This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static workflows, inefficient stateful computations, and lack of human intervention by leveraging LangGraph's graph-based workflow construction and dynamic decision-making capabilities. LangGraph allows large language models (LLMs) to dynamically determine control flows, invoke tools, and assess the necessity of further actions, improving flexibility and efficiency. The system architecture incorporates Apache Spark Streaming, Kafka, and LangGraph to create a high-performance sentiment analysis system. LangGraph's capabilities include precise state management, dynamic workflow construction, and robust memory checkpointing, enabling seamless multi-turn interactions and context retention. Human-in-the-loop mechanisms are integrated to refine sentiment analysis, particularly in ambiguous or high-stakes scenarios, ensuring greater reliability and contextual relevance. Key features such as real-time state streaming, debugging via LangGraph Studio, and efficient handling of large-scale data streams make this framework ideal for adaptive decision-making. Experimental results confirm the system's ability to classify inquiries, detect sentiment trends, and escalate complex issues for manual review, demonstrating a synergistic blend of LLM capabilities and human oversight. This work presents a scalable, adaptable, and reliable solution for real-time sentiment analysis and decision-making, advancing the use of Agent AI and LangGraph in big data applications.

Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Janani Ravi and the team and is specially produced in collaboration with Janani for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP funding scheme, terms and conditions apply. Note from the team ... This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data.

Description: This course prepares participants to begin running data analysis on databases. Both univariate and multivariate analysis are covered with a particular focus on regression analysis. Regression analysis is done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course is on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses. Description: This course covers the required fundamentals about big data technology that will help you confidently lead a big data project in your organization.

Learn how to use Spark, Pyspark AWS, Spark applications, Spark EcoSystem, Hadoop and Mastering PySpark.

Implement any project that requires PySpark knowledge from scratch. Know the theory and practical aspects of PySpark and AWS. People who are beginners and know absolutely nothing about PySpark and AWS. People who want to develop intelligent solutions. People who want to learn PySpark and AWS. People who love to learn the theoretical concepts first before implementing them using Python. People who want to learn PySpark along with its implementation in realistic projects.

As technology evolves at a rapid pace, the healthcare industry is transforming quickly along with it. Tech breakthroughs like IoT, advanced imaging, genomics mapping, artificial intelligence and machine learning are some of the key items re-shaping the space. The result is better patient care and health outcomes. To facilitate this shift to the next generation of healthcare services – and to deliver on the promise of improved patient care – organizations are adopting modern data technologies to support new use cases. We are a large company operating healthcare facilities across the US and employing over 20,000 people.

Let's add the stages in the Pipeline object and we will then perform these transformations in order.

Let's add the stages in the Pipeline object and we will then perform these transformations in order.