5 Keys to Getting the Most Out of Big Data Apps

Big data applications are making the transition from proof-of-concept to production, and with this transition comes demand for application resiliency so that results are reliable and secure. Ensuring that big data apps are resilient can prevent problems like failure of apps under too-large data sets, or questions raised due to unexpected data losses during processing.

As with most processes, incorporating application resilience during the development process is far better than trying to fix problems once an application has gone from the sandbox into production. Preventing problems is ultimately less costly in terms of both time and money. Here are 5 ways to make sure your organization can get the most out of its big data apps.

1. Ensure Big Data Apps Scale Properly

Just because you successfully test big data apps with smaller datasets doesn’t mean they won’t buckle underneath larger datasets. You may get an app to work just fine with a smaller set of data only to find that with a dataset of the size you plan to use in production, the app takes too long or fails altogether. Your big data apps should be able to handle production-sized datasets, and should be able to handle datasets that may be larger in different dimensions (whether they’re deeper, more detailed datasets, or broader ones, for example). Scalability should be tested and proven up front, because otherwise you’ll find that rebuilding and recoding is frequently necessary, wasting resources at every step.

2. Know What’s Going on With Data, Code, Infrastructure and the Network

When big data apps experience problems, you need to be able to locate where the problem is. It may be located in the data itself, in the code, in the hardware used to run the app, or in the network. By isolating the location of problems quickly, you can fix them faster. When testing apps, you need to learn how long each step in the process typically takes, so that if one of those steps is unusually protracted, you can investigate and isolate the problem more readily. After you believe you have fixed a problem, continue to monitor the application steps where the problem occurred before, so you can be confident that it really has been addressed properly. 

3. Ensure There’s an Audit Trail and no Loss of Data

When your apps are designed to include an audit trail showing who used the application, what data was ingested, and what policies (whether corporate or governmental) apply, you can be more confident that your big data apps will meet requirements for security, privacy, and governance. Moreover, your big data apps need to be auditable to ensure that every step can be accounted for. Test this capability in the “sandbox” stage and when it’s deployed. In certain industries (particularly financial), not accounting for every single transaction and process step can lead to serious legal problems.

4. Apps and Data Should Be Portable as Technology Evolves

Big data apps are moving from experimental to production-ready, and at the same time, technology is continuing to advance. In general, this is good news, because it can help you run big data apps faster and derive more insights from data. But if your big data apps can’t work with different platforms and products, you’re dooming them to limited usefulness, and a shorter lifecycle. Is your data available to the end-user via standard APIs and SQL? Can data processed through MapReduce be processed by Spark or Tez without a major code overhaul? If the answer is yes, you’re on the right track, and your big data apps will be more “future-proof.”<

5. Make Sure Your Bare Metal Computing Delivers on its Promises

Many organizations are turning to bare metal computing for its speed advantages. But why go to the trouble if storage or networking is slow? The speed advantages of bare metal computing can be significantly eroded unless your provider also offers the fastest networking and storage. If you want to make the most of bare metal computing then your provider should offer the fast networking and fast storage you need to make those blazing speeds really mean something.

Bigstep’s Full Metal Cloud is the obvious choice for bare metal computing. With the latest version of Cloudera Hadoop Distribution, 4 to 40 Gbps connectivity, and all-SSD pseudo-distributed storage, you can get speeds up to 40 Gbps between compute instances and their attached storage blocks, so I/O bottlenecks are no longer a concern. 

When you work hard to create resilient big data apps, test them thoroughly, and make sure they are flexible and scalable for the long term, it only makes sense that you would run them in a bare metal environment like Full Metal Cloud, which allows your big data apps to sprint at thoroughbred speed without being slowed by storage or connectivity concerns.