In today’s digital world, Big Data Analytics has emerged as a critical component for the modern enterprise, and deriving meaningful insights from data has become paramount. However, choosing the right infrastructure to support Big Data Analytics can be a daunting task. But with so much at stake, how is any organization to decide whether they should host their data on-premises, on the cloud, or in a hybrid format? Don’t fret, our team is here to help you pick the solution that’s right for you.
The essential things you need from a data analytics platform
Security and performance are vitally important if you want Big Data Analytics as it usually involves security-sensitive applications that have to cope with ever-increasing amounts of data. And there are countless case studies which show that a proven way to do this is with on-premises implementation together with an in-memory database. Not to mention the benefit of being able to install a solution on your existing hardware, giving you full control over your own data and enabling you to integrate it with other existing applications and databases.
Is the cloud better for strategic thinking?
A separate on-premises data center may be ideal for operating an in-memory database, but it’s not always the best solution for the overall IT strategy of the entire company. If you think outside of the technical requirement, the cloud offers more flexibility and is often more cost-effective with demand-oriented billing. Plus, you can reduce the IT administration effort needed, as third-party cloud providers take over both maintenance and updating.
But all of this is irrelevant if your chosen cloud isn’t set up for data analytics. One of the most important features of an in-memory database is that it includes analytics tools that are cloud-ready. This is the only way to ensure that Big Data Analytics can be flexibly adapted to the changing requirements of your organization. And if you already use a cloud solution such as Amazon Web Services, Google Cloud, or Microsoft Azure the most strategic thing you can do is find an in-memory database that enables you to do your analytics with what you already have – with no costly implementations or changes to your infrastructure. Let’s go over the key differences between on-premises and cloud-based solutions for Big Data Analytics.
On-Premises vs. Cloud for Big Data Analytics: 5 Key Differences
1. Performance
- On-premises: Due to its proximity to an organization’s data sources and architectures, on-premise infrastructure can achieve low latency if it’s well-maintained, appropriately sized, and regularly updated.
- Cloud: With virtually limitless resources, the cloud infrastructure can provide the necessary performance for advanced data analytics when used with a modern data platform.
2. Scalability
- On-premises: Scaling could be more time-consuming and complex since it requires a ton of additional planning, testing, and deployment efforts to a physical location. As your data analytic workloads increase, an organization may find it difficult or costly to keep up with the increasing demands.
- Cloud: A cloud infrastructure is more forgiving since you can scale up or down as needed when your analytic workloads evolve over time.
3. Security
- On-premises: Enterprises bears sole responsibility for their security so this means the company has full autonomy over security practices and tools, which can present both advantages and disadvantages.
- Cloud: Both the service provider and the customer share the responsibility of cloud security. Depending on the service, the provider oversees certain layers of the security stack, while the customer is responsible for the rest.
4. Functionality
- On-premises: The organization has complete control over how things operate and the long-term roadmap. The company purchases and maintains the infrastructure needed to support its desired levels of performance, storage requirements, and data architectures, such as data warehouses and data lakes.
- Cloud: Cloud service providers offer a faster pace of innovation and greater availability of cutting-edge technologies since cloud service providers are continuously expanding what they offer.
5. Costs
- On-premises: The bulk of the expenses stem from buying, handling, and upkeeping data center infrastructure. Capital expenditures, especially for hardware, tend to be the biggest cost drivers.
- Cloud: By adopting a cloud computing model, enterprises only need to pay for the resources they actually use, without the added expenses of maintenance and upkeep. The price scales up or down based on the amount of resources consumed.
On-Premises vs. Cloud: Pros and Cons
The decision of whether to implement on-premises or Loading...cloud analytics can have significant implications for an enterprise. Either data infrastructure has its own set of advantages and disadvantages, ranging from control over security practices and cost factors for maintenance and scalability. Below is a brief overview of the pros and cons of having an on-premises or cloud-based analytic infrastructure.
On-premises pros:
- Complete control over security practices and tooling
- Autonomy over hardware and long-term roadmap
- No reliance on third-party providers
On-premises cons:
- High upfront costs for purchasing and maintaining infrastructure
- Slow pace of innovation compared to the cloud
- Limited scalability and flexibility
Cloud pros:
- Pay only for resources used, without maintenance and upkeep costs
- High scalability and flexibility
- Access to cutting-edge technologies and faster innovation
Cloud cons:
- Limited control over security practices and tooling
- Dependence on third-party providers
- Operating expenditures dominate costs
Choose a solution to fit your strategy – not the other way around
We don’t want to sit on the fence here, but really it all comes down to what you’re trying to achieve with your data analytics – and the big strategic idea that’s driving all this analysis in the first place. After all, one man’s hybrid cloud is another woman’s on-premises nightmare.
Ultimately, if you’re a decision-maker you need to ask yourself the following three fundamental questions:
- How much flexibility does the IT infrastructure require? Big data analytics that constantly operate under a full load doesn’t necessarily need to be able to scale flexibly up and down.
- Can the cloud really reduce costs for you? The answer to this question varies greatly depending on the specific application.
- How dependent is your organization on the services of a particular cloud provider? If you have vendor lock-in, make sure the database is set up for that vendor.
Inevitably the answers to these questions may vary over time. That’s why we believe the key is finding an in-memory analytics database that works both on-premises and with your cloud operations – and at best even in parallel and on different platforms. This will give you maximum flexibility for a data strategy today, tomorrow, and in the future. Learn more about Exasol’s high-performance in-memory analytics database today or sign up for a free trial to experience the power of Exasol for yourself!