Databricks Data Engineer Associate Certification Preparation

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Description of Databricks Data Engineer Associate Certification Preparation

Preparation course for Databricks Data Engineer Associate certification exam Version 2 and 3
What you'll learn
Databricks Lakehouse Platform and its tools
Build ETL pipelines
Process data incrementally
Create production pipelines
Create Dashboards in Databricks
Implement best security practices
Requirements
Internet Access: Since Databricks is a cloud-based platform, having a stable internet connection is essential to access the Databricks Workspace and its features.
Curiosity and Eagerness to Learn: A willingness to explore and learn new technologies, along with a curious mindset, is the most important requirement. Databricks provides extensive documentation, tutorials, and resources that can guide you through the learning process.
By leveraging these minimal requirements, you can begin your journey of learning Databricks and gradually build your knowledge and skills over time. As you progress, you can delve deeper into the platform's advanced features and explore real-world data engineering use cases. Remember, the key is to start and keep learning through hands-on practice and continuous exploration.
Basic SQL knowledge
Description
Whether you're a seasoned data professional or just starting your journey, this course provides the perfect blend of theory and hands-on examples to ensure your success. With practical exercises and step-by-step guidance, you will learn how to navigate the Data Lakehouse architecture, explore the Data Science and Engineering workspace, and master the powerful Delta Lake.A Certified Databricks Data Engineer unlocks endless possibilities in the world of data processing and analytics. In this comprehensive course, you will gain the knowledge and skills to harness the power of the Databricks Lakehouse Platform, empowering you to tackle real-world data challenges with confidence and efficiency. Here's a breakdown of the topics covered in this course:Understand how to use and the benefits of using the Databricks Lakehouse Platform and its tools, including:Data Lakehouse (architecture, descriptions, benefits)Data Science and Engineering workspace (clusters, notebooks, data storage)Delta Lake (general concepts, table management and manipulation, optimizations)Build ETL pipelines using Apache Spark SQL and Python, including:Relational entities (databases, tables, views)ELT (creating tables, writing data to tables, cleaning data, combining and reshaping tables, SQL UDFs)Python (facilitating Spark SQL with string manipulation and control flow, passing data between PySpark and Spark SQL)Incrementally process data, including:Structured Streaming (general concepts, triggers, watermarks)Auto Loader (streaming reads)Multi-hop Architecture (bronze-silver-gold, streaming applications)Delta Live Tables (benefits and features)Build production pipelines for data engineering applications and Databricks SQL queries and dashboards, including:Jobs (scheduling, task orchestration, UI)Dashboards (endpoints, scheduling, alerting, refreshing)Understand and follow best security practices, including:Unity Catalog (benefits and features)Entity Permissions (team-based permissions, user-based permissions)These topics provide a comprehensive coverage of the Databricks Lakehouse Platform and its tools, allowing learners to gain a solid understanding of data engineering concepts and practices using Databricks.
Who this course is for
Data Engineers: Data engineers who work with big data and want to enhance their skills in managing and processing data using Databricks
Data Scientists: Data scientists who want to expand their knowledge of data engineering to build efficient pipelines for their data science workflows.
Software Engineers: Software engineers who are involved in building data-intensive applications and want to leverage Databricks for data processing
Database Administrators: Database administrators who want to gain expertise in managing and optimizing data lakes and data pipelines using Databricks.
Data Architects: Data architects who are responsible for designing data architecture and want to incorporate the Databricks platform to their architecture stack
Analytics Professionals: Professionals working in analytics or business intelligence roles who want to leverage Databricks for data engineering tasks
Data Consultants: Consultants or freelancers who provide data engineering services and want to specialize in using Databricks for their projects.Published 7