Manuscript Number : SHISRRJ247285

Real Time Data Analysis

Authors(2) :-K Naresh, K Ujwala

In today's dynamic business environment, Human Resources (HR) plays a pivotal role in organizational success. For HR professionals to lead strategic initiatives and make well-informed decisions, it is now essential to leverage data analytics. With the help of Tableau, a top business intelligence and data visualisation platform, this project will examine HR data and provide useful insights for better staff management. The project will begin with a comprehensive data collection process, data cleaning, aggregating diverse HR datasets such as employee demographics, gender, total count of employees, attrition rate and job satisfaction rating. Utilizing Tableau's robust capabilities, the analysis will focus on identifying patterns, trends, and correlations within the HR data, ultimately providing a holistic view of the workforce. The exploration of job satisfaction ratings, facilitated through interactive Tableau dashboards, delves into the factors influencing employee contentment. Concurrently, demographic insights, including education background and age group analyses, shed light on the diverse aspects within the organization, fostering inclusivity and informed decision-making. To enhance user experience and exploration, action filters have been strategically applied, enabling seamless cross-sheet interactivity. Tailored filters specific to gender, education background and age groups empower HR professionals to dynamically explore attrition rates, facilitating precise intern.

Authors and Affiliations

K Naresh
Assistant Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
K Ujwala
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India

Data Integration, Time-Series Analysis, Data Visualization, Data Synchronization, Continuous Analytics, Data Ingestion

  1. "Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data" by Byron Ellis, Nelson Corrente, and Michael Gorham.
  2. "Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing" by Tyler Akidau, Slava Chernyak, and Reuven Lax.
  3. "Storm: Distributed and fault-tolerant real time computation" by Nathan Marz and James Warren. This paper introduces the Storm framework for real-time data processing.
  4. "Apache Kafka: A Distributed Streaming Platform" by Neha Narkhede, Gwen Shapira, and Todd Palino. This paper provides an overview of Apache Kafka, a popular real-time messaging system.
  5. Coursera offers courses such as "Real-Time Analytics with Apache Storm" and "Big Data Analysis with Spark SQL".
  6. Udemy has courses like "Real-Time Data Analysis Using Kafka, Spark, and Cassandra" and "Streaming Big Data with Spark Streaming and Scala - Hands On!".
  7. Blogs from companies like Confluent, Databricks, and DataStax often provide insights into real-time data analysis techniques and best practices.
  8. Reports from research firms like Gartner and Forrester might offer insights into emerging trends and technologies in real-time data analytics.
  9. Official documentation for technologies such as Apache Kafka, Apache Spark Streaming, Apache Flink, and others often provide detailed guides and tutorials for real-time data analysis.
  10. Platforms like Stack Overflow, Reddit (e.g., r/data science, r/bigdata), and LinkedIn groups related to data science and big data often have discussions, Q&A sessions, and shared resources on real-time data analysis topics.

Publication Details

Published in : Volume 7 | Issue 2 | March-April 2024
Date of Publication : 2024-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 99-107
Manuscript Number : SHISRRJ247285
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

K Naresh, K Ujwala, "Real Time Data Analysis", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 2, pp.99-107, March-April.2024
URL : https://shisrrj.com/SHISRRJ247285

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