The process of scoping a big data project requires a multifaceted approach. In fact, there is almost something of an art form behind putting together something so technical. A project involving big data must be efficient, graceful, streamlined and executable in ways that other technical projects don’t require. What are the big factors that need to be looked at before an approach and application can be decided on? Take a look at the seven factors that must be considered for launching a successful big data project.
SLA
A service level agreement, or SLA, serves as the bedrock for any project that involves big data analysis. An SLA is a collection of requirements that an application needs to deliver. It is concerned mostly with what can be done, how quickly it can be done and how accurately it can be done.
Fault-Tolerance
Fault-tolerance is becoming increasingly more important as big data is getting more complicated. Fault-tolerance offers protection, isolation and remediation in the event that a fault occurs somewhere in a system.
Security
Security is a non-negotiable aspect of every big data project. The value of data in today’s world means that hackers are more creative and motivated than ever before. It is important that a system involving big data is capable of detecting, blocking and anticipating attacks.
Scalability
The title of big data may not really do this industry justice. The amount of data collected and processed every hour in the world is actually huge. It is certainly incalculable for the human mind. The large volume of data being processed every second makes scalability a big issue to consider. Any system that’s designed to handle big data needs to be able to handle ever-growing amounts of data. This means that a relevant system should be able to be as efficient and accurate when processing one piece of information as it is when processing an endless stream of information generated by an enterprise.
Integration
A big data project that is successfully executed won’t interrupt other components of a system. A new application must align smoothly with existing processes if a launch is to be successful. Unfortunately, many projects hit snags because of code that cannot be easily integrated or optimized. Time is lost on many projects as pieces of code go back and forth between developers. The workaround for this problem is to choose an application that offers functional code that is separated from operational code.
Covering Operational Code
Time and resources can still be lost to operational code long after an application has been launched successfully. The fact of the matter is that most mainstream developers have been trained in functional code. The ability to work with operational code is a rare and expensive expertise. This is why choosing an application for big data that can handle operational issues automatically is such a positive step for enterprises. Taking this roadblock off the table allows developers to focus on managing and optimizing a system using functional code instead of getting bogged down by operational code.
Easy Upgrades
Developers know all too well the fact that an initial launch is only the start of a project. Constant needs for upgrades and backward compatibility checks are time consuming and expensive. In addition, the efficiency and integrity of an application can be put at risk each time an upgrade is launched. Many enterprises actually abstain from performing necessary upgrades frequently because of the large size of the databases involved. What if upgrades could be simpler? The solution is to choose a big data application that is built to be supported in multiple versions. That same application should also be built with backwards compatibility in mind.
Starting a big data project comes with many complications. Being thorough before you begin will help make the process much easier from start to finish. Consider these factors and you’ll be able to properly scope out and execute your next big data project.