Apache Avro & Kafka Data Engineering Projects

EDUCBA via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

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Overview

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This Specialization provides hands-on, project-driven training in modern data engineering using Apache Avro, Apache Kafka, Apache Spark, and Apache Solr. Learners gain practical experience building serialization pipelines, real-time streaming systems, production-grade Kafka consumers, and enterprise search solutions through realistic industry use cases. By completing the program, learners develop job-ready skills for designing, implementing, and optimizing end-to-end data platforms used in large-scale analytics and streaming environments.

Syllabus

Course 1: Master Apache Avro: Build & Apply Serialization Pipelines
Course 2: Master Real-Time Streaming with Kafka & Spark
Course 3: Mastering Kafka Consumer Systems for Telecom Data
Course 4: Mastering Apache Solr: Index, Search & Analyze Content

Courses

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5 hours 18 minutes
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Learners will identify Avro’s role in data engineering, apply schema-based serialization techniques, construct Avro records, and implement complete serialization–deserialization pipelines using both command-line tools and generated code. This hands-on course provides a practical, project-driven introduction to Apache Avro, one of the most efficient and widely used data serialization systems in modern big data and distributed applications. Through structured modules, learners progress from foundational concepts—such as downloading Avro, defining namespaces, and working with GenericRecord structures—to advanced workflows involving DatumWriter, schema parsers, file readers, and type-safe code generation. By completing the course, learners gain the ability to confidently build, test, and troubleshoot real-world Avro pipelines used in analytics, data streaming, and microservices environments. What makes this course unique is its end-to-end, demonstration-rich approach, guiding learners from raw schema creation to full serialization and deserialization execution. With clear explanations, practical examples, and tool-based workflows, this course equips participants with job-ready Avro skills that can be immediately applied in professional data engineering projects.
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5 hours 18 minutes
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Learners will build, analyze, and implement real-time data pipelines; produce and consume streaming data; and apply aggregation logic to identify top trending songs using Apache Kafka and Apache Spark. This hands-on course equips learners with practical skills in streaming architecture, data ingestion, transformation logic, and end-to-end pipeline execution. Throughout the course, learners benefit from a structured, project-based approach that mirrors real industry workflows. They will set up a complete development environment, design a scalable project structure, implement streaming logic in Scala, and write processed data back to Kafka topics. By completing the project, learners gain confidence in working with real-time event data—one of the most in-demand capabilities in today’s data engineering roles. What makes this course unique is its clear focus on a real-world use case: computing top trending songs from continuous data streams. This contextual approach ensures that learners not only understand Kafka and Spark concepts but also apply them in a meaningful, production-style pipeline. Ideal for aspiring data engineers, developers, and technology professionals seeking practical, industry-relevant streaming expertise.
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8 hours 23 minutes
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Learners will index diverse content types, apply OCR processing, configure multilingual search, integrate Java applications, and implement advanced faceted search using Apache Solr. This course equips learners with the practical skills to build fast, scalable, and intelligent search solutions for real-world applications. Throughout the course, learners benefit from hands-on exposure to Solr’s full capabilities—from extracting text with Apache Tika and Tesseract to indexing PDFs, Word documents, images, and CSV files. They will also explore Solr’s internal architecture, understand how search queries are processed, and apply programmatic indexing and retrieval using Java. What makes this course unique is its end-to-end focus on real data formats, step-by-step practical demonstrations, and clear integration of external tools that modern search systems rely on. By the end, learners will be able to design, build, and optimize powerful search experiences that mirror professional, production-level implementations. This course is ideal for developers, analysts, and technical professionals seeking to elevate their expertise in enterprise search technologies.
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4 hours 38 minutes
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By the end of this course, learners will be able to build, configure, and optimize Apache Kafka producer and consumer systems using real telecom data. They will deploy Kafka infrastructure, implement Spring Boot–based consumers, manage offsets with manual commit strategies, and analyze Kafka’s polling mechanisms for reliable, real-time message processing. This hands-on course benefits anyone seeking practical, industry-ready Kafka skills. Using a telecom data scenario, learners gain firsthand experience with real-world streaming challenges—working through message production, Zookeeper coordination, console validation, and advanced consumer behavior. Instead of abstract theory, the course emphasizes applied learning through step-by-step demonstrations and functional code examples. What makes this course unique is its end-to-end, scenario-driven approach, guiding learners from foundational Kafka concepts to building production-level consumer pipelines. With clear walkthroughs, structured modules, and real-time debugging demonstrations, learners will confidently develop resilient Kafka applications suitable for large-scale data environments. Whether you are upskilling for a data engineering career or enhancing backend development expertise, this course equips you with the essential tools to operate Kafka in real-world systems.