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Operate and Monitor Kafka Clusters


Operate and Monitor Kafka Clusters
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 39m | Size: 311 MB​

Kafka failures are subtle until they break your pipeline. This course will teach you how to operate, monitor, scale, and troubleshoot KRaft-based Kafka clusters so they remain reliable, observable, and production-ready.

What you’ll learn
Kafka clusters degrade quietly. Lag rises, replicas fall behind, and storage pressure builds until performance collapses.
In this course, Operate and Monitor Kafka Clusters, you’ll gain the ability to operate KRaft-based Kafka clusters safely in production.
First, you’ll explore the core operational responsibilities needed for healthy brokers, partitions, and replicas.
Next, you’ll discover how to monitor Kafka’s health and performance metrics using JMX, CLI tools, and dashboards.
Finally, you’ll learn how to scale, tune, and troubleshoot Kafka clusters so they remain stable, observable, and ready for real workloads.
When you’re finished, you’ll have the skills and knowledge required to run reliable, observable, and scalable Kafka clusters using the modern KRaft architecture.

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OpenTelemetry in Practice – The Complete Observability Course


3.85 GB | 21min 4s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​
Files Included :
FileName :001 Welcome – Course Overview.mp4 | Size: (35.48 MB)
FileName :002 Aligning Expectations.mp4 | Size: (17.16 MB)
FileName :001 My Local Development Setup.mp4 | Size: (8.5 MB)
FileName :002 Windows Users – Installing WSL.mp4 | Size: (23.64 MB)
FileName :003 Windows Users – WSL Key Features.mp4 | Size: (23.95 MB)
FileName :004 Finding Your Way Around the Course Materials.mp4 | Size: (13.96 MB)
FileName :001 Section Overview.mp4 | Size: (5.81 MB)
FileName :002 Defining Observability.mp4 | Size: (15.51 MB)
FileName :003 Metrics, Logs, and Traces – the Three Pillars.mp4 | Size: (9.88 MB)
FileName :004 Understanding Metrics and Metric Types.mp4 | Size: (12.11 MB)
FileName :005 Working with Histograms and Percentiles.mp4 | Size: (24.51 MB)
FileName :006 Logs and the Case for Structured Logging.mp4 | Size: (16 MB)
FileName :007 How Distributed Tracing Works.mp4 | Size: (14.51 MB)
FileName :008 Combining Observability Signals Effectively.mp4 | Size: (15.88 MB)
FileName :009 Defining Service Level Indicators.mp4 | Size: (20.79 MB)
FileName :010 Setting Service Level Objectives.mp4 | Size: (6.34 MB)
FileName :011 Service Level Agreements Explained.mp4 | Size: (15.92 MB)
FileName :012 Managing Risk with Error Budgets.mp4 | Size: (13.1 MB)
FileName :013 Monitoring with the Four Golden Signals.mp4 | Size: (21.1 MB)
FileName :001 Section Overview.mp4 | Size: (3.32 MB)
FileName :002 Preparing the Development Environment.mp4 | Size: (26.76 MB)
FileName :003 Prometheus – a Quick Overview.mp4 | Size: (21.88 MB)
FileName :004 Lab – Setting Up Prometheus – Lab Overview.mp4 | Size: (16.88 MB)
FileName :005 Lab – Prometheus Configuration.mp4 | Size: (28.52 MB)
