Choosing the Right Big Data Tool for the Right Job

Bookmark and Share

Along with an increasing flow of big data that needs to be captured and analyzed, IT departments today also have more solution choices than ever before.

However, before making a solution selection, organizations need to understand their requirements and also evaluate the attributes of the possible tools.

Hadoop and NoSQL databases have emerged as leading choices by bringing new capabilities to the field of data management and analysis. At the same time, the RDBMS, firmly entrenched in most enterprises, continues to advance in features and varieties to address new challenges.

Three experts on this topic, including Brian Bulkowski, CTO and co-founder of Aerospike; Kevin Petrie senior director and technology evangelist at Attunity; and Reiner Kappenberger, global product management at HPE Security - Data Security, discussed how to leverage big data with these solutions in a recent DBTA webinar.

According to Bulkowski, Aerospike discovered advertising companies need fast effective analytics on very large datasets.

As a result, the company set out to perfect the best architecture to address this need, Bulkowski said, which then led to a realization that a variety of enterprises were also looking for similar tools.

“The architecture we’ve seen in the last few years is starting to flow through to enterprises,” Bulkowski said. When people  talk about big data they primarily talk about Hadoop and Hadoop-related analytics with systems such as Spark. “In reality what we’ve seen at Aerospike is a wide variety of analytic solutions.”

However, platform proliferation can result in a high management and cost burden, Petrie noted.

According to Petrie, Attunity can address these issues by moving data easily with no manual ETL coding by pointing to source and target.

Its software can support relational databases that typically handle the online transactional processing, the data warehouses that deal with analytic activities for the business, and the NoSQL databases that will manage data with non-traditional tables, Petrie said.

Once a solution is picked, securing that data is equally as important as finding the right tools to tackle the big data ecosystem, Kappenberger stressed.

“We’ve seen that people who take proactive approach in big data ecosystems have been able to scale much faster and much larger because they can collect and analyze more data than they have before,” Kappenberger said.

Traditional security is a layered approach, emphasized Kappenberger, who recommends using a  variety of approaches to protect data, including encryption.

To view a replay of this DBTA webinar, go here.