Advanced Data Engineering with Snowflake from Coursera

You'll start by learning about DevOps, DevOps practices, and how DevOps fits into the context of data engineering. You'll incorporate source control, declarative management of database objects, continuous delivery, and use a command-line interface to implement DevOps best practices into a data pipeline. You'll specifically learn how to:

- Use Snowflake's git integration to add source control to your data pipeline

- Use GitHub for team-wide collaboration on your data pipeline

- Use CREATE OR ALTER to declaratively manage database objects

- Use GitHub Actions to implement continuous delivery for your pipeline

- Use Snowflake CLI to deploy changes into dedicated data environments

You'll also learn about observability, and how to implement it to maintain and monitor the health and performance of your data pipeline. You'll specifically learn how to:

- Use logs to keep a record of events that occur within your pipeline

- Use traces to maintain a detailed journey of events for operations in your pipeline

- Use alerts to monitor for specific conditions in your pipeline, and combine them with notifications to encourage action among team members if critical errors occur in the pipeline

Throughout the course, you'll follow along with the instructor using a combination of Snowflake, Visual Studio Code, GitHub, and the command line. The course is supplemented with readings containing resources to level up your understanding of specific concepts.

You'll come away understanding how to incorporate DevOps best practices into data pipelines, and how to use observability to monitor the health and performance of pipelines.

What's inside

Syllabus

DevOps with Snowflake

In this module, you'll understand how DevOps helps software development teams iterate safely and efficiently, and understand how those practices can be applied in the field of data engineering. You'll learn how to implement a few key DevOps best practices for data pipelines. Namely, you'll learn how to implement source control for pipeline objects, how to declaratively manage database objects, and how to introduce changes to dedicated data development environments using continuous integration. By the end of the module, you'll understand how data pipelines can be built collaboratively by large teams, and how they can be evolved efficiently and reliably.

Career center

Learners who complete Advanced Data Engineering with Snowflake will develop knowledge and skills that may be useful to these careers:

Data Engineer

A Data Engineer designs, builds, and maintains the infrastructure and systems that collect, process, and store data. This advanced course provides highly relevant skills for a Data Engineer, focusing on implementing robust and reliable data pipelines. It helps build a strong foundation in DevOps best practices, including source control using Snowflake's git integration and GitHub for collaborative development. Learning to declaratively manage database objects and implement continuous delivery with GitHub Actions directly prepares you for deploying and evolving data systems efficiently. Furthermore, mastering observability with logs, traces, and alerts for monitoring pipeline health is crucial for any Data Engineer ensuring system reliability and performance. This particular course, with its hands-on approach using Snowflake, Visual Studio Code, GitHub, and the command line, offers practical expertise highly sought after in the field.

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Data Operations Engineer

A Data Operations Engineer applies DevOps principles to data management, aiming to improve the quality, speed, and collaboration across data teams. This course is an excellent fit for a Data Operations Engineer, as it directly teaches the implementation of DevOps best practices for data pipelines. You will learn to incorporate source control using Snowflake's git integration and GitHub, declaratively manage database objects with CREATE OR ALTER, and implement continuous delivery for data pipelines using GitHub Actions. The course's focus on deploying changes into dedicated data environments and the use of the command line are core to DataOps methodologies. Furthermore, comprehensive coverage of observability, including logs, traces, and alerts for monitoring pipeline health, is essential for ensuring operational efficiency and reliability. This particular course provides specialized, hands-on expertise in establishing robust and collaborative data operational workflows.

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Data Platform Engineer

A Data Platform Engineer is responsible for building, maintaining, and scaling the underlying infrastructure and tools that enable data teams to store, process, and analyze data efficiently. This course provides highly relevant skills for a Data Platform Engineer specializing in modern cloud data platforms like Snowflake. The comprehensive coverage of DevOps best practices, including source control using git integration, declarative management of database objects, and continuous delivery with GitHub Actions, is fundamental to building a robust and automated data platform. Furthermore, the detailed exploration of observability techniques — using logs, traces, and alerts for monitoring data pipeline health and performance — is critical for ensuring the reliability and operational efficiency of the entire data platform. This particular course's hands-on approach helps platform engineers create scalable, maintainable, and high-performing data environments.

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Data Infrastructure Engineer

A Data Infrastructure Engineer specializes in building and maintaining the foundational systems, tools, and environments that support an organization's data needs, focusing on scalability and performance. This course provides crucial skills for a Data Infrastructure Engineer, as it deepens understanding of operationalizing robust data pipelines using DevOps best practices. The curriculum covers implementing source control with Snowflake's git integration, declaratively managing database objects, and continuous delivery via GitHub Actions for reliable deployments. Furthermore, mastering observability techniques including logs, traces, and alerts to monitor data pipeline health is fundamental to ensuring the stability and efficiency of critical data infrastructure. This particular course, with its hands-on approach and focus on collaborative, evolvable data pipelines, equips engineers to design and manage resilient data platforms.

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DevOps Engineer

A DevOps Engineer bridges the gap between development and operations, automating software delivery and infrastructure management to improve speed, efficiency, and reliability. This course is highly relevant for a DevOps Engineer aiming to specialize in data platforms, as it explicitly teaches how to implement DevOps best practices within data engineering. You will learn to incorporate source control using Snowflake's git integration and GitHub, declaratively manage database objects, and deploy changes using continuous delivery with GitHub Actions. The course's practical emphasis on the command line and dedicated data environments is central to DevOps methodologies. Additionally, understanding observability with logs, traces, and alerts, critical for monitoring data pipeline health, directly aligns with the operational responsibilities of a DevOps Engineer in maintaining complex systems. This particular course offers a specialized application of DevOps principles to the burgeoning field of data, enhancing versatility.

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Data Engineering Consultant

A Data Engineering Consultant advises organizations on designing, implementing, and optimizing their data strategies and infrastructure, often leading complex projects. This advanced course provides highly relevant expertise for a Data Engineering Consultant, offering in-depth knowledge of modern data pipeline development and operational best practices. The comprehensive coverage of DevOps principles, including source control with GitHub and continuous delivery with GitHub Actions, is essential for guiding clients through robust, scalable solution deployments. Furthermore, mastering observability with logs, traces, and alerts empowers a consultant to establish effective monitoring strategies and ensure operational excellence for client data platforms. This particular course, by emphasizing collaborative development and efficient evolution of data pipelines within the Snowflake ecosystem, provides the practical and strategic insights necessary to deliver high-impact consulting services.

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Data Architect

A Data Architect designs the overarching data strategy, including data flow, storage, and processing solutions, ensuring scalability, security, and integrity across an organization. While a Data Architect typically focuses on high-level design, this course provides a deep understanding of the practical implementation of robust data pipelines and operational excellence, which is invaluable for designing effective solutions. The emphasis on DevOps best practices, such as declarative management of database objects and continuous delivery, helps architects design systems that are maintainable and easily deployable. Furthermore, a firm grasp of observability concepts, using logs, traces, and alerts, ensures that designed architectures can be effectively monitored and managed post-deployment. This particular course, by detailing how data pipelines can be built collaboratively and evolved reliably, provides critical insights into building resilient and high-performing data ecosystems.

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Big Data Engineer

A Big Data Engineer builds and maintains the large-scale data processing systems and infrastructures required to handle vast amounts of data efficiently. This course provides highly pertinent skills for a Big Data Engineer, as it focuses on advanced techniques for implementing robust and scalable data pipelines, particularly within a powerful, cloud-native platform like Snowflake. The curriculum's emphasis on DevOps best practices, including source control using git integration, declarative management of database objects, and continuous delivery with GitHub Actions, is crucial for managing the complexity of big data environments. Furthermore, mastering observability techniques — using logs, traces, and alerts to monitor the health and performance of these pipelines — is essential for ensuring the reliability and operational efficiency of big data initiatives. This particular course delivers practical, hands-on expertise vital for operationalizing big data solutions effectively.