FileName :006 Lab – Launching and Validating Prometheus.mp4 | Size: (47.52 MB)
FileName :007 Loki – a Quick Overview.mp4 | Size: (17.19 MB)
FileName :008 Lab – Setting Up Loki – Lab Overview.mp4 | Size: (22.27 MB)
FileName :009 Lab – Loki Deployment.mp4 | Size: (37.25 MB)
FileName :010 Lab – Integrating Loki with Prometheus.mp4 | Size: (12.94 MB)
FileName :011 Lab – Validating Loki Log Ingestion.mp4 | Size: (49.76 MB)
FileName :012 Lab – Inspecting Loki Metrics.mp4 | Size: (25.55 MB)
FileName :013 Tempo – a Quick Overview.mp4 | Size: (17.26 MB)
FileName :014 Lab – Setting Up Tempo – Lab Overview.mp4 | Size: (16.18 MB)
FileName :015 Lab – Tempo Deployment.mp4 | Size: (30.49 MB)
FileName :016 Lab – Setting Up Grafana – Lab Overview.mp4 | Size: (13.29 MB)
FileName :017 Lab – Grafana Deployment and Integration.mp4 | Size: (36.65 MB)
FileName :018 Lab – Deploying the Sample Application – Lab Overview.mp4 | Size: (28.89 MB)
FileName :019 Lab – Sample Application Deployment.mp4 | Size: (47.48 MB)
FileName :020 Putting It All Together – the Full Stack Architecture.mp4 | Size: (11.42 MB)
FileName :001 Section Overview.mp4 | Size: (3.99 MB)
FileName :002 Introducing OpenTelemetry.mp4 | Size: (18.88 MB)
FileName :003 the OpenTelemetry API and SDK Separation.mp4 | Size: (34.34 MB)
FileName :004 Automatic vs Manual Instrumentation.mp4 | Size: (16.38 MB)
FileName :005 the Role of the OpenTelemetry Collector.mp4 | Size: (22.3 MB)
FileName :006 Standardizing Telemetry with Semantic Conventions.mp4 | Size: (24.79 MB)
FileName :001 Section Overview.mp4 | Size: (6.18 MB)
FileName :002 Lab – Local Development Environment Setup – Lab Overview.mp4 | Size: (51.14 MB)
FileName :003 Lab – Development Environment Configuration.mp4 | Size: (74.95 MB)
FileName :004 the Collector Pipeline Architecture.mp4 | Size: (22.99 MB)
FileName :005 Lab – Setting Up the OTel Collector – Lab Overview.mp4 | Size: (21.84 MB)
FileName :006 Lab – OTel Collector Deployment.mp4 | Size: (67.02 MB)
FileName :007 Lab – Testing the OTel Collector – Lab Overview.mp4 | Size: (7.76 MB)
FileName :008 Lab – Validating End-to-End Data Flow.mp4 | Size: (24.42 MB)
FileName :009 Lab – Keeping an Eye on the Collector.mp4 | Size: (44.93 MB)
FileName :001 How OpenTelemetry Handles Metrics.mp4 | Size: (16.08 MB)
FileName :002 Lab – Auto-instrumentation for Metrics – Lab Overview.mp4 | Size: (12.08 MB)
FileName :003 Lab – Enabling Auto-instrumentation for Metrics.mp4 | Size: (58.09 MB)
FileName :004 Lab – Manual Instrumentation for Metrics – Lab Overview.mp4 | Size: (17.2 MB)
FileName :005 Lab – Defining Custom Business Metrics.mp4 | Size: (95.03 MB)
FileName :006 Lab – Inspecting Custom Metrics in Prometheus.mp4 | Size: (40.27 MB)
FileName :001 How OpenTelemetry Handles Traces.mp4 | Size: (16.54 MB)
FileName :002 Lab – Auto-instrumentation for Traces – Lab Overview.mp4 | Size: (10.01 MB)
FileName :003 Lab – Enabling Auto-instrumentation for Traces.mp4 | Size: (49.35 MB)
FileName :004 Lab – Manual Instrumentation for Traces – Lab Overview.mp4 | Size: (15.55 MB)
FileName :005 Lab – Defining Custom Spans.mp4 | Size: (165.44 MB)
