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We're here to help you find itData Engineering with Databricks SQL - Extended is a comprehensive 4-day course designed for aspiring data engineers. Students will gain an in-depth understanding of the Databricks Lakehouse Platform, including Delta Lake fundamentals, Spark SQL for ETL processes, and Structured Streaming for incremental data processing. The course also covers the Medallion Architecture, the creation and management of Delta Live Tables, and optimizing workflows with Databricks Jobs. Additionally, learners will explore Databricks SQL for building queries and dashboards, and understand Permissions management within the Lakehouse. Upon completion, participants will be adept at implementing, optimizing, and securing data pipelines, ensuring robust and efficient data solutions.
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♱ Excluding VAT/GST
Classroom Training price is on request
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To successfully undertake the Data Engineering with Databricks SQL - Extended course, it is recommended that students have:
These prerequisites will ensure that participants can effectively engage with the course material and maximize their learning experience.
1. Introduction:
The Data Engineering with Databricks SQL - Extended course offers in-depth understanding and hands-on experience in data engineering using Databricks and Delta Lakehouse Platform, tailored for IT professionals aiming to excel in big data and analytics.
2. Job Roles and Audience:
1. Course Introduction:
The "Data Engineering with Databricks SQL - Extended" course spans four days and provides comprehensive knowledge of Databricks' Lakehouse Platform, Delta Lake fundamentals, relational entities, ETL with Spark SQL, structured streaming, medallion architecture, Delta Live Tables, Databricks Jobs, Databricks SQL, and managing permissions and security.
2. Learning Objectives and Outcomes:
The Databricks Lakehouse Platform is a unified data management system that combines the best elements of data lakes and data warehouses. It allows organizations to store vast amounts of raw data in its native format (data lake), then organize, manage, and secure this data into structured databases (data warehouse) for easier analysis and reporting. This platform supports various data engineering duties, making it ideal for processing and analyzing big data. It seamlessly integrates with AWS, enhancing performance and scalability. Pursuing a databricks certification or enrolling in a data engineering course could significantly benefit professionals working with this technology.
Delta Lake is an open-source storage layer that brings reliability to data lakes. It offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing on top of existing storage systems. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs. This helps data engineers ensure data integrity with complex, concurrent data pipelines. Enhancing these systems with a Databricks certification or an AWS data engineer certification through specific data engineering courses and data engineer classes can significantly boost one's expertise in managing and utilizing Delta Lake effectively.
Spark SQL is a module in Apache Spark that integrates relational processing with Spark’s functional programming API. It provides a common way to write SQL queries to access data, making it easier to harness the power of big data processing. With Spark SQL, professionals can blend the capabilities of SQL and procedural processing, which increases data accessibility and performance. This makes it especially valuable for tasks often covered in a data engineering course or data engineer classes, enhancing skill sets for roles needing databricks certification or aws data engineer certification.
Structured Streaming is a scalable and fault-tolerant stream processing service built into Apache Spark, which allows you to process live data streams. It enables data engineers to model streaming data as an infinite table, continuously appending incoming data. This service supports a variety of input sources and output sinks like Kafka, and integrates with data platforms such as Databricks. It provides real-time data insights, crucial for timely decision-making — a key skill covered in many data engineering courses and could prepare for certifications like AWS data engineer certification. Structured Streaming is integral for anyone pursuing a career in data engineering.
Medallion architecture is a pattern used in big data engineering to create modular, scalable applications. In this architecture, data flows through layers, each responsible for specific tasks like ingestion, transformation, and aggregation. Frequently used in platforms like AWS, where obtaining an AWS data engineer certification would be beneficial, it offers a structured approach to data processing. This architecture is crucial for data engineers, and understanding it can be enhanced by taking data engineering courses or specific data engineer classes.
Delta Live Tables is a feature in Databricks, a platform that makes it simpler to manage and automate data pipelines efficiently. It's especially useful for data engineers, who can easily build reliable and maintainable data workflows. This technology ensures that the data flows smoothly, is error-free, and automatically updates, thus helping to maintain data accuracy. By using Delta Live Tables, data engineers improve the quality of data in real-time, supporting better decision-making across various business functions. It’s highly beneficial for those pursuing a data engineering course or aiming for a Databricks certification in their career development.
Databricks Jobs refers to the scheduling and automation component within the Databricks platform, a unified data analytics system. It allows users to set up and manage periodic data processing tasks, which are crucial for data engineering workflows. By utilizing Databricks Jobs, data engineers can automate their data pipelines, ensuring timely data transformation and analysis. This feature supports various tasks, from simple data loading to complex machine learning model training, making it essential for professionals looking to enhance their capabilities in data engineering.
Databricks SQL is a component of Databricks that allows users to run SQL queries on their data. It's designed to optimize the performance of query execution, making it highly efficient for data analysis and reporting. This tool is commonly used in conjunction with data engineering, integrating well with various certifications such as AWS data engineer certification. Learning Databricks SQL can be a beneficial skill in data engineering courses and data engineer classes, helping professionals manage and make sense of large datasets efficiently and effectively.
Permissions management is the process of defining and controlling access to resources within a system, ensuring that users have appropriate levels of permissions based on their roles. It entails granting or denying rights to use specific resources like files, databases, or applications, helping maintain security and operational efficiency. Proper permissions management minimizes risks of unauthorized access and data breaches, while facilitating compliance with regulations. Techniques and best practices can be enhanced through courses like data engineering courses, specifically targeting AWS data engineer certification, which covers aspects of permissions in cloud environments.
1. Introduction:
The Data Engineering with Databricks SQL - Extended course offers in-depth understanding and hands-on experience in data engineering using Databricks and Delta Lakehouse Platform, tailored for IT professionals aiming to excel in big data and analytics.
2. Job Roles and Audience:
1. Course Introduction:
The "Data Engineering with Databricks SQL - Extended" course spans four days and provides comprehensive knowledge of Databricks' Lakehouse Platform, Delta Lake fundamentals, relational entities, ETL with Spark SQL, structured streaming, medallion architecture, Delta Live Tables, Databricks Jobs, Databricks SQL, and managing permissions and security.
2. Learning Objectives and Outcomes:
