Building Smarter Data Pipelines: SQL, Spark, Kafka & GenAI

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Master the complete data engineering pipeline from ingestion to analytics. Learn to build scalable data systems using Apache Kafka, Spark, and cloud platforms while integrating cutting-edge generative AI technologies. Apply your skills through hands-on projects that mirror real-world data engineering challenges in modern enterprises.

Syllabus

Course 1: Data Engineering: Pipelines, ETL, Hadoop
Course 2: Engineering Data Ecosystems: Pipelines, ETL, Spark
Course 3: Data Warehousing: Schema, ETL, Optimal Performance
Course 4: Microsoft SQL Server: Performance Tuning Essentials
Course 5: Cloud Architecture Design Patterns
Course 6: GenAI for Data Engineers: Scaling with GenAI
Course 7: Apache Kafka - An Introduction
Course 8: Smart Data Cleaning with Generative AI

Courses

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2 hours 35 minutes
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As part of the GenAI Academy, this course explores how Generative Artificial Intelligence (GenAI) is transforming the field of data engineering. This course serves as a primer where learners will discover the key capabilities of GenAI and uncover practical strategies to leverage these powerful tools in their day-to-day data engineering work. This course is designed for data engineering team leaders and data engineers, including managers and team leads who are responsible for guiding their teams towards innovative practices, as well as data engineers and aspiring professionals looking to enhance their workflows and future-proof their skillsets by incorporating GenAI-powered tools. Learners should have a basic understanding of data pipelines, ETL/ELT processes, and data transformation, along with familiarity with databases, data warehouses, big data frameworks, and programming languages like Python and SQL. An open mindset and curiosity to explore new GenAI technologies are essential. By the end of this course, data engineers will be equipped with the knowledge and skills to start scaling their productivity by harnessing the transformative potential of GenAI.
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2 hours 31 minutes
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Apache Kafka is a powerful, open-source stream processing platform that enables businesses to process and analyze data in real-time. This course introduces the core concepts and architecture of Apache Kafka, guiding learners. This course is designed for aspiring data engineers, software developers interested in data processing, and IT professionals looking to diversify into data engineering. It also targets data scientists seeking to understand real-time analytics platforms and technical managers overseeing data-driven projects. Learners are expected to have a basic understanding of programming concepts and familiarity with the command line. No prior experience with Apache Kafka is required, but having a general interest in messaging systems and real-time data processing will be beneficial. After completing this course, learners will be able to describe Apache Kafka's architecture and its components, enhancing data pipeline efficiency. They will also be able to configure and manage Kafka clusters, ensuring high availability and fault tolerance. Additionally, learners will be equipped to create and use topics, publishers, and subscribers to facilitate real-time data exchange, as well as implement basic stream processing applications using Kafka Streams to address real-world data challenges.
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2 hours 43 minutes
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"Cloud Architecture Design Patterns" is a comprehensive course designed to introduce learners to the essential principles and patterns in cloud architecture. This course blends theoretical lessons with practical examples to equip participants with the skills necessary to design robust, scalable, and efficient cloud systems. Through learning everything from basic concepts to advanced patterns, the course ensures a well-rounded education in cloud architecture design. Reflect on a moment early in my career, when a project suffered due to our team's inadequate understanding of cloud scalability. This challenging experience, involving system downtimes during critical peak loads, highlighted the critical need for robust architecture an eye-opening episode that showcased the direct impact of architecture on performance and cost-efficiency. This course is designed for aspiring cloud architects, software developers, IT professionals, and anyone interested in cloud technologies. It is ideal for those looking to deepen their understanding of cloud architecture design patterns and enhance their skills in creating robust, scalable, and efficient cloud systems. Whether you are new to cloud architecture or seeking to refine your expertise, this course provides valuable insights and practical knowledge applicable to various roles in the tech industry. Participants should have a basic understanding of cloud computing concepts, including knowledge of service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid). Familiarity with software design principles, such as object-oriented programming, design patterns, and a basic understanding of RESTful services, is also recommended. This foundational knowledge will help learners fully engage with the course material and apply the concepts effectively in real-world scenarios. By the end of this course, learners will have a solid foundation in cloud design patterns, enabling them to effectively architect solutions that leverage cloud technologies. Whether you are new to cloud architecture or seeking to enhance your existing skills, this course provides the knowledge and tools necessary to succeed in the rapidly evolving field of cloud computing.
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2 hours 36 minutes
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Data warehousing is a critical component of modern business intelligence, providing a centralized repository for structured and organized data. This course focuses on the fundamental aspects of data warehousing, including schema design, extract, transform, load (ETL) processes, and techniques for optimizing performance. By comprehending these core concepts, participants will be equipped to design and implement efficient data warehouses that support informed decision-making and business intelligence initiatives. This course is tailored for Data Engineers, Database Administrators, Business Intelligence Developers, and Data Analysts who are looking to deepen their understanding of data warehousing. These professionals play a crucial role in managing and analyzing vast amounts of data, ensuring that organizations can leverage this data for strategic decision-making. By enhancing their skills in data warehousing, participants will be better equipped to contribute to their organizations' business intelligence efforts and improve overall data management practices. To gain the most from this course, participants should have a basic knowledge of databases and SQL. Familiarity with these foundational concepts is essential as it will allow learners to effectively grasp the more advanced topics covered in the course. This background knowledge will enable participants to engage more deeply with the material, understand the practical applications, and apply the techniques discussed to real-world scenarios. Upon completing this course, learners will be able to explain the importance of data warehousing in business intelligence, highlighting how it supports decision-making processes. They will also gain the skills to design and implement effective schema designs for data warehouses, ensuring data is organized and accessible. Additionally, participants will learn to implement ETL processes to efficiently load and transform data into a data warehouse and apply performance optimization techniques to enhance the efficiency and responsiveness of data warehouse systems.