FileName :006 Lab – Debugging Instrumented Code.mp4 | Size: (41.6 MB)
FileName :007 Lab – Cleaning Up Instrumentation Code.mp4 | Size: (131.55 MB)
FileName :001 How OpenTelemetry Handles Logs.mp4 | Size: (18.41 MB)
FileName :002 Lab – Auto-instrumentation for Logs – Lab Overview.mp4 | Size: (18.54 MB)
FileName :003 Lab – Enabling Auto-instrumentation for Logs.mp4 | Size: (100.56 MB)
FileName :004 Lab – Validating Log Instrumentation.mp4 | Size: (15.43 MB)
FileName :005 Lab – Manual Instrumentation for Logs – Lab Overview.mp4 | Size: (14.24 MB)
FileName :006 Lab – Writing Manual Log Statements.mp4 | Size: (88.41 MB)
FileName :007 Lab – Log-Trace Correlations in Grafana – Lab Overview.mp4 | Size: (24.86 MB)
FileName :008 Lab – Enabling Log-Trace Correlations.mp4 | Size: (40.41 MB)
FileName :009 Lab – Externalizing Configuration – Lab Overview.mp4 | Size: (17.29 MB)
FileName :010 Lab – Externalizing and Refactoring Configuration.mp4 | Size: (67.92 MB)
FileName :001 Section Overview.mp4 | Size: (5.69 MB)
FileName :002 Lab – Worker Metrics Auto-instrumentation – Lab Overview.mp4 | Size: (15.89 MB)
FileName :003 Lab – Enabling Worker Metrics Auto-instrumentation.mp4 | Size: (73.12 MB)
FileName :004 Lab – Worker Logs Auto-instrumentation – Lab Overview.mp4 | Size: (23.48 MB)
FileName :005 Lab – Enabling Worker Logs Auto-instrumentation.mp4 | Size: (56.71 MB)
FileName :006 Lab – Worker Traces Auto-instrumentation – Lab Overview.mp4 | Size: (17.55 MB)
FileName :007 Lab – Enabling Worker Traces Auto-instrumentation.mp4 | Size: (33.53 MB)
FileName :001 Understanding Context Propagation.mp4 | Size: (23.12 MB)
FileName :002 Lab – Frontend-to-Worker Propagation – Lab Overview.mp4 | Size: (25.46 MB)
FileName :003 Lab – Adding Trace Context Injection to the Frontend.mp4 | Size: (33.49 MB)
FileName :004 Lab – Adding Trace Context Extraction to the Worker.mp4 | Size: (81.58 MB)
FileName :005 Lab – Validating Distributed Trace Connectivity.mp4 | Size: (37.37 MB)
FileName :006 Lab – Worker-to-Frontend Propagation – Lab Overview.mp4 | Size: (25 MB)
FileName :007 Lab – Finishing Bidirectional Propagation.mp4 | Size: (86.44 MB)
FileName :001 Lab – Worker Custom Metrics – Lab Overview.mp4 | Size: (17.74 MB)
FileName :002 Lab – Adding Custom Metrics to the Worker.mp4 | Size: (68.49 MB)
FileName :003 Lab – Validating Worker Custom Metrics.mp4 | Size: (116.69 MB)
FileName :004 Lab – Worker Custom Spans – Lab Overview.mp4 | Size: (14.96 MB)
FileName :005 Lab – Adding Custom Spans to the Worker.mp4 | Size: (33.84 MB)
FileName :006 Lab – Redis Exporter Pattern – Lab Overview.mp4 | Size: (17.66 MB)
FileName :007 Lab – Redis Exporter Deployment.mp4 | Size: (29.67 MB)
FileName :008 Lab – Environment-Driven Configuration – Lab Overview.mp4 | Size: (13.88 MB)
FileName :009 Lab – Switching to Environment-Driven Configuration.mp4 | Size: (26.7 MB)
FileName :010 Lab – Structured Logging for the Worker – Lab Overview.mp4 | Size: (21.4 MB)
FileName :011 Lab – Converting Worker Logs to Structured Format.mp4 | Size: (81.08 MB)
FileName :012 Lab – Validating Structured Log Output.mp4 | Size: (28.98 MB)
