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| Course Fee | 1,875 |
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Total Fees |
1,875 (USD) |
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Koenig Learning Stack
Free Pre-requisite TrainingJoin a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.
Assessments (Qubits)Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.
Post Training ReportsReceive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.
Class RecordingsGet access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.
Free Lab ExtensionsExtend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.
Free Revision Classes Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
Forging Data Excellence: From Spark to Kafka equips professionals with the skills to manage, process, and analyze data efficiently, catering to a range of roles in the IT and data domains.
Course Introduction:
The "Forging Data Excellence: From Spark to Kafka" course equips participants with essential skills to leverage Apache Spark and Apache Kafka for efficient data processing and streaming, fostering a deep understanding of real-time data management.
Learning Objectives and Outcomes:
