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Why Data Storage Challenges Lead Many Businesses to Adopt NoSQL Databases

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Data analytics is rapidly becoming essential to business success. Organizations that are able to mine data for business insights will have the ability to outflank the competition and capture greater market share.

This is no easy task given today's data storage challenges.

The amount of structured and unstructured data continues to grow exponentially, much of it generated by Internet-connected devices.

In a recent survey by Datos IO, 59 percent of IT professionals said they expect the size of their organization’s database to double within the next two years. Of course, unstructured and semi-structured data constitutes more than 80 percent of data volume, and the type of data being processed and stored is increasingly diverse.

But size isn’t all that matters.

A lot of this data is trapped in isolated silos, making it difficult to aggregate the data for analysis.

Some experts have suggested that data scientists spend 80 percent of their time schlepping data around, and that 60 percent of the cost of a data warehouse project is spent on the extract, transform and load (ETL) operations necessary to move data from one place to another.

The Four NoSQL Data Storage Models

These challenges are leading more organizations to adopt NoSQL databases. Unlike relational databases that store data in columns and rows according to a fixed schema, NoSQL databases are flexible and dynamic.

There are four different NoSQL data storage models — document, graph, tabular and key-value — but all enable you to add data on the fly without a predefined schema. They can handle large volumes of structured, semi-structured and unstructured data that changes rapidly, and offer better performance than traditional database platforms. Horizontal scalability is another key benefit of NoSQL databases.

With traditional databases, scaling is vertical, meaning that you must migrate to a bigger server if your database exceeds the capacity of your current platform. NoSQL databases scale horizontally across multiple servers, including commodity on-premises and cloud-based platforms, making them more cost-effective from an infrastructure perspective. Many NoSQL solutions are even capable of distributing data across servers automatically.

According to a report just released by Allied Market Research, the NoSQL market in North America is expected to see a compound annual growth rate of more than 40 percent through 2020. Key-value stores should remain the most preferred type of NoSQL due to their use in in web applications, but document databases are increasingly attractive because of their simple design and basic ability to support analytical queries.

There are a number of open source NoSQL platforms, including Apache Cassandra, CouchDB and mongoDB. However, open source solutions do not provide all of the features enterprises need and come with a significant learning curve.

A Single Platform That Supports All NoSQL Data Storage Models

DataStax Enterprise is a commercial offering based upon Apache Cassandra that features advanced indexing and search and powerful integrated analytics in a single platform that supports key-value, tabular, document and graph models. Forrester Research named DataStax a leader in “The Forrest Wave: Big Data NoSQL, Q32016,” stating that it provides “fault tolerance, scale-out architecture, low-latency data access and simplified administration.”

Technologent has forged a partnership with DataStax to deliver the DataStax Enterprise solution. In our next post we’ll expand upon the advantages of NoSQL and explore some of the use cases for DataStax Enterprise.

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