Gain expertise in developing APIs, microservices, and cloud-based architectures using languages like Java, C#, and JavaScript.
For learners with an understanding of data or software concepts and are looking to develop their skills in data management, data modelling, data infrastructure and cloud engineering.
Participants can expect to gain hands-on experience with tools and technologies commonly used in data engineering. This program is also great to use for re-skilling and growing talent from within.
Learn the principles of both SQL and NoSQL databases. Dive into the essential process of ETL, exploring tools and best practices to ensure data quality and reliability. Harness the power of Python developing scripting and automation skills to streamline data workflows. Exploring key Python constructs and libraries for data engineering tasks.
Explore data collection, cleaning, and basic statistical techniques. Derive insights from raw data and present them eectively. Develop essential programming skills in Python, debugging techniques, version control, and the principles of test-driven development (TDD) to ensure robust and maintainable code. Apply ML, time series analysis, text analytics, and NLP foundations to solve complex real-world problems.
Seamlessly integrate data from various sources, including real-time streaming data. Discover the power of Hadoop and Spark and explore advanced data storage solutions. Learn how to establish data quality frameworks and implement data governance policies to ensure data accuracy and compliance with regulatory standards.
Implement CI/CD practices specific to data engineering. Understand the principles of DataOps for ecient data pipeline development and management. Explore cloud engineering best practices and tools. Delve into MLOps to streamline ML model deployment. Learn how to optimise data processes and infrastructure for sustainability.
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