Ansible, the easy-to-use IT automation engine, can transform and optimize tasks such as rolling updates with just a few lines of code. In this course, explore intermediate to advanced use cases for using this powerful tool at scale. Instructor Anthony Sequeira shares instructions and demonstrations designed to help you master the details of Ansible playbook construction and execution. Learn how to take full advantage of Ansible by building thoughtfully designed tasks. Explore how to reuse Ansible functionality with roles, as well as comb through Ansible Galaxy to leverage content from the Ansible community. Plus, learn how to encrypt secret information, use Ansible for the management of network devices, and leverage the power of idempotence.

Ansible is an easy-to-use IT automation engine. It can transform and optimize tasks such as rolling updates with just a few lines of code. However, many organizations aren't using Ansible modules and playbooks in a reusable fashion; they are reinventing the wheel every time they automate. This training course introduces intermediate to advanced use cases for using the tool at scale. Instructor Robert Starmer shows how to reuse Ansible functionality with roles, encrypt secret data with Ansible Vault, support idempotence, and manage multiple groups of systems with Ansible Tower. He shows you how to string these concepts together to use Ansible in the most efficient way possible: variablizing resources, capturing your ideas into roles, and extending Ansible from simple server management to network management-and beyond.

Join concept artist Anthony Jones as he shares his latest work, where he finds inspiration, and how he sees the big trends in 3D and VR developing over the next few years.

Develop cross-platform apps using standard web technologies (HTML, CSS, and JavaScript) and Apache Cordova.

Explore how to build batch mode data pipelines with Apache Flink, the powerful and popular stream-processing platform.

Learn how to use Apache Flink relational APIs—the Table API and SQL—for batch and real-time exploratory data analytics.

Discover how to build a real-time stream processing pipeline with Apache Fink. Learn about the platform's windowing, event-time processing, and state management features.

Scalable and distributed message queuing plays an important role in building real time big data pipelines. Asynchronous publisher/subscriber models are required to handle unpredictable loads in these pipelines. Apache Kafka is the leading technology today that provides these capabilities and is an essential skill for a big data professional. In this course, Kumaran Ponnambalam provides insights into the scalability and manageability aspects of Kafka and demonstrates how to build asynchronous applications with Kafka and Java. Kumaran starts by demonstrating how to set up a Kafka cluster and explores the basics of Java programming in Kafka. He then takes a deep dive into the various messaging and schema options available. Kumaran also goes over some best practices for designing Kafka applications before finishing with a use case project that applies the lessons covered in the course.

One of the key components of a big data processing pipeline is a scalable and distributed message queue. Message queues enable real-time streaming capabilities with multiple producers and consumers of data. This enables real-time applications that can analyze data and produce insights in a scalable fashion. Apache Kafka provides these capabilities. As the de facto standard for open-source messaging, Apache Kafka is an essential skill for data scientists, big data engineers, data architects, and solution architects. In this course, instructor Kumaran Ponnambalam introduces Apache Kafka and explains its fundamental concepts and basic operations. Kumaran covers basic concepts like messages, topics, logs, and more. He shows you how to use the Kafka command line, as well as partitions and groups. He goes over Kafka Java programming, then concludes with a use case project.

Get up and running with Apache Spark quickly. This practical hands-on course shows Python users how to work with Apache PySpark to leverage the power of Spark for data science.

Learn how to do deep learning with images on Apache Spark, using the Databricks deep learning library and Python.

Get up to speed with Spark, and discover how to leverage this powerful platform to efficiently and effectively work with big data.

Discover how to make Apache Spark work with other big data technologies to build data pipelines for data engineering and DevOps.

Data engineering is the foundation for building analytics and data science applications in the new Big Data world. Data engineering requires combining multiple big data technologies to construct data pipelines and networks to stream, process, and store data. This course focuses on building full-fledged solutions that combine Apache Spark with other Big Data tools to create end-to-end data pipelines. Instructor Kumaran Ponnambalam begins by defining data engineering, its functions, and its concepts. Next, Kumaran goes over how Spark capabilities such as parallel processing, execution plans, state management options, and machine learning work with extract, transform, load (ETL). He introduces you to batch processing use cases and processes, as well as real-time processing pipelines. After walking you through several useful best practices, Kumaran concludes with an end-to-end exercise project.

Keep your Apache web servers up and running with these practical configuration and logging techniques.

Learn how to use the GraphQL.NET framework to build a GraphQL endpoint. Discover how to create a GraphQL project, configure the schema, allow updates through mutations, and more.

Learn how to leverage SoapUI to fully automate testing of APIs.

Learn how to validate and test your API to ensure it's working as intended and solving business problems.

Learn the basics of API testing. Discover how to use several robust tools for testing APIs at scale in an organization.