Big data is an incredibly valuable commodity. Unfortunately, many brands put the cart before the horse when they implement their big data strategies. They begin scaling their data harvesting campaigns without making sure they have the right infrastructure in place.
Bernard Marr, a big data author and keynote speaker, states that big data has become more accessible to SMEs over the last few years, primarily due to advances in cloud technology.
“Until recently it was hard for companies to get into big data without making heavy infrastructure investments (expensive data warehouses, software, analytics staff, etc.). But times have changed. Cloud computing in particular has opened up a lot of options for using big data, as it means businesses can tap into big data without having to invest in massive on-site storage and data processing facilities.”
Setting up your big data infrastructure isn’t difficult, provided you take the right steps. Here are some questions you need to ask beforehand.
1. How Homogenous is My Data?
You have two main options for setting up your big data infrastructure. Purchasing the infrastructure from Teradata, Splunk or another big data storage solution offers a high ROI for many brands.
However, Cory Minton, Systems Engineer for Dell EMC, states that purchasing a big data system has one significant limitation – these systems are only ideal for handling homogenous data sets.
If you plan on collecting and extracting more diverse data sets, you should plan on building your own infrastructure instead. Companies that plan on collecting 20 or more fields of data from multiple sources should create their own data system from scratch. For more simple applications, such as using an Instagram bot for more followers, you can rely on an existing infrastructure instead.
2. How Can I Secure My Data?
Data breaches are occurring more regularly every year. One study estimates that the cost of a data breach is $158 for every record compromised.
Your infrastructural decisions play an important role in securing your data against breaches. According to EY, there are four things that need to be taken into consideration while securing data:
- Data encryption
Your big data infrastructure is only as strong as its weakest link, so all of these factors need to be carefully implemented.
3. Should I Stream or Batch Data?
There are a couple of ways that you can handle data. You can use batching products, such as Hadoop MapReduce or Apache Spark. You can also use a streaming product, such as Apache Kafka or Flink.
There is a tradeoff between the two options. Streaming allows you to handle data more quickly, while batching preserves the integrity of your data better. Ramaninder Singh, a big data engineer for the Bettson Group and a Hadoop expert, provides a succinct explanation of the differences.
“Batch processing is very efficient in processing high volume data. Where data is collected, entered to the system, processed and then results are produced in batches. Here time taken for the processing is not an issue. Batch jobs are configured to run without manual intervention, trained against entire dataset at scale in order to produce output in the form of computational analyses and data files. Depending on the size of the data being processed and the computational power of the system, output can be delayed significantly.”
You need to consider the sensitivity of your data and the need for scalability.
Big data is a force to be reckoned with for countless industries. However, it is useless without the right infrastructure behind it.
Before investing in a big data solution, you need to make sure your infrastructure is properly established. Everything else will fall into place after that.