FileName :001 Section Overview.mp4 | Size: (5.66 MB)
FileName :002 Lab – Application Kubernetes Manifests – Lab Overview.mp4 | Size: (13.58 MB)
FileName :003 Lab – Writing the Application Manifests.mp4 | Size: (71.07 MB)
FileName :004 Lab – Observability Stack Manifests – Lab Overview.mp4 | Size: (32.24 MB)
FileName :005 Lab – Writing the OTel Collector Manifest.mp4 | Size: (40.62 MB)
FileName :006 Lab – Writing the Observability Backend Manifests.mp4 | Size: (76.72 MB)
FileName :007 Lab – Writing the Kube State Metrics Manifest.mp4 | Size: (33.21 MB)
FileName :008 Lab – AI-Assisted Manifest Audit.mp4 | Size: (25.73 MB)
FileName :009 Lab – Setting Up Kustomize – Lab Overview.mp4 | Size: (16.68 MB)
FileName :010 Lab – Structuring Configuration with Kustomize.mp4 | Size: (54.12 MB)
FileName :011 Lab – Verifying Kubernetes Manifests – Lab Overview.mp4 | Size: (12.77 MB)
FileName :012 Lab – Checking the Generated Manifests.mp4 | Size: (54.89 MB)
FileName :013 Lab – Deploying to Kubernetes – Lab Overview.mp4 | Size: (18.54 MB)
FileName :014 Lab – Rolling Out Manifests to the Cluster.mp4 | Size: (57.32 MB)
FileName :015 Lab – Troubleshooting Telemetry Collection.mp4 | Size: (33.4 MB)
FileName :016 Lab – Verifying Telemetry Data – Lab Overview.mp4 | Size: (13.4 MB)
FileName :017 Lab – End-to-End Telemetry Verification.mp4 | Size: (96.9 MB)
FileName :jest config.ts | Size: (632 B)
FileName :index.ts | Size: (4.21 KB)
FileName :instrumentation.ts | Size: (1.19 KB)
FileName :logger.ts | Size: (246 B)
FileName :metrics.ts | Size: (730 B)
FileName :translation.ts | Size: (8.1 KB)
FileName :queue.ts | Size: (5.47 KB)
FileName :sse.ts | Size: (2.85 KB)
FileName :types.ts | Size: (1.2 KB)
FileName :ioredis.ts | Size: (1.88 KB)
FileName :uuid.ts | Size: (80 B)
FileName :translation test.ts | Size: (6.3 KB)
FileName :sse test.ts | Size: (4.37 KB)
FileName :jest config.ts | Size: (632 B)
FileName :index.ts | Size: (5.48 KB)
FileName :instrumentation.ts | Size: (1.59 KB)
FileName :logger.ts | Size: (246 B)
FileName :metrics.ts | Size: (730 B)
FileName :translation.ts | Size: (10.69 KB)
FileName :queue.ts | Size: (7.03 KB)
FileName :sse.ts | Size: (3.42 KB)
FileName :tracers.ts | Size: (421 B)
FileName :types.ts | Size: (1.2 KB)
FileName :ioredis.ts | Size: (1.88 KB)
FileName :uuid.ts | Size: (80 B)
FileName :translation test.ts | Size: (6.3 KB)
FileName :sse test.ts | Size: (4.37 KB)
FileName :jest config.ts | Size: (632 B)
FileName :index.ts | Size: (6.46 KB)
FileName :instrumentation.ts | Size: (1.59 KB)
FileName :logger.ts | Size: (286 B)
FileName :metrics.ts | Size: (730 B)
FileName :translation.ts | Size: (10.69 KB)
FileName :queue.ts | Size: (7.24 KB)
FileName :sse.ts | Size: (3.42 KB)
FileName :tracers.ts | Size: (421 B)
FileName :types.ts | Size: (1.28 KB)
FileName :ioredis.ts | Size: (1.88 KB)
FileName :uuid.ts | Size: (80 B)
FileName :translation test.ts | Size: (6.3 KB)
FileName :sse test.ts | Size: (4.37 KB)]
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OpenTelemetry Foundations: Hands-On Guide to Observability


