Real-Time, Real Fast: Kafka & Spark for Data Engineers

Modern organizations can’t wait until tomorrow to know what happened today: they need live visibility into orders per minute, anomaly rates, user activity, and so on. Real-time dashboards are no longer “nice to have”; they are essential for decision-making in e-commerce, finance, IoT, and operations. This course teaches you how to design and implement real-time dashboards powered by Apache Spark Structured Streaming. Through three hands-on modules, you’ll first master the streaming fundamentals for dashboarding: micro-batches, triggers, checkpoints, and schema enforcement. Next, you’ll integrate Spark with Kafka to process real-world event streams, apply event-time windows and watermarks to handle late or out-of-order data, and persist metrics into Delta Lake for reliable BI consumption. Finally, you’ll learn how to publish dashboards, configure refresh strategies, optimize performance with caches and materialized views, monitor pipeline health, and ensure recovery under failure. This course is ideal for data professionals, analysts, and engineers who want to build or operate real-time analytics systems. Whether you work in business intelligence, data engineering, or analytics, this course will help you turn streaming data into live, actionable dashboards. Learners should know basic Python and Spark DataFrames, and be familiar with SQL and JSON to follow the course smoothly. By the end, you won’t just know how to build a working dashboard; you’ll be able to operate one in production, keeping it accurate, fast, and trustworthy as data changes second by second.

Master Apache Kafka configuration, monitoring, and optimization for production environments. This hands-on course teaches you to design high-availability topic architectures, diagnose performance bottlenecks using consumer lag analysis, and tune producers and consumers for maximum throughput while meeting strict latency SLAs. Through real-world scenarios based on challenges faced by companies like Netflix, LinkedIn, Uber, and Walmart, you'll learn to prevent data loss during broker failures, eliminate consumer lag issues, and optimize Kafka clusters processing millions of events per second. By the end of this course, you'll have the skills to build, monitor, and optimize production Kafka infrastructure that handles massive scale while maintaining reliability and performance. This course is designed for software engineers, data platform specialists, and DevOps professionals who work with real-time data systems and want to deepen their expertise in Apache Kafka. Ideal learners already understand basic Kafka concepts and distributed systems fundamentals but seek to enhance their ability to configure, monitor, and optimize Kafka clusters for high-throughput, low-latency production environments. It’s also valuable for those preparing for roles in data engineering, site reliability, or systems performance optimization. Learners should have a basic understanding of distributed systems and networking concepts, familiarity with command-line interfaces, and introductory knowledge of Apache Kafka fundamentals such as topics, producers, and consumers. Prior experience with Linux environments, Docker, or monitoring tools like Grafana and Prometheus will be helpful but not required. By the end of this course, you’ll be able to configure and optimize Apache Kafka clusters for high throughput, low latency, and maximum availability. You’ll gain hands-on experience in monitoring broker health, diagnosing consumer lag, and tuning producer and consumer performance for real-world production environments. With these skills, you’ll be ready to build, scale, and maintain data streaming systems that power modern, high-performance applications.

Real-time data is everywhere — from fraud detection in financial transactions to personalized recommendations in e-commerce and anomaly detection in IoT devices. Traditional batch processing is too slow for these use cases, and businesses need insights the moment data is generated. This course teaches you how to design, build, and operate reliable streaming pipelines using Apache Spark Structured Streaming and Kafka. In this course, you’ll start with the fundamentals of Spark’s streaming model, learning how micro-batching, triggers, and checkpoints enable continuous processing. You’ll then connect Spark to real-world sources like Kafka, apply event-time processing with watermarks, and deliver results to Delta Lake. Finally, you’ll take pipelines to production by enriching streams with static data, monitoring query health, handling failures, and ensuring scalability. This course introduces you to real-time data processing using Apache Spark Streaming. You’ll learn how to handle continuous data flows, design fault-tolerant stream pipelines, and analyze live data efficiently. By the end, you’ll understand how Spark handles streaming workloads, integrates with various data sources, and powers decision-making in real-world applications. Learners should have a basic understanding of Python programming and Spark DataFrames, along with familiarity with JSON and SQL. By the end, you’ll have the skills to confidently implement streaming solutions that power real-time decision-making in modern data-driven organizations.

