Big data promises much in terms of business value, but it can be difficult for businesses to determine how to go about deploying the architecture and tools needed to take advantage of it.
Everything from descriptive statistics to predictive modeling to artificial intelligence is powered by big data. And what an organization wants to accomplish with big data will determine the tools it needs to rollout.
At the 2017 Dell EMC World conference on Monday, Cory Minton, a principal systems engineer for data analytics at Dell EMC, gave a presentation explaining the biggest decisions an organization must make when deploying big data. Here are six questions that every business must ask before getting started in the space:
The first question to ask is whether your organization wants to buy a big data system or build one from scratch. Popular products from Teradata, SAS, SAP, and Splunk can be bought and simply implemented, while Hortonworks, Cloudera, Databricks, Apache Flink can be used to build out a big data system.
Buying offers a shorter time to value, Minton said, as well as simplicity and good value for commodity use cases. However, that simplicity usually comes with a higher price, and these tools usually work best with low diversity data. If your organization has an existing relationship with a vendor, it can be easier to phase in new products and try out big data tools.
Many of the popular tools for building a big data system are cheap or free to use, and they make it easier to capitalize on a unique value stream. The building path provides opportunities for massive scale and variety, but these tools can be very complex. Interoperability is often one of the biggest issues faced by admins who go this route.
Batch data, offered by products like Oracle, Hadoop MapReduce, and Apache Spark, are descriptive and can handle large volumes of data, Minton said. They can also be scheduled, and are often used to build out a playground of sorts for data scientists to experiment.
