As 2016 is around the corner, the question is what this year will bring for Big Data. Here are my top assumptions for the year to come:
- The growth for relational databases will slow down, as more companies will evaluate Hadoop as an alternative to classic rdbms
- The Hadoop stack will get more complicated, as more and more projects are added. It will almost take a team to understand what each of these projects does
- Spark will lead the market for handling data. It will change the entire ecosystem again.
- Cloud vendors will add more and more capability to their solutions to deal with the increasing demand for workloads in the cloud
- We will see a dramatic increase of successful use-cases with Hadoop, as the first projects come to a successful end
What do you think about my predictions? Do you agree or disagree?
while I agree that classic RDBMS are of a shrinking species, I feel that NoSQL DB will be favoured over Hadoop. Hadoop is not suitable for workloads where a low latency is required. what I believe however is that Hadoop is the choice for their big data analytics stack. Spark will definitely do it in the area of data (pre) processing and predictive analytics due to its versatility.
Hadoop itself can hardly be compared to a database; I rather think that more and more NoSQL DB’s will start to use HDFS as solid base …