OpenTelemetry Foundations: Hands-On Guide to Observability
2025-02-17
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 2.25 GB | Duration: 5h 6m​

Learn Observability, OpenTelemetry Instrumentation, Telemetry Signals, Context Propagation, Collector and more!

What you’ll learn
Observability: Signals, Monitoring Vs Observability, Reliability Metrics & Solutions
Hands-On OpenTelemetry Demo
OpenTelemetry Instrumentation: APIs & SDKs, Zero-code & Code-based instrumentation
Hands-on OpenTelemetry Instrumentation
Telemetry Signals: Metrics, Traces, Logs & Baggage
OpenTelemetry Tools: OTLP & SDK Exporter
OpenTelemetry Collector: Installation & Configuration
Observability Data Analysis
Observability adoption within your organisation

Requirements
Basic technology & application development understanding
Programming knowledge not required but advantageous

Description
Unlock the power of observability with this comprehensive OpenTelemetry Foundations course! Designed for both developers and operations teams with little to no experience in observability and OpenTelemetry, this course provides a solid foundation to help you understand and implement OpenTelemetry in your organization.What You’ll Learn:Introduction to Observability: Understand the importance of observability in modern software systems and how it can help you gain insights into your applications.Core Principles of OpenTelemetry: Learn the fundamental concepts and principles that drive OpenTelemetry, the leading open-source observability framework.Key Components and Architecture: Dive into the architecture of OpenTelemetry, exploring its key components and how they work together to provide comprehensive observability.Practical Use Cases and Examples: See OpenTelemetry in action with examples and use cases that demonstrate its capabilities and benefits.How to Get Started with OpenTelemetry in Your Organization: Get practical tips and guidance on how to implement OpenTelemetry in your own projects and start reaping the benefits of enhanced observability.Who Should Enroll: This course is ideal for developers and operations teams who are looking to master Observability and OpenTelemetry . Whether you’re looking to enhance your skills or implement observability in your organization, this course will provide you with the knowledge and tools you need to succeed.Enroll Today!Join us on this journey to mastering OpenTelemetry and take the first step towards achieving comprehensive observability in your software systems. Enroll today and start transforming the way you monitor and manage your applications!

Who this course is for:
Everyone interested in Observability, Beginners looking to learn OpenTelemetry, Experienced techs looking to level up in Observability & OpenTelemetry, Managers looking to understand Observability & OpenTelemetry

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OpenShift Fundamentals and Workload Deployment


OpenShift Fundamentals and Workload Deployment
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 1h 29m | 201 MB
Instructor: Marcel Rummens​

Learn the fundamentals of OpenShift by installing a local cluster, operating core Kubernetes resources, deploying containerized applications, and troubleshooting basic connectivity issues to confidently run workloads on the OpenShift platform.

What you’ll learn

Running containerized workloads in production requires more than basic Kubernetes knowledge. You need to understand how enterprise platforms like OpenShift extend Kubernetes and streamline cluster operations. In this course, OpenShift Fundamentals and Workload Deployment, you’ll gain the ability to confidently deploy, manage, and troubleshoot applications in an OpenShift environment.

First, you’ll explore how OpenShift builds on Kubernetes and set up a local cluster for hands-on practice. Next, you’ll discover how to work with core resources using both the web console and the oc CLI. Finally, you’ll learn how to deploy and expose containerized applications, scale and update them, and resolve common connectivity issues.

When you’re finished with this course, you’ll have the skills and knowledge of OpenShift fundamentals needed to operate a cluster, manage core workloads, and troubleshoot basic application problems in real-world environments.

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OpenNebula Installation and Infrastructure Management


OpenNebula Installation and Infrastructure Management
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 54m | Size: 257 MB​

Build your own private cloud with confidence. This course teaches you how to install, configure, and manage an OpenNebula environment to streamline virtualization and cloud operations.

What you’ll learn
Building hands-on skills in virtualization, storage, and networking is essential to managing data center resources in your cloud environment. In this course, OpenNebula Installation and Infrastructure Management, you’ll learn how to install, configure, and manage an OpenNebula cloud environment. First, you’ll explore how to set up the OpenNebula front-end and configure authentication. Next, you’ll manage compute hosts, create virtual machines, and handle storage with datastores and images. Finally, you’ll learn how to configure virtual networks, address ranges, and security groups. When you’re finished with this course, you’ll have the skills and knowledge to deploy and manage a complete OpenNebula infrastructure for private cloud environments.


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OpenNebula Hybrid and Multi-cloud Integration


OpenNebula Hybrid and Multi-cloud Integration
Duration: 1h 14m | .MP4 1280×720 30 fps(r) | AAC, 48000 Hz, 2ch | 182.48 MB
Genre: eLearning | Language: English​

Hybrid and multi-cloud environments can be difficult to manage at scale. This course will teach you how to unify infrastructure, automate deployments, and build a flexible cloud strategy using OpenNebula.

