Requirements

• Basic Knowledge of Data Engineering: Familiarity with concepts like data pipelines
• ETL (Extract
• Transform
• Load) processes
• and data transformation.
• Experience with SQL: Knowledge of SQL (Structured Query Language) for querying and manipulating data. This is essential for working with Databricks and Spark SQL for data transformations.
• Familiarity with Cloud Platforms: Basic understanding of cloud services (such as AWS
• Azure
• or Google Cloud)
• as Databricks integrates with these platforms for storage and compute resources.

Description

The Databricks Data Engineer Associate course is a comprehensive learning path designed to equip data engineering professionals with the skills necessary to build, optimize, and manage scalable data pipelines using the Databricks platform. Databricks, built on top of Apache Spark, is a powerful unified analytics platform that integrates with cloud-based solutions such as AWS, Azure, and Google Cloud. This course focuses on the essential tools and concepts for data engineers, including data pipelines, cloud integration, performance optimization, and the use of Databricks notebooks for collaboration and development.

Course Overview

Data engineering is a rapidly evolving field that demands expertise in managing big data, building robust data pipelines, and ensuring that large-scale data processing workflows run efficiently. The Databricks Data Engineer Associate certification is designed to prepare you for these challenges by providing hands-on experience with Databricks and Apache Spark.

Throughout the course, learners will gain in-depth knowledge of data engineering fundamentals, cloud platforms, and the key technologies required for building reliable data pipelines. You will also be introduced to advanced techniques for optimizing and managing data workflows and ensuring high performance in distributed data environments.

This course is not only about learning Databricks and Apache Spark but also about understanding how to apply these technologies to real-world scenarios. You will work on projects and case studies to gain practical experience in solving data engineering challenges in the context of modern cloud infrastructures.

Key Concepts Covered

1. Introduction to Databricks and Apache Spark

The course begins with a deep dive into the Databricks platform and Apache Spark, two foundational technologies for handling big data. Databricks integrates Spark with cloud storage and compute resources, enabling data engineers to build and scale data pipelines easily.

• Databricks Overview: Learn about the features of the Databricks platform, including the collaborative notebooks, the interactive development environment, and the integration with cloud-based platforms such as AWS, Azure, and Google Cloud.

• Apache Spark Fundamentals: Understand how Apache Spark works, including its core components (Spark SQL, Spark Streaming, and MLlib) and its architecture for distributed computing. Gain insight into the advantages of Spark for big data processing and how it differs from traditional data processing technologies.

2. Building Data Pipelines

Data pipelines are the backbone of modern data engineering. This section focuses on creating, managing, and optimizing data pipelines using Databricks.

• ETL (Extract, Transform, Load) Workflows: Learn how to build ETL pipelines using Databricks, transforming raw data into meaningful datasets. You will cover extracting data from various sources, applying transformations using Spark, and loading it into target destinations such as data lakes or relational databases.

• Data Ingestion: Understand the process of ingesting data into Databricks from a variety of sources, including cloud storage systems, relational databases, and streaming data sources. Learn best practices for handling batch and real-time data ingestion.

• Data Transformation: Gain hands-on experience with Spark SQL to clean, filter, and transform data. Learn how to join datasets, apply aggregations, and perform complex queries to process large-scale data.

3. Delta Lake and Data Storage

Delta Lake is a powerful feature of Databricks that allows you to build a reliable and scalable data lake with ACID transaction support. It provides a unified platform for managing both batch and real-time data.

• Delta Lake Overview: Learn the benefits of Delta Lake, such as its ability to handle structured and unstructured data, schema enforcement, and the management of large-scale data lakes.

• Delta Lake Operations: Learn how to perform basic Delta Lake operations like creating tables, inserting, updating, and deleting data, and managing transactions. Explore how Delta Lake handles time travel and versioning for historical data analysis.

• Optimizing Data Storage: Understand how to optimize data storage by leveraging Delta Lake’s features like partitioning, compaction, and data skipping to improve query performance and reduce storage costs.

4. Performance Optimization

Optimizing data processing performance is critical in big data environments. This section covers techniques to improve the efficiency of data pipelines and queries.

• Caching and Persistence: Learn how to cache data in memory to improve the performance of iterative operations. You will also explore the concept of persistence and how to use it to manage data storage in Spark.

• Partitioning: Understand how partitioning data can improve performance by enabling parallel processing and reducing data shuffling.

• Tuning Spark Jobs: Gain hands-on experience with tuning Spark jobs to improve performance, such as optimizing shuffle operations, reducing the number of stages, and adjusting configurations for large-scale workloads.

5. Cluster Management

Databricks leverages clusters to process data across distributed systems. Managing clusters efficiently is a key skill for any data engineer working in a big data environment.

• Cluster Configuration: Learn how to configure clusters in Databricks, selecting the appropriate cluster size, type, and runtime environment for your workloads.

• Cluster Optimization: Understand best practices for optimizing cluster performance, such as adjusting resource allocation and scaling clusters based on workload demands.

• Cluster Monitoring and Troubleshooting: Explore tools for monitoring cluster performance, identifying issues, and troubleshooting cluster-related problems to ensure that data pipelines run smoothly.

6. Data Security and Governance

Data security and governance are essential for protecting sensitive information and ensuring compliance with regulatory standards.

• Access Control and Permissions: Learn how to configure role-based access control (RBAC) to secure data in Databricks, ensuring that only authorized users can access or modify specific datasets and resources.

• Data Encryption: Understand how to encrypt data both in transit and at rest to protect sensitive information and ensure compliance with industry standards.

• Audit Logging: Learn how to implement audit logging in Databricks to track user actions and ensure data integrity.

7. Collaborative Development with Databricks Notebooks

Databricks Notebooks provide an interactive environment for developing and testing data engineering code. These notebooks support collaboration and version control, making them a key tool for data engineers.

• Using Databricks Notebooks: Learn how to create, share, and collaborate on notebooks for writing data engineering code, building visualizations, and documenting processes.

• Version Control: Understand how to use Git integration within Databricks notebooks for version control and collaborative development.

8. Integration with Cloud Services

Databricks integrates seamlessly with major cloud platforms like AWS, Azure, and Google Cloud, providing a powerful environment for working with cloud-based data and computing resources.

• Cloud Storage Integration: Learn how to use cloud storage services (such as S3 or ADLS) with Databricks to store and retrieve data for processing.

• Cloud Compute Integration: Understand how Databricks integrates with cloud computing services to scale processing resources dynamically based on workload demands.

Who this course is for:

• Data Engineer
• Big Data Developers
• Cloud Data Engineers