Big Data Tools: Top 10 Essential Features

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The Big Data analyzes can transform data into information , in order to exploit them for various purposes: decision making, cost reduction, competitive advantage, developing new services / products

In this context, there are more and more Big Data tools on the market . However, not everyone is equal. Find out what are the ten essential features of a good Big Data analytics tool.

 

The results of Big Data tools must be integrable

The insights obtained from the data models must be able to be used with other applications , in order to make decisions in real time.

Thus, insights must be created in a format that can be integrated into the different decision-making platforms . They can thus be applied to an event data flow in real time.

Big Data tools must support different types of data

Before performing Big Data analysis, Data Scientists must perform Data Cleansing , labeling, and organizing data.

Big Data tools need to support different types of data from multiple sources, as well as integration protocols and scenarios to speed up and simplify these steps.

Big Data tools need to simplify data mining

One of the steps in Big Data analysis is the discovery and data mining phase . This step helps to understand the context of a problem within the company in order to better formulate the analytical questions.

A good Big Data tool must be used to support the hypothesis testing process on the different data. Thus, bad assumptions can be quickly dismissed, and new connections between data can be discovered .

 

Big Data tools must support different types of analysis

 

There are different types of data analysis methods : predictive analysis, real-time analysis, Machine Learning, Business Intelligence …

each of these approaches can bring different results to the company. Thus, the best Big Data tools must support the different methods of analysis.

Big Data tools must offer scalability

Data Scientists often test different data models on small data sets.

However, in order to be economically exploitable and deliver results quickly, these models must offer scalability to be able to be applied to large datasets at lower cost.

Analytical tools must allow version control

In the context of large-scale analytical projects, it is possible to adjust different parameters of the analytical models. However, while some of these changes may seem promising at first glance, they can cause unexpected problems once in production .

A version control feature built into a Big Data tool can help track these changes, and possibly revert to an earlier version if something goes wrong.

Big Data tools must allow easy integration

Simple integration allows Data Scientists and developers to focus on improving data analysis applications and models .

It also makes it easier to share results with other developers and Data Scientists. Thus, Big Data tools must allow a simple integration with the applications used by the company and with its Data Warehouse.

 

Big Data Tools Must Offer Data Management Features

 

To ensure continuity and standardization, Big Data tools must offer Data Management capabilities . Indeed, often the more the volume of data increases, the more the variety of data also increases.

Big Data tools must help Data Governance

A good Big Data tool must offer Data Governance features to enable businesses to comply with applicable laws and secure their data . It is necessary to be able to follow the source and the characteristics of the data sets used to develop an analytical model. It is also essential to be able to secure and manage data used by Data Scientists and engineers.

Big Data tools must compose data processing frameworks

Some Big Data tools focus on data processing, others on analysis. Some frameworks, like Apache Spark , support both. This versatility allows developers and Data Scientists to use a single tool throughout the process.

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