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Site Reliability Engineer

A Site Reliability Engineer applies software engineering principles to operations, ensuring the reliability, scalability, and performance of large-scale systems. This course may be particularly helpful for a Site Reliability Engineer focusing on data platforms, as it provides a comprehensive introduction to implementing observability for data pipelines. The modules on using logs to record events, traces for detailed operational journeys, and alerts coupled with notifications for critical error detection are directly applicable to SRE responsibilities. Understanding how to monitor the health and performance of data pipelines using Snowflake's observability framework can translate into building more resilient data infrastructure. Furthermore, the course's emphasis on continuous delivery and managing changes to dedicated data environments may help SREs ensure efficient and reliable deployments of data-intensive applications. This particular course offers specialized knowledge in maintaining data system uptime and performance for critical data services.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models into production, often requiring robust data infrastructure and operational pipelines. This course may be helpful for a Machine Learning Engineer, as it focuses on the engineering principles of building and maintaining reliable data pipelines, which are the lifeblood of any successful machine learning system. The emphasis on DevOps best practices, such as source control with GitHub and continuous delivery for data environments, mirrors the MLOps principles essential for deploying and managing ML models. Understanding observability through logs, traces, and alerts is vital for monitoring the health of data feeding ML models and the performance of the models themselves in production. This particular course provides the critical data engineering foundation that can enable a Machine Learning Engineer to build more robust, reproducible, and monitorable ML solutions.

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Analytics Engineer

An Analytics Engineer builds and maintains data models within data warehouses, transforming raw data into reliable datasets optimized for analytical consumption. This course may be useful for an Analytics Engineer by providing a deep understanding of the underlying data pipelines that feed their analytical models. While an Analytics Engineer focuses on the 'T' in ELT, understanding the 'EL' and the operational robustness of the pipeline is crucial. The course's emphasis on DevOps best practices, such as source control and continuous delivery for data environments, helps ensure that the data flowing into their models is consistent and reliable. Crucially, the observability module, teaching the use of logs, traces, and alerts, can empower an Analytics Engineer to collaborate more effectively with data engineers, diagnose upstream data issues, and ensure the health of their data sources. This particular course helps bridge the gap between raw data and analytics-ready assets.

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Cloud Engineer

A Cloud Engineer designs, implements, and manages cloud-based infrastructure and services, ensuring optimal performance, security, and cost-efficiency. This course may be useful for a Cloud Engineer specializing in data solutions, as it focuses on building and managing data pipelines within a cloud-native platform like Snowflake. The practical skills in implementing DevOps best practices, such as source control, continuous delivery with GitHub Actions, and declarative management of database objects, are highly transferable to broader cloud infrastructure automation. Furthermore, understanding how to implement observability using logs, traces, and alerts to monitor pipeline health is crucial for maintaining reliable cloud data services. This particular course offers specific expertise in operationalizing data workflows in a cloud environment, which can enhance a Cloud Engineer's ability to support data-intensive applications and platforms within the cloud ecosystem.

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Integration Engineer

An Integration Engineer designs and implements solutions that connect different software systems and applications, enabling seamless data flow and communication across an enterprise. This course may be helpful for an Integration Engineer working with data-intensive systems, especially those built on cloud platforms like Snowflake. The focus on building and maintaining robust data pipelines, including the application of DevOps principles such as source control with GitHub and continuous delivery, is highly relevant to ensuring reliable data integration. Moreover, the module on observability, teaching the use of logs, traces, and alerts to monitor the health and performance of data pipelines, can be directly applied to ensuring the reliability of data integration processes. This particular course offers practical skills in managing the operational aspects of data movement, which can enhance an Integration Engineer's ability to build dependable system interconnections.

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Data Quality Engineer

A Data Quality Engineer focuses on ensuring the accuracy, completeness, and consistency of data throughout its lifecycle, often implementing processes to validate and cleanse information. This course may be helpful for a Data Quality Engineer, particularly in understanding how to monitor the integrity and flow of data within a pipeline. The observability section, covering the use of logs and traces to record events and detailed operational journeys, provides crucial tools for identifying where data quality issues might originate or manifest. Implementing alerts for specific conditions in your data pipeline can be directly applied to detect anomalies or failures related to data quality. This particular course offers a practical understanding of how robust data engineering practices, including continuous delivery and monitoring, contribute to maintaining high data quality across an organization's analytical landscape.

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Database Administrator

A Database Administrator manages and maintains database systems, ensuring their performance, security, and availability. For a Database Administrator pivoting to cloud-native data platforms, this course may be useful. It introduces concepts like declarative management of database objects within Snowflake, which is a modern approach to managing schema and other database elements programmatically. While traditional DBAs focus on specific server management, understanding how to implement continuous delivery for database changes using GitHub Actions helps prepare for automated database operations. Furthermore, the observability module, teaching the use of logs, traces, and alerts to monitor data environment health, aligns with the core responsibility of a Database Administrator to ensure system integrity and performance. This particular course offers a specialized perspective on managing data environments in a highly automated, cloud-based context.

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