Imagine deploying schema changes with confidence—knowing your pipeline will handle them gracefully, consumers will stay healthy, and your data will stay consistent. That's the difference between hoping your CDC pipeline works and knowing it will. In this course you will learn how to build a working, vendor‑neutral CDC pipeline and a single, unified table from evolving source schemas. Starting with Debezium streaming changes from Postgres/MySQL into Kafka, you will use Schema Registry to enforce compatibility, then apply streaming SQL in Flink (or ksqlDB) to map, cast, and merge divergent fields into a canonical model. Finally, you will persist results to an Apache Iceberg table and query it instantly with Trino. Along the way, you’ll learn practical strategies to manage schema drift, choose compatibility modes (backward/full), and avoid breaking downstream consumers. Everything runs locally with Docker so you can reproduce it anywhere and take the same patterns to your cloud stack later. This course is designed for engineers working with Kafka, Debezium, and streaming SQL who need reliable schema evolution and canonical modeling skills. Learners should be familiar with Basic SQL, Docker, and familiarity with Kafka or streaming concepts. By the end of the course,you will be able to implement a small end‑to‑end CDC pipeline that streams from a source DB and unifies evolving schemas into a single queryable table.

Master the design and implementation of consistent streaming data pipelines using Apache Kafka, Spark, and Flink. In this hands-on course, you'll apply systematic decision frameworks to select appropriate delivery guarantees (at-most-once, at-least-once, exactly-once) based on business requirements and failure scenario analysis. You'll implement end-to-end exactly-once processing by configuring Kafka producer transactions, Spark Structured Streaming checkpoints, and Hudi transactional tables, then validate your implementation through integration testing with failure injection. Finally, you'll evaluate watermarking strategies by analyzing event arrival patterns to optimize the latency-completeness tradeoff and meet specific SLA requirements. Through realistic scenarios—from preventing duplicate billing in order processing to optimizing IoT event pipelines for sub-10-second P95 latency—you'll develop the skills to architect production streaming systems that balance correctness, performance, and operational simplicity. Intermediate data and platform engineers using Kafka, Spark, or Flink who want to design production streaming pipelines with correct delivery guarantees, exactly-once semantics, and low-latency processing. Foundational knowledge of distributed systems; basic experience with Apache Kafka or similar messaging systems; familiarity with SQL; and introductory experience with stream or batch data processing concepts. By the end of this course, you will be able to design and validate production-ready streaming pipelines with correct delivery guarantees, exactly-once semantics, and low-latency event-time processing.

Ship data and schema changes without outages. This hands-on course teaches you how to treat schemas as contracts, evolve them safely, and keep producers, consumers, and warehouses green end-to-end. You’ll design compatibility policies in a Schema Registry (backward/forward/full, transitive), automate checks in CI, and practice expand → adapt → contract rollouts. In streaming labs, you’ll capture OLTP changes with Debezium, deliver Avro-encoded events to Kafka, and route malformed records to a DLQ with actionable alerts. On the analytics side, you’ll evolve BigQuery/Iceberg schemas additively (NULLABLE/defaulted columns), shield downstream users with views/contracts, and validate correctness with queries and time travel. Realistic scenarios walk you through enum expansions, type widening, null/tombstone semantics, and subject naming rules. This course is for data engineers, backend engineers, and analytics engineers who work with real-time or streaming data systems and need to evolve schemas without downtime. It’s also useful for platform engineers and architects responsible for data contracts, CDC pipelines, or Kafka-based platforms. Learners should have basic SQL knowledge and a general understanding of streaming systems such as Kafka, along with familiarity with Git and the command line. Experience with schemas, CDC, Docker, or cloud data warehouses is helpful but not required. By the end, you’ll have runnable templates, governance checklists, and a portfolio-ready project that proves you can design zero-downtime change—confidently and repeatably. For more information, check out the document.

Building a data pipeline is easy. Building one that automatically recovers from failures, maintains data integrity during outages, and runs reliably in production—that's what separates junior engineers from platform architects. This course teaches you to design self-healing pipelines with automated recovery, fault tolerance, and disaster recovery built in from day one. You'll learn to build and schedule streaming workflows using modern orchestrators like Airflow and Prefect, implement reliability patterns including idempotence, checkpointing, and dead-letter queues for exactly-once-ish processing, and design multi-region recovery strategies that keep data flowing during regional failures. Through hands-on labs and real-world examples from Airbnb, LinkedIn, Netflix, and Uber, you'll master the orchestration and recovery techniques that turn fragile scripts into production-grade infrastructure. Learn to handle automated retries, run safe backfills, implement checkpoint-based recovery, and execute disaster recovery playbooks that restore pipelines after outages. Engineers who build or maintain real-time data pipelines and need stronger orchestration, reliability, and recovery skills. Basics of Python & SQL, Linux CLI, and Kafka fundamentals. Cloud account helpful but optional. By the end of the course, learners will be able to design, orchestrate, and recover real-time data pipelines that run reliably at production scale.

