Big Data analytics involves the cleaning, transformation and modeling of data, for the discovery of information useful for making decisions.
The information obtained from the big data analytics tools includes correlations, hidden patterns, customer preferences, and market trends. A variety of sophisticated applications with elements such as statistical algorithms, predictive models, etc. are often used. Below are some of the data analysis technologies you should know.
Kafka
Kafka is a Distributed Streaming platform with Key Capabilities, which are consumer, publisher, and subscriber related. Kafka is open-source software that provides a pooled, low-latency, high-throughput platform for managing all data fed in real-time. The other benefit of this platform is its ability to scale horizontally. The only weakness with Kafka is the absence of good monitoring solutions.
Splunk
Splunk is software that allows you to uncover the hidden value of data. It indexes and correlates real-time data via a searchable repository. It is from the repository that it creates reports, graphs, dashboards, alerts, and visualizations. Splunk can also be used for managing apps, improving security, and for business & web analytics. The only downside of Splunk is that it can be difficult to learn for new users.
KNIME
KNIME enables users to form visual data flows, implement some of the steps they created, and view results. This enhances a better understanding of data, data science workflows & recyclable components. The perks of using KNIME include its ability to connect to various data sources, provision of control over what happens with data at every stage, etc. KNIME also has many functionalities that can be reused. These functionalities are components verified by KNIME experts. Users can reuse them as their personalized KNIME nodes for tasks that often repeat.
The downside of KNIME is that simple tasks can take a long time, and there are usually problems with data imports & merging files.
