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VMblog's Expert Interviews: Dremio Talks Replatforming to Cloud Infrastructures, Cloud Migration Approaches, Analytics and More


As more and more organizations look into the possibility of moving their infrastructure and applications to the cloud in support of digital transformation, part of the decision making process they go through is to figure out the best approach to cloud migration in order to achieve long-term success.  While some favor lift-and-shift, others have come down on the side of replatforming to cloud infrastructure providers like AWS, Azure, and Google cloud.

Is there a single path to success?  What is the answer?  To learn more, I spoke with an industry expert, Tomer Shiran, co-founder and CEO at Dremio.

VMblog:  To jump right into the thick of things, what are the pros and cons of re-platforming to cloud infrastructure providers like AWS, Azure or Google Cloud, for example?

Tomer Shiran:  As companies re-platform to cloud infrastructure providers like AWS, Azure, and Google cloud, they position themselves to benefit from all that cloud has to offer: faster time to market, flexibility, elasticity, and a predictable cost model. However, each cloud vendor offers a wide range of data management platforms; leaving companies struggling to rationalize which service is best for a given workload. In addition, companies will eventually consolidate data from different services to perform their analytics. Even for data analytics, each vendor provides multiple options. It is common for companies to feel overwhelmed by this "tyranny of choice."

In addition, each of these options is specific to a given cloud platform vendor. These offerings are compelling, but they are not equivalent across on-prem offerings or similar offerings from other cloud vendors. Organizations need to understand what they're getting into - this is likely to be a long term relationship, with non-trivial costs to change.

VMblog:  What are the different approaches to migrating to the cloud?

Shiran:  Many companies are looking at how to start leveraging the flexibility and elasticity of the cloud, and, with that, especially for more mature companies, come many challenges because you may have different applications and data infrastructure that already exists in the organization. There are different approaches when it comes to moving your analytics to the cloud, however analytics is one of the workflows that we see moving to the cloud faster than others.

There are three fundamental strategies when it comes to moving a workload like analytics to the cloud. The first one is a lift and shift strategy, the second is a cloud native strategy and the third one is what we call a hybrid cloud strategy.

Hybrid cloud is the best approach for most companies. A hybrid cloud is a mix of on-prem and cloud services and allows you to evolve the balance of workloads between on premise and cloud over time so you can gradually migrate more workloads to the cloud while keeping a balance of where you're running different apps and services that don't require elasticity those can run in the on-premise infrastructure. 

VMblog:  How does Dremio ease the challenge of moving analytics to the cloud?

Shiran:  There are two trends happening right now in the industry. The first one is that analytics on modern data is incredibly hard, just the fact that it's no longer a world where you can have all your data in one relational database. Today we have data lakes and we have noSQL databases and just the size and complexity of the data makes it very hard to deal with and makes it very hard to make this data accessible to the users who want to consume it and drive insights from the data.

Business analysts and data scientists are entirely dependent on IT to get interactive access to the data they need. They've grown up with Google and being able to get instantaneous responses to questions and with smartphone applications where they can book travel within two minutes on their phone without waiting on anybody. When you combine these two forces, it introduces a real challenge for companies because the technology stack in the world of analytics infrastructure really has not evolved very much over the last 20 years. So we have data in different places with different tools.

Dremio simplifies and governs the process of achieving interactive speed on data from any source, at any scale, at any time, through a self-service model delivered on an open source platform. Dremio empowers data scientists and business analysts to discover, curate, accelerate and share data for their specific needs, without burdening IT.

Dremio provides a future-proof strategy for data, allowing companies to choose the best tools for analysts, and the right database technologies for applications, without compromising on the ability to leverage data to power the business.

VMblog:  What makes Dremio different from other technologies and vendors?

Shiran:  One of the challenges in accessing data is providing links between BI tools like Tableau, Qlik and Microsoft's Power BI, and the disparate data sources such as data lakes, NoSQL data stores and relational databases. Unlike traditional approaches which require building a data warehouse or rely on point-to-point single server designs, Dremio connects any analytical process to any data source and scales from one to 1,000 plus servers, running in the cloud, on dedicated hardware, or in a Hadoop cluster. With Dremio, data scientists and business analysts can perform critical data tasks themselves, without being dependent on IT. We bring self-service to the entire analytics stack.

VMblog:  What can we expect from Dremio in the next six months?  The next year?

Shiran:  We recently announced the newest version of our open source platform, Dremio 2.0, that includes support for Looker and Starflake Data Reflections. Over the next several months we will be supporting new open source initiatives that leverage Apache Arrow, for columnar in-memory analytics, as well as expanding our partner ecosystem and international presence.


Published Monday, June 11, 2018 7:42 AM by David Marshall
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