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3 hours 25 minutes
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This course provides a comprehensive guide to mastering data engineering, where you'll learn to build robust data pipelines, delve into ETL (Extract, Transform, Load) processes, and handle large datasets using Hadoop. You will gain expertise in extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or big data platforms. With hands-on experience in Hadoop, the industry-standard framework for handling massive datasets, you’ll learn to manage and process massive datasets efficiently. Whether you're a beginner or an experienced professional, this course equips you with the skills to design, implement, and manage data pipelines, making you a valuable asset in any data-focused organization. This course is ideal for aspiring data engineers, software developers interested in data processing, and IT professionals looking to expand their expertise into data engineering. It is also suitable for business analysts and other professionals who seek a foundational understanding of data handling technologies to improve decision-making capabilities and enhance their roles in data-driven environments. Whether you are just starting your journey in data engineering or looking to strengthen your existing skills, this course will provide the knowledge and tools you need to succeed. To get the most out of this course, you should have a basic understanding of programming concepts and some familiarity with database systems. A foundational knowledge of Python programming and SQL will be helpful, as will an understanding of relational database systems. No prior experience with Hadoop is required, but a keen interest in big data and data analytics will greatly enhance your learning experience. By the end of this course, you will be able to analyze the architecture and components of data pipelines and understand their impact on data flow and processing efficiency. You will learn how to implement robust ETL processes that are scalable and maintainable, and you will be equipped to handle big data challenges using Hadoop’s ecosystem tools, such as HDFS, MapReduce, Hive, Pig, and Spark. This course will prepare you to design, implement, and manage data solutions that can drive meaningful insights and support strategic decision-making in any organization.
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2 hours 41 minutes
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This course is designed to provide you with a foundational understanding of how modern data ecosystems work. From data pipelines to ETL processes, and big data handling using Apache Spark, you’ll explore the essential tools, techniques, and technologies that drive decision-making in today’s data-driven world. Whether you’re an aspiring data engineer or someone interested in the mechanics of data handling, this course will lay the groundwork for your journey into the exciting field of data engineering. This course is ideal for aspiring data engineers, software developers, database administrators, and IT professionals looking to expand their skills in data handling and processing. Additionally, analysts and business professionals interested in data technologies will find the course beneficial for enhancing their understanding of the fundamental processes behind data ecosystems and big data. Participants should have a general interest in data and a basic understanding of programming concepts. Familiarity with database systems will be helpful, but prior experience with Spark is not required. An interest in big data and data analytics will enrich your learning experience throughout the course. By the end of this course, participants will be able to identify the components and importance of data ecosystems, understand the structure and function of data pipelines, and recognize the critical steps involved in ETL workflows. Additionally, you'll gain introductory knowledge of big data handling with Apache Spark and its applications in large-scale data processing.
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3 hours 14 minutes
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Do you want your applications to run smoothly and efficiently, with lightning-fast database responses and minimal downtime? Well! You are in the right place to achieve this. Welcome to our comprehensive course on optimizing SQL Server performance, where you will discover the techniques to maintain efficiency and ensure smooth back-end operation for your applications. You will also learn to maximize database performance techniques and strategies to enhance query tuning, indexing strategies, and overall database optimization. This course is perfect for Database Administrators, IT Professionals, Data Analysts, and Technical Managers involved in SQL Server management and performance optimization. If you're responsible for ensuring the efficiency of database operations and looking to enhance your SQL Server performance skills, this course will provide you with essential tools and techniques. Having a basic understanding of SQL query language, SQL Server, and database management concepts is beneficial. This foundational knowledge will help you better understand the performance optimization strategies covered throughout the course. By the end of this course, you will be able to analyze and tune SQL queries, evaluate database indexing strategies, monitor SQL Server performance, troubleshoot common issues, and apply best practices for consistent and reliable database operations. These skills will enable you to enhance database efficiency, minimize downtime, and optimize system performance effectively.
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6 hours 30 minutes
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Tired of spending hours on tedious data cleaning? Imagine if AI could handle the heavy lifting for you, turning days of work into minutes. From detecting errors to organizing vast datasets, Generative AI can not only save you time but also elevate your data quality to new heights. Dive into this course to learn how to transform data prep from a chore into a game-changing advantage! This short course was created to help you leverage Generative AI to simplify data cleaning and preparation, making workflows faster, more efficient, and accurate. By completing this course, you’ll gain hands-on knowledge of Generative AI techniques that can be immediately applied to improve your data preparation workflows. By the end of this 2.5-hour long course, you will be able to: - Identify common challenges in data cleaning and preparation that can be automated with Generative AI. - Apply Generative AI tools and techniques to automate repetitive data cleaning tasks, streamlining the data preparation process. - Evaluate the effectiveness of Generative AI in improving the efficiency and accuracy of data cleaning and preparation processes. - Implement specific Generative AI strategies in data cleaning workflows to minimize manual effort. This course is unique because it combines a practical, hands-on approach with real-world case studies, enabling you to directly apply AI tools and techniques to relevant data challenges. You’ll not only explore cutting-edge AI tools but also gain valuable insights into optimizing your data preparation processes through automated solutions. To be successful in this course, you should have a background in data handling and basic AI concepts. Experience with programming in Python and data preparation will be helpful to get the most out of the exercises. Throughout the course, you'll be assessed through a combination of practice quizzes, hands-on exercises, and a final graded assessment. These assessments are designed to ensure you've truly mastered the material and can apply your newfound knowledge to real-world scenarios. To succeed, stay engaged with the hands-on exercises, actively explore the Generative AI tools introduced, and take time to analyze the case studies provided. These activities will not only deepen your understanding but also equip you with actionable skills for immediate use in your data projects.