The Kafka Elastic program is a focused, 16–20 hour training designed to equip professionals with end-to-end knowledge of real-time data streaming using Apache Kafka and its seamless integration with Elasticsearch. Through a combination of instructor-led sessions, hands-on labs, and guided use cases, learners will gain practical experience in building scalable data pipelines, managing stream processing, and enabling powerful analytics and monitoring. The course starts with Kafka fundamentals, covering key concepts such as producers, consumers, brokers, topics, partitions, and message delivery guarantees. From there, learners explore real-time streaming workflows and how to architect them for high availability and fault tolerance. The second half of the course focuses on Elasticsearch integration—how to index Kafka data, create dashboards, monitor system performance, and support enterprise-grade analytics. Ideal for engineers, data teams, and IT professionals, this course empowers participants to drive real-time decision-making in their organizations.

Mastering Apache Ambari is a 16–24 hour specialized training program designed to help professionals manage, monitor, and maintain Hadoop clusters using Apache Ambari. This course focuses on simplifying Hadoop administration by leveraging Ambari’s centralized management, monitoring, and provisioning capabilities. Participants will learn how to install, configure, and operate Hadoop clusters, monitor cluster health, manage services, and troubleshoot issues using Ambari’s intuitive interface. Through hands-on exercises and real-world operational scenarios, learners gain the skills required to efficiently manage large-scale Hadoop environments.

The Performance Testing using JMeter course is an intensive 2–3 day hands-on training program designed to help QA professionals understand, design, and execute performance, load, and stress tests using Apache JMeter. Participants will learn how to build realistic test scenarios, configure test plans, analyze performance metrics, and interpret real-time results to identify bottlenecks. The course also covers integration of JMeter into CI/CD pipelines for automated performance validation. With practical labs and real-world case studies, this program equips learners to confidently evaluate system scalability, stability, and responsiveness in modern application environments.

There is a growing demand for PySpark professionals in the finance, healthcare, and technology industries. Learning PySpark can open up new career opportunities and increase earning potential for individuals with big data processing and analysis skills. Since PySpark is open-source, it is free to use, modify, and distribute. This has contributed to its popularity, as it has a large and active community of users and contributors constantly improving and enhancing its functionality. While at Florence, aspirants get to learn the latest of Pyspark in data cleaning, processing, analysis, and machine learning. It also supports multiple file formats, including CSV, JSON, and Parquet, making it flexible and adaptable to different data sources and workflows.