Requisite Features of Big Data Analytics Tools

The sheer volume of Big Data that modern organizations have been generating and managing is clear indication that Big Data is not just a fad. To be able to survive this new wave of technology, organizations need to stay conditioned on the most relevant information, analyzing which would help to inform critical business decisions. Big data analytics fine-tunes this process to provide the best insights and knowledge available to analysts and decision makers in real time.

A complex entirety of processes including data scientists, business management, production teams and developers characterizes big data analytics, just a small fraction of which is designing a new data analytics model.

Big data analytics tools must have purposeful features, which would be vital to reduce the data scientists’ effort in improving business results. Some of them are:

1. Support embedded analytics

Big data analytics tools need to have APIs that are well-documented and use de facto standards, such as REST or JavaScript interfaces, along with sample code, videos, and a developer community active enough to provide assistance and share examples. The tools can support embedded analytics if they are designed as a series of micro-services or modular services with associated APIs that host applications can call to execute analytic functions. A lot of well-developed big data analytics tools are now offering APIs that help developers in the creation of a complete custom interface.

2. Provide integrated views of data

A big data analytics tool can be deemed good if it successfully melds fragmented data to project a holistic aspect of the enterprise. A federated query engine displays the benefit of providing fresh data integrated from several systems to customersin real time. The tool deals with the complexities of resolving the query, normally by referring statistics about each database’s activity and characteristics, and utilizing algorithms that optimize query execution plans.

3. Support high-velocity data

Businesses today are no more satisfied with snapshots of fast-moving data like big data as they desire to perform real-time analyses against all the data. Big data analytics tools can interface with streaming processing foundations, such as Spark Streaming and Apache Kafka, or they require a self-employed streaming engine that visualizes real-time streaming data to users as it flows through the system.

4. Support self-service

Organizations ought to provide analytics to a vast network of users, the range of which is still growing, to be able to profit from data. This includes every worker—decision makers who take in information and power users who analyze that data.

5. Query large volumes of data

The advent of Internet of things has caused a skyrocketing increase in the volume of data that business users want to query. In the world of big data, the data is too varied and volume of it is too much to move cost effectively. Big data analytics tools should be able to run directly against big data sources like Spark or Hadoop clusters, or operational systems running in the cloud or on-premises.

6. Optimize big data query performance

When optimizing query performance against large volumes of data, BI vendors have to make a choice between querying all the data directly that would cause slow query response time, or extracting and loading data into a local data store, delivering fast queries against an old data subset.

7. Underpin  all data sources

Some of the most common data management systems in the evolved data landscape are relational databases, analytic databases, Hadoop, Spark, NoSQL, cloud file systems, search engines, cloud applications and machine applications. A big data analytics tool should be able to query every one of these data sources and applications and visualize the data in real time for users to be able to identify trends and anomalies and act accordingly.

8. Support all data formats

Big data analytics tools should be able to interpret and parse non-relational data types like XML and JSON, and deal sparsely with non-symmetric tables and nested data structures. They should also be able to query the increasingly popular NoSQL systems with the use of analytic features, if available.

9. Manage data security

Big data analytics tools should be able to integrate with an organization’s existing authentication framework, and provide close control over the functions, features, and data accessible to users based on role, group and individual permissions. The tools should provide sufficient security within a multi-tenant cloud environment.

10. Support governed discovery

Big data analytics tools need to offer a single environment for both power and casual users with the environment tailored to the users’ individual requirements.