Making Comparison on Big Data solutions and Discussing Prospects

Big Data Solutions

In some of the recent years, you can see quite a lot of things that have taken place within the Big Data Ecosystem. First of all, the market got tremendously crowded by the big data infrastructure dealers. Whereas open source data infrastructure (Apache Hadoop, MongoDB, Apache Kafka and the rest) emerged to grant a valid solution to big data like a problem statement, legacy data infrastructure providers (IBM, Oracle, HPE, Teradata) latched on this prospect to grant more managed and viable offerings. Moreover, these legacy players tackled their top challenges to sustain new as well as growing requirements:

  • Improved scalability
  • Better performance
  • Deeper integration with NoSQL and Hadoop

The second thing is, this “crowding up” led to an apparent confusion among consumers, magnified by the truth that big data is moderately a recent as well as complex technology. It is not hard to determine which features actually matter for definite business use cases.

The main players

The vendor landscape of big data can generally be divided into 2 types of players: the first one is the old guard (HPE, Oracle, Teradata, etc.) & the second one is the new entrants (Hortonworks, Cloudera, Pivotal, etc.).

The comparison

We examined some big data vendors with the intention to find out what actually is favourable for customers & how all stack up. This is a qualitative evaluation & the benefits of every solution might vary based on the industry, user’s specific use case, and other metrics.

Architecture

Here, cross-platform big data tools generally score higher, this is where many players hesitate. New entrants such as Hortonworks and Cloudera are tied strongly with Hadoop. Databricks is a glint. On the other hand, Vertica 8 has broad integrations to HDFS, Apache Spark, and Kafka too, which indicates that customers can do data analysis as it is without moving or transforming it.

Vertica is bunched with built-in prediction modeling & geospatial analytical capabilities that provide it an edge over the other competitors. As per the recent release, Vertica also contains support for native algorithms of machine-learning. Under the latest release, for in-database ML, the parallel algorithms have been taken in the Vertica, in order that users can efficiently analyse data as well as make predictions devoid of exporting it out of Vertica. A lot of platforms currently do not provide built-in capabilities of data science.

Cost

In the global level, conventional database players have developed custom hardware around commodity components, & core revenues are derived through grand maintenance costs. Through the Draconian pricing model & the vendor lock-in stage, supremacy of these database giants is challenged from a cheaper and much cost-effective option.

Vertica has best and moderately priced licenses, in comparison with the other players (that also need a great deal of exertion in configuration). Besides big licensing fees from other vendors, many deployments also require large teams to deploy as well as run. Conversely, Vertica needs few specialists & configuration prerequisites can be tackled through the traditional developers. It is here that HPE Vertica has done a huge play & is nipping at opponents’ heels with the most cost-effective option.

Furthermore, the subscription-based model lessens the entry barrier for tiny players & it appears to have well paid off in India. This enables SMBs to implement the platform via paying a smaller amount upfront, & not be concerned about “vendor lock-in.”

In Conclusion

In case you are in search of sheer number-crunching aptitude of heavy data sets as well as prefer a SQL database, then choose Vertica—since data loads rapidly and is most suitable for heavy-duty queries. In a lot of manners, Vertica can be taken under use for big and small enterprises both and it is also suitable for thorough business intelligence. And if you desire a true columnar storage choice, Vertica has the finest analytics platform capabilities; therefore it is the perfect fit.

Presently, with data management targeting the cloud, the wars of DB will currently be fought in the cloud instead of on the premises. Competitors like Cloudera, Amazon Redshift & Snowflake Computing provide extra cloud elasticity.

Regardless of where we stand at the present, the track from here on would finalize the big data’s future. Other players would implement such features very soon and be ready for the competition.

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