Big Data, Hadoop & Cloud: Tackling a Chain of Emerging Challenges
In the current digital economy, data plays a vital role which is seen akin to the role of oil in traditional commerce. It is also believed to have gone through various stages of growth and evolution like oil. About a decade ago, data was in the initial phase, similar to “land grabbing”, where all companies wanted to ensure that they had access to consumer data. The next phase was the delineation, when the companies defined the boundaries and applications of big data. This is followed by the efficiency phase, in which companies see themselves now. Since data is already available and applications decided, it is required to find the correct match between expertise and technology.
However, many companies have found it difficult to navigate these phases. Even though several companies managed to find a way out of the delineation phase, they have since struggled with the efficiency stage. They have found it difficult to find the right experts and match them with the right technology. Here are the challenges they have faced:
Big data challenges
Big data has posed storage and analytics problems. Traditional storage systems have proved inefficient in storing the huge volumes of unstructured data. Also, since traditional systems involve huge cost and scalability implications, several companies have found refuge in the cost efficient and scalable open sources such as Hadoop, Spark and Apache. There are several such open sources for big data solutions and navigating and finding the one best suited may pose a separate challenge altogether.
Hadoop challenges
Hadoop is a great solution for several big data problems. However, management of Hadoop and its implementation are not easy. It requires extremely adept professionals to handle Hadoop on premises. The learning curve for the solution is also not easy and hence, the internal data team may often find handling Hadoop a challenge. It also poses scalability issues and involves great infrastructure cost, straining the resources of companies. This has prompted the shift to cloud based Hadoop solutions.
Challenges in cloud migration
Cloud based solutions offer a degree of freedom in scaling up the required data needs of the company. The process is faster and easier as well. The efficiency and cost-effectiveness can be attributed to the freedom from investment on premise infrastructure. However, the process has its own challenges. These include performance issues, reliability and accessibility of data and its scalability. Here are a few ways to tackle these challenges:
- Think long term - Companies which are opting for Hadoop or similar solutions must think of future possibilities in terms of new technology. Technologies such as AI, VR and IoT will generate more data and in different formats. Hence, technology platforms being built by companies must account for such increase in data and its interpretation and analysis.
- Get the right experts - In the long term, those companies will be more successful which can make optimum utilization of their data. This is possible by engaging the right experts on the job. Whether these experts are hired or employed will depend on the companies need and resources.
- Ensure data security at the company’s end - While cloud providers enable easy storage and analytics solutions, companies must ensure data security at their end. Since the data is owned by the company, they must ensure all necessary security measures are undertaken.
Big data is poised to grow bigger and the challenge it poses today will only increase with time. The current lack of experts in the field warrants more job opportunities for people with expertise in data analytics. Hence, those looking for a career here may find it worthwhile to enrol in any of the good online data analytics courses.
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