In IT departments around the globe, CTOs, CIOs, and CEOs are asking the same question: “How can we use Big Data technologies to improve our platform operations?” Your particular role could be responsible for solving for a wide variety of use cases ranging from real-time monitoring and alerting to platform operations analysis or behavioral targeting and marketing operations. The solutions for each of these use cases vary widely as well. But no matter which Big Data solution you choose, make sure you avoid the following 3 pitfalls.
Pitfall #1: Assuming a single solution fits all use cases
In a recent post, Liam Eagle of 451 Research looked at GoGrid’s Big Data product set, which is purpose-built for handling different types of workloads. He noted that variety is the key here. There isn’t a single one-size-fits-all solution for all your use cases. At GoGrid, for example, many of our Big Data customers are using 3 to 5 solutions, depending on their use case, and their platform infrastructure typically spans a mix of cloud and dedicated servers running on a single VLAN. So when you’re evaluating solutions, it makes sense to try out a few, run some tests, and ensure you have the right solution for your particular workload. It’s easy for an executive to tell you, “I want to use Hadoop,” but it’s your job that’s on the line if Hadoop doesn’t meet your specific needs.
As I’m sure you already know, Big Data isn’t just about Hadoop. For starters, let’s talk about NoSQL solutions. The following table lays out a few options and their associated use cases to help illustrate the point.
|Solution||Common Use Cases||Pros and Cons|
|Cassandra||(more…) «Implementing Big Data in the Cloud: 3 Pitfalls that Could Cost You Your Job»|