More Info:
OpenNebula Hybrid and Multi-cloud Integration

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OpenAI ChatGPT Practical Applications


OpenAI ChatGPT Practical Applications
Released 2/2026
Duration: | .MP4 1280×720 30 fps(r) | AAC, 48000 Hz, 2ch | 234.57 MB
Genre: eLearning | Language: English​

Stop getting generic ChatGPT responses. This course will teach you a repeatable prompt structure and an iterative workflow to turn vague ideas into clear, usable deliverables.

More Info:
OpenAI ChatGPT Practical Applications


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OpenAI ChatGPT Advanced Features


OpenAI ChatGPT Advanced Features
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 42m | 120 MB
Instructor: Renganathan Padmanabhan​

This course will teach you how to perform data analysis using ChatGPT’s advanced features, including how to analyze multiple content and data formats and visualizing the data insights, thus simplifying your daily tasks significantly.

What you’ll learn

Exploring new data sets, cleaning and analyzing diverse content formats, and making sense of data are often the most time-consuming parts of a data analyst’s role. In this course, OpenAI ChatGPT Advanced Features, you’ll gain the ability to streamline that process using ChatGPT.

First, you’ll explore how to discover and access different data sets through ChatGPT’s web browsing capabilities. Next, you’ll learn how to use ChatGPT’s deep research feature to retrieve in-depth analysis and surface the data sources behind the insights. Finally, you’ll dive into various content and data formats to categorize, summarize, and prepare data insights for visualization and documentation.

When you’re finished with this course, you’ll have the skills and knowledge needed to accelerate your daily data analysis tasks using ChatGPT’s advanced features.

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OpenAI ChatGPT Advanced Features (2026)


Free Download OpenAI ChatGPT Advanced Features (2026)
Released 4/2026
By Renganathan Padmanabhan
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 43m | Size: 108 MB

This course will teach you how to perform data analysis using ChatGPT’s advanced features, including how to analyze multiple content and data formats and visualize the data insights, thus simplifying your daily tasks significantly.
What you’ll learn
The most time consuming parts of a data analyst’s role are exploring new data sets, cleaning and analyzing different content and data formats, and then making sense of the data.
In this course, OpenAI Chat GPT Advanced Features, you will learn how to explore and discover different data sets through ChatGPT’s web browsing capabilities.
First, you will explore how to leverage ChatGPT’s deep research feature to retrieve in-depth analysis of the topics you are trying to understand with the required data sources.
Next, you will dive deeper into different content and data formats to categorize and summarize insights within data sets.
Finally, you will prepare these data insights to be visualized and documented.
When you are finished with this course, you will have the skills and knowledge of ChatGPT’s advanced features needed to make your everyday data analysis easier to execute for ongoing efforts.
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https://www.pluralsight.com/courses/advanced-features-openai-chatgpt


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Open Table Formats: Core Concepts


Open Table Formats: Core Concepts
Released 2/2026
By Nikola Ilic
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 59m | Size: 123 MB​

Open table formats are essential for modern data platforms. This course will teach you the core concepts behind Delta Lake, Iceberg, and Hudi that transform data lakes into reliable lakehouses.

What you’ll learn
Traditional data lakes built on simple file formats like Parquet face critical limitations around reliability, consistency, and manageability. Teams struggle with data corruption, failed updates, schema drift, and the inability to time travel or audit changes. Open table formats emerged to solve these fundamental problems, but understanding what they are and which one to choose can be overwhelming.
In this course, Open Table Formats: Core Concepts, you’ll gain foundational knowledge of how table formats work and why they’ve become essential in modern data architectures.
First, you’ll explore what open table formats are and how they differ from traditional Hive-style tables and Parquet-only approaches, understanding the limitations they were designed to overcome. Next, you’ll discover the core problems they solve, such as ACID transactions, schema evolution, time travel, data versioning, and reliability guarantees, through concrete examples and visual comparisons. Finally, you’ll learn the high-level characteristics that differentiate the three major formats: Iceberg’s snapshot-based catalog-centric model, Delta Lake’s transaction log simplicity, and Hudi’s incremental processing focus with Copy-on-Write and Merge-on-Read table types.
When you’re finished with this course, you’ll have a clear conceptual foundation in open table formats, enabling you to understand architectural discussions, evaluate format choices, and confidently proceed to implementation and design courses in the Open Table Formats learning path.