and reliability across these distributed data sets.

Enterprises today are increasingly adopting hybrid and multi-cloud strategies. According to you, what are the key considerations when managing data across diverse infrastructures?

The shift towards hybrid and multi-cloud strategies brings flexibility but also complexity in managing data across diverse infrastructures. Key considerations include:

• Data Governance: Ensuring consistent data governance policies across different environments is crucial. Dremio enables centralized governance, allowing businesses to manage data across clouds without compromising on compliance or security.
• Data Portability: Data should be easily transferable between clouds without vendor lock-in. Dremio’s open architecture, based on Apache Iceberg and Apache Arrow, ensures that enterprises can access and move data seamlessly across various cloud environments.
• Performance and Cost Optimization: Managing data in a hybrid environment requires careful consideration of both performance and costs. Dremio’s cloud-native architecture is optimized to minimize data movement and enable on-demand, high-performance querying across diverse infrastructures.

With cloud migration being a significant trend, please highlight the top challenges that enterprises face when migrating their data warehouses to the cloud.

To stay at pace with the rapid evolutions in AI and ML, companies need to stabilize the underlying data infrastructure. But what happens when the immense amount of data held today isn’t properly managed? Migrating data warehouses to the cloud is a complex process that involves several challenges, including:

• Data Gravity: The sheer volume of data stored on-premise can make migrations slow and costly. Enterprises need to prioritize what to move and when.
• Performance Consistency: Ensuring that the performance of analytics workloads remains consistent post-migration is critical. Latency and network bandwidth between on-prem and cloud environments can affect performance.
• Data Governance and Security: Migrating to the cloud also involves ensuring that security policies are enforced in the cloud environment. Data privacy and governance become even more critical during this transition.

Dremio’s solution addresses these challenges by enabling enterprises to run analytics directly on both their cloud and on-prem data, eliminating the need for complex and costly migrations to traditional cloud data warehouses.

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What emerging trends do you believe will shape the future of data platforms, and how is Dremio positioning itself to stay ahead?

Several key trends are reshaping the future of data platforms:

• Data Democratization: Businesses need to give more people access to data without overwhelming IT teams. Dremio’s self-service analytics capabilities empower teams across organizations.
• AI and Machine Learning: As enterprises continue to embrace AI, they will require data platforms that can handle complex, real-time data queries at scale. Dremio’s performance optimizations and native AI capabilities support this.
• Data Governance in a Decentralized World: With the rise of data mesh, governance will play a critical role in ensuring enterprises can manage decentralized data environments. Dremio’s strong governance and security capabilities help businesses manage this transition effectively.

Before we wrap up, what five tips would you give to tech entrepreneurs aiming to innovate in the data and analytics space?

There are many to consider, but I would focus on the following five tips:

1. Focus on Solving Real Problems: Identify the pain points your target market faces and craft solutions that address them in innovative ways.
2. Prioritize Customer Feedback: Stay close to your customers; their feedback will help you refine your product roadmap and grow your business.
3. Stay Ahead of Trends: Keep an eye on emerging technologies like AI, machine learning, and cloud computing, and adapt your offerings accordingly.
4. Build an Open and Flexible Architecture: Vendor lock-in is a concern for many enterprises. Building a platform that supports open standards can help attract and retain customers.
5. Create a Strong Culture: Building an innovative company requires a talented and motivated team. Invest in creating a culture that fosters creativity and collaboration.

Thank you, Tomer, for sharing your insights with us.

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