Rating
4
Custom Training
Hybrid
This training course, 'Big Data Engineering with Hadoop and Spark', is designed to immerse participants in the landscape of big data technologies. Focusing on two of the most popular frameworks, Hadoop and Spark, this course will guide attendees through foundational concepts, best practices, and real-world applications. By the end of the training, participants will not only understand the architecture and functionalities of Hadoop and Spark but will also gain hands-on experience in processing and analyzing large datasets effectively.
In this comprehensive training program, participants will explore the intricacies and capabilities of big data frameworks, specifically Hadoop and Apache Spark. The course will begin with an introduction to big data concepts, illustrating the importance of dealing with vast amounts of data in today’s world. Following this, we will delve into the Hadoop ecosystem, exploring HDFS (Hadoop Distributed File System) and MapReduce programming model, as well as various components such as Hive and HBase. You'll learn how to set up a Hadoop cluster, manage data ingestion, and perform data transformations efficiently. Next, we will shift our focus to Apache Spark, highlighting its speed and ease of use compared to Hadoop. Participants will engage in hands-on exercises, utilizing Spark’s RDDs (Resilient Distributed Datasets) and DataFrames to manipulate and analyze data. The course will cover Spark Streaming for real-time data processing and Spark SQL for querying structured data, providing learners with a diverse toolkit for data engineering. Moreover, the training will include real-world case studies to demonstrate how companies leverage these technologies to drive decision-making processes. You’ll also learn best practices for tuning performance and managing resources in a big data environment, ensuring that you can deploy and maintain your own big data infrastructure efficiently. At the conclusion of this training, participants will possess the essential skills needed to begin their journey as big data engineers, equipped with the knowledge to tackle the complexities of data processing and analysis.
Mid Level Manager
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No prior knowledge of the subject is required
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Expert-led courses designed by industry leading professionals
Flexible formats: online, in-person, and blended options.
Covers a wide range of industries and skills.
Customizable programs to meet your company’s specific needs.
Interactive experiences designed to boost retention.
Scalability to accommodate teams of any size
Upon successful completion, you will receive the nationally recognized ICT50220 Diploma of Big Data Engineering qualification. This certification encompasses specializations in Data Analytics and Data Warehousing and is recognized by industry leaders, enhancing your career opportunities in the field of big data technologies.
Christina streamlined our cloud infrastructure. Her DevOps expertise saved us a lot of time!
She automated our deployments seamlessly. Excellent work!
Christina is highly skilled in Kubernetes and Docker. Great to work with!
Her CI/CD solutions were spot on. I highly recommend her for any cloud project!
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
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