Big data has become one of the most revolutionary technologies of today’s generation. There are all kinds of applications that leverage big data and many experts have demonstrated how data analytics will evolve in the next decade. Even though companies are realizing the upside of adopting and implementing big data solutions, they are also struggling with some challenges. The following are some of these big data-related challenges that companies are facing when dealing with large volumes of data:
1. Using Appropriate Technologies
One common problem that companies run into when they get started with big data is how to pick the appropriate technologies. There is a host of big data technologies to choose from, and it can be tricky which suits your needs aptly. Common comparisons include Spark Vs Hadoop, Cassandra Vs HBase, etc. So, how do you decide which technologies are best suited for your data storage and analytical needs? Well, taking professional help is highly recommended for that. You can approach a vendor or big data expert such as K2View for big data consulting.
2. Data Quality Management
Ensuring that data quality is maintained is not a mean feat. Usually, you need to analyze and collect data from different sources and in different formats. For instance, an online store would need to collect data from social media, website logs, competitor’s website scan, etc. The formats of these databases could be different which can make it difficult to connect them with each other.
Another challenge in terms of data quality is accuracy. This is because the raw data collected is not 100% accurate and suffers from issues such as contradictive values, duplicate values, etc. To remove such issues, you can compare the records with different sources to ensure data accuracy. You can also merge similar records so that there are no duplicates.
3. High Costs
Implementing big data solutions comes at a high cost. If you have the on-premises solution, you need to invest a lot in hardware, staff (developers, administrators, etc.), electricity, etc. Even though many big data frameworks are open source, you still need to pay for setting up and configuring these software applications. If you are deploying a cloud-based solution, then you also need to bear the costs of cloud services.
To minimize big data costs, you need to take a closer look at your requirements. For instance, if you want to deploy cloud-based big data services, then you can pick a hybrid solution in which you put some of the processes on the cloud and some inside the premises which is cost-effective. You can also reduce costs by optimizing algorithms as that leads to lower power consumption, or seek cheaper data storage options, etc.
One of the fundamental principles of a big data projects is that it grows considerably fast. This raises the challenge of how to upscale with the least effort and minimal costs. The actual problem is not the introduction of new processing and storing operations but rather the complication of scaling up. After all, you would want the infrastructure’s performance to stay consistent after upscaling while staying within the budget.
To handle upscaling properly, improve your big data architecture. Also, analyze the algorithms and see if they are future-ready for the upscaling. Lastly, try to perform systematic performance audits on a regular basis to identify weak spots and fix them.
Big data is as groundbreaking as it is challenging. We have come a long way in the implementation of big data solutions in the past few years and notice a visible difference in how massive records are processed into invaluable insights about market trends, consumer behavior, etc. Still, there are difficulties in handling large databases. We have addressed some of the major ones in the points above. Hopefully, it will help.