Master the design, implementation, and optimization of production-ready streaming data pipelines using Apache Kafka and Flink. This intermediate-level course teaches you to evaluate log configurations against governance requirements (PCI-DSS, GDPR, SOC2) and cost constraints, design stream processing topologies that join and aggregate data in real time with exactly-once semantics, and optimize pipelines through partition tuning, compression, and cost modeling. You'll work through hands-on labs that mirror real-world scenarios at DoorDash, Netflix, and Robinhood: comparing retention policies against compliance rules, building a Kafka Streams application that joins orders and payments to calculate 5-minute revenue totals, and diagnosing performance bottlenecks to meet SLAs within budget. Intermediate data engineers and platform engineers who build or operate real-time streaming systems and want to master Kafka/Flink governance, joins, windowing, and cost-optimized scaling. Understanding of distributed systems, basic Apache Kafka knowledge, familiarity with SQL and streaming concepts, Python or Java programming experience. By the end, you'll design and optimize a multi-tenant streaming platform with governance controls—skills directly applicable to streaming data engineer, real-time platform engineer, and data infrastructure roles.

In a world where business decisions happen in seconds, is your data fast enough? Traditional batch processing creates a critical "insight lag," forcing you to react to yesterday's news. This hands-on course empowers you to design, build, and optimize high-speed data pipelines that serve as the nervous system of modern business. Working in a ready-to-use cloud environment with industry-standard Apache Spark, you will master the complete lifecycle of real-time data engineering. Through practical, real-world case studies from e-commerce, IoT, and FinTech, you'll learn to build live operational dashboards, apply window functions to analyze trends over time, and design a sophisticated, real-time fraud detection engine. You will leave this course with the skills to transform massive, high-speed data streams into immediate, actionable business value and become the go-to expert for creating low-latency solutions that give companies their competitive edge. This course is designed for professionals and aspiring practitioners who want to harness the power of real-time analytics. Whether you are a data analyst, data engineer, or data scientist seeking to advance your skills, or an IT professional and developer working with IoT, cloud, or streaming systems, this course will equip you with the practical tools and techniques to analyze data as it flows. Business professionals will also benefit from understanding how real-time insights can accelerate smarter decision-making across industries. Learners should have a basic understanding of Python and SQL to follow the exercises effectively. The hands-on labs use a free Databricks account, and a setup guide is provided, so no prior experience with Databricks or Spark is required to get started. By the end of this course, learners will be able to design and implement efficient real-time data solutions using streaming technologies. They will learn to differentiate between batch, micro-batch, and continuous streaming patterns to solve business problems, apply time-based functions and watermarking for stateful data analysis, and optimize streaming pipelines by identifying and resolving performance challenges such as data skew.

“Design Real-Time Architectures with Apache Spark & Kafka” is an intermediate-level course crafted for learners aiming to build modern, scalable streaming systems. Across engaging, scenario-driven lessons, the course offers a comprehensive introduction to designing and implementing real-time data pipelines. Participants explore the foundations of streaming concepts, event-driven patterns, and the unique demands of low-latency processing. They gain practical experience working with Apache Kafka for event ingestion and Apache Spark Structured Streaming for real-time computation, learning to transform raw streams into actionable insights. The curriculum emphasizes reliable pipeline design, covering fault tolerance, checkpointing, and performance tuning to ensure systems can operate at scale. Through hands-on practice, guided dialogues, and real-world financial data scenarios, learners develop the confidence to architect, optimize, and deploy production-ready streaming solutions. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments. Learners should know basic Python or Scala, be comfortable with the command line, understand distributed systems at a high level, and have a simple introductory familiarity with Kafka and Spark. This course is ideal for aspiring data engineers, analysts or data scientists shifting into real-time systems, and software engineers exploring event-driven architecture. It also suits anyone working with large-scale data or financial and AI/ML pipelines who wants to understand how real-time data powers modern systems. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments.

In large-scale data engineering environments, performance issues such as slow transformations, excessive shuffle operations, and unbalanced workloads can impact analytics, reporting, and SLA commitments. This course teaches you how to analyze, diagnose, and optimize Apache Spark applications so they run faster, more efficiently, and more reliably. In this course, you’ll start by learning the fundamentals of Spark job execution, including how stages, tasks, shuffle operations, and execution plans reveal where bottlenecks occur. You’ll explore Spark’s built-in monitoring tools to interpret job behavior. From there, you’ll apply practical optimization techniques, including improving data partitioning, mitigating data skew, optimizing joins, configuring caching strategies, and choosing efficient file formats. You’ll also learn how to tune executors, memory, cores, and dynamic allocation to balance cost and performance across workloads. Learners should be familiar with basic knowledge of Python and Spark DataFrames; familiarity with JSON and SQL. This course is designed for data engineers and developers who need to diagnose and optimize Spark jobs running on large-scale distributed data pipelines. By the end, you’ll have the skills to confidently apply advanced tuning strategies, improve throughput, reduce shuffle overhead, and optimize resource usage.