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6 Reasons Why It's Better to Run Hadoop in the Cloud
Want to begin taking advantage of the power of Hadoop for managing and analyzing big data? If so, you have a lot of decisions to make regarding how and where to store your big data and run your Hadoop operations. The cloud is ideal for leveraging Hadoop, and here’s why.
1. Smaller Investment for Larger Gains
Big data is a resource hog. It takes a significant investment in hardware to house and process huge sets of data, and not all businesses can (or should) make this investment in hardware and resources from the beginning. Using the cloud for Hadoop operations allows you to take a tiny dip in the big data pool before throwing significant funds into infrastructure before you even get started. You can use Hadoop on a trial basis in the cloud with very little investment, and the cloud is easily scalable as your data and analysis grow.
2. Quickly Scalable According to Needs
Scalability is closely related to the first issue: the investment it takes to get started with Hadoop is significant. The cloud is structured so that as your data sets expand, you add users, and need more analysis from Hadoop, your cloud platform can grow with you. In the cloud, you don’t have to halt growth and expansion when resources are tight. The delays associated with the time it takes to acquire and set up all the servers you need for expansion can be as costly as the new hardware. With the cloud, you can grow when it’s time, without delays for funding or setting up new infrastructure.
3. Pay for What You Need, When You Need It
Running all that hardware is also expensive. Usually, it requires that hardware is up and running 24-7, even if you’re only working with Hadoop analysis once per day or less. Servers have to be powered and maintained and operational to capture data as it is generated. With cloud services and a pay-as-you-go plan, you only pay for what you need when you need it. This eliminates the expense of powering and maintaining equipment 24-7 that you only need a couple of hours per day.
4. Matching Resource Needs with Workload Requirements
Hadoop jobs aren’t homogeneous, meaning some jobs take a lot of memory but little bandwidth, while other jobs suck up lots of bandwidth but don’t require lots of memory. In a cloud environment, you don’t have to invest in hardware capable of the maximum amount needed—the cloud can allot resources as Hadoop jobs require. The cloud accommodates a diverse range of Hadoop job requirements.
5. Putting the Data Where the Apps Are
Most organizations are already running most or all of their applications in the cloud, so it only makes sense to store and run Hadoop operations there, as well. With the Full Metal Cloud (which uses no hypervisor) latency problems are a non-issue. You can run Hadoop jobs in the cloud as quickly and powerfully as if they were in servers on site.
6. Solving Multi-Tenancy Issues
In environments in which multiple users are working with Hadoop jobs simultaneously, issues arise when users’ jobs begin interfering with one another. Multiple users can also present security issues within the IT infrastructure. Often, systems admins lock down the amount of memory allotted to Hadoop jobs, meaning that a user who does have a job requiring lots of resources can’t get to those resources. Using the cloud eliminates all of these issues, allowing users to run the Hadoop jobs they need to when they need to.
Visit Bigstep today to see how the Full Metal Cloud can empower your Hadoop goals. A free trial is available.