geograph-3150005-by-chris-morgan-100659661-primary-idge

HBase: The database big data left behind

HBase: The database big data left behind

A few years ago, HBase looked set to become one of the dominant databases in Big Data. The primary pairing for Hadoop, HBase saw adoption skyrocket, but it has since plateaued, especially compared to NoSQL peers MongoDB, Cassandra, and Redis, as measured by general database popularity.

The question is why.

That is, why has HBase failed to match the popularity of Hadoop, given its pole position with the popular Big Data platform?

The answer today may be the same offered here on InfoWorld in 2014: It's too hard. Though I and others expected HBase to rival MongoDB and Cassandra, its narrow utility and inherent complexity have hobbled its popularity and allowed other databases to claim the big data crown.

It started well for HBase. Writing in late 2014, I argued that Hadoop's preference for HBase, along with its ability to "scale limitlessly as load and performance demands increase simply by adding server nodes," would keep it as a top-three database for years to come. I was wrong.

Read Also:
How storage startup Rubrik models DevOps

According to DB-Engines, which tracks database popularity across a number of metrics (Google searches, job postings, forum mentions), HBase was on a tear for years, keeping pace with the top NoSQL peers. Early in 2015, however, HBase started to slide, even as MongoDB and Cassandra kept rising:

Some argue that DB-Engines' rankings may undercount an essential popularity metric: how much data is actually stored. By this metric, posits Ewan Leith, "HBase and Cassandra would be in the lead of 'new' DBs."

But a quick look at Redis, similar to HBase in terms of being a simple data store with limited applicability, suggests something else might be afoot. Redis, after all, started to taper off at about the same time as HBase. The reason for this dual decline is probably tied to the basic workloads both can support, as MongoDB's Mat Keep told me:

Of course Keep has a bias, but it's not unfounded. Given that much of MongoDB's success derives from its ability to support a broad array of operational workloads, Keep is also in a good position to highlight a key failing in HBase.

Read Also:
How To Read Analytics Clues for a Cross-Device Marketing Strategy

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
How storage startup Rubrik models DevOps

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Nanogrids, Microgrids, and Big Data: The Future of the Power Grid

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Big Data is Transforming Commercial Construction

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
Optimizing Project Staffing to Improve Profitability with Cortana Intelligence

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
Real or virtual? The two faces of machine learning

Leave a Reply

Your email address will not be published. Required fields are marked *