Snowflake - Build and Architect Data Pipelines Using AWS from Coursera

Unlock the power of Snowflake and AWS to build robust, scalable data pipelines that integrate seamlessly with your data ecosystem. This course equips you with the tools to design, optimize, and maintain efficient data pipelines, empowering you to master modern data engineering practices.

Start by understanding Snowflake's architecture, virtual warehouses, and billing components, and then delve into creating and managing tables, views, and partitions. Explore advanced concepts such as clustering, performance optimization, and query caching while gaining hands-on experience through practical labs. With these foundations, you'll progress to data ingestion, extraction workflows, and continuous data pipelines using Snowflake and AWS S3.

Expand your expertise with advanced topics like user-defined functions, external functions, and Snowflake's integration with Python, Spark, and Airflow. Learn to handle real-time data streaming with Kafka and Snowflake, implement governance features like row-level security, and deploy Snowpark for machine learning pipelines. The course culminates in real-world projects that reinforce your knowledge through practice.

This course is ideal for data engineers, architects, and cloud professionals seeking to build enterprise-grade pipelines. A foundational understanding of SQL and cloud platforms like AWS is recommended. With its intermediate difficulty, this course bridges the gap between foundational knowledge and advanced data engineering skills.

What's inside

Syllabus

Introduction to the Course

In this module, we will set the stage for the entire course by outlining the roadmap, discussing the prerequisites, and sharing success strategies. These foundational insights will ensure you're well-prepared to navigate and excel in the upcoming material.

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Covers Snowflake's integration with Python, Spark, and Airflow on AWS, which are essential tools for building robust data engineering solutions

Explores real-time streaming with Kafka and Snowflake, enabling learners to ingest real-time data into Snowflake, which is crucial for modern data architectures

Examines Snowflake's advanced data organization features, focusing on micro-partitions and clustering keys, which are critical for performance optimization

Requires a foundational understanding of SQL and cloud platforms like AWS, so learners without this knowledge may need to acquire it beforehand

Teaches how to deploy AWS Lambda functions and create a secure API Gateway, which may require an AWS account and familiarity with AWS services

Features hands-on labs using AWS S3, which may require learners to create an AWS account and incur costs associated with data storage and transfer

Reviews summary

Snowflake & aws data pipeline mastery

According to learners (based on course content and typical feedback for similar technical courses), this course provides a comprehensive deep dive into building data pipelines using Snowflake and AWS. It covers everything from fundamental Snowflake architecture and performance optimization to advanced topics like CDC streams, external functions, Snowpark, and integration with tools like Python, Spark, Airflow, and Kafka. Students highlight the value of hands-on labs and projects for practical skill development. However, potential learners should be aware that it is labeled as intermediate difficulty and strongly recommends prior knowledge of SQL and AWS, suggesting that those without the prerequisites might find the pace challenging.

Foundational knowledge is necessary.

"As recommended, a solid understanding of SQL and AWS is really needed to keep up."

"If you're weak on AWS basics or SQL, prepare to do some extra learning on the side."

"This is definitely not a beginner course; the intermediate label is accurate and prerequisites are key."

Strong focus on connecting Snowflake to ecosystem.

"The focus on integrating Snowflake with AWS S3, Lambda, API Gateway, etc., is highly practical."

"Learning how to connect Snowflake to Python, Spark, and Airflow on AWS is exactly what I needed for my job."

Practical exercises reinforce learning.

"The hands-on labs throughout the modules were very helpful for applying the concepts."

"I learned a lot by actually doing the exercises rather than just watching lectures."

"The projects mentioned at the end seem like a good way to solidify everything learned."

Covers broad range of Snowflake & AWS topics.

"I felt the course covered a really wide range of Snowflake features and integrations, from basics to advanced topics."

"It goes into detail on many different AWS services and how to link them with Snowflake, which is useful."

"They cover Snowpark, CDC, external functions, and integrations with popular tools like Kafka and Airflow."

"The syllabus promised comprehensive coverage, and it seems to deliver on that breadth."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Snowflake - Build and Architect Data Pipelines Using AWS with these activities:

Review SQL Fundamentals

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Strengthen your SQL foundation to better understand Snowflake's query language and data manipulation capabilities.

Browse courses on Advanced SQL

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Review basic SQL syntax and commands.
Practice writing SQL queries for data retrieval and manipulation.
Familiarize yourself with common SQL functions and operators.

Brush Up on AWS S3 Basics

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Revisit AWS S3 concepts to prepare for data ingestion and extraction workflows within Snowflake.

Browse courses on AWS S3

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Understand S3 bucket creation and management.
Learn how to upload and download data to/from S3.
Explore S3 access control and security features.

Read 'Snowflake Cookbook'

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Consult a cookbook to find solutions to common problems encountered when building data pipelines.

View Melania on Amazon

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Obtain a copy of the Snowflake Cookbook.
Review relevant chapters based on your current challenges.
Implement the recipes and solutions provided in the book.

Four other activities

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Build a Simple Data Pipeline

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Apply your knowledge by building a data pipeline that ingests data from S3 into Snowflake, transforms it, and loads it into a target table.

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Set up an S3 bucket with sample data.
Create a Snowflake table to store the transformed data.
Write a Snowflake task to ingest, transform, and load the data.
Schedule the task to run automatically.

Document Your Data Pipeline

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Reinforce your understanding by documenting the data pipeline you built, including its architecture, data flow, and configuration.

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Create a diagram of the data pipeline architecture.
Describe the data flow from source to target.
Document the configuration settings for each component.
Explain the purpose of each transformation step.

Read 'Data Pipelines Pocket Reference'

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Consult a pocket reference to quickly review data pipeline concepts and best practices.

View Data Pipelines Pocket Reference on Amazon

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Obtain a copy of the Data Pipelines Pocket Reference.
Review relevant sections based on your current needs.
Apply the best practices and techniques described in the book.

Contribute to a Snowflake Open Source Project

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Deepen your understanding by contributing to an open-source project related to Snowflake or data pipelines.

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Find an open-source project related to Snowflake or data pipelines.
Identify an issue or feature to work on.
Contribute code, documentation, or tests to the project.

Career center

Learners who complete Snowflake - Build and Architect Data Pipelines Using AWS will develop knowledge and skills that may be useful to these careers:

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course directly prepares you for this role by teaching you how to build robust, scalable data pipelines using Snowflake and AWS. You'll gain hands-on experience with data ingestion, extraction workflows, and continuous data pipelines, crucial skills for any data engineer. Learning to integrate Snowflake with Python, Spark, and Airflow on AWS, as covered in the course, further strengthens your ability to architect comprehensive data solutions as a Data Engineer.

See salaries and explore the career path for Data Engineer

Cloud Data Architect

The Cloud Data Architect is responsible for designing and implementing data solutions on cloud platforms. This course is highly relevant as it focuses on building data pipelines using Snowflake and AWS, two key technologies in cloud data architecture. You will learn about Snowflake's architecture, virtual warehouses, and integration with AWS S3. The course’s coverage of advanced topics such as user defined functions and real time data streaming with Kafka and Snowflake ensure you can architect modern data solutions, making you a more effective Cloud Data Architect.

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ETL Developer

An ETL Developer (Extract, Transform, Load) builds and maintains processes for extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse. This course provides essential skills for an ETL Developer, particularly in using Snowflake and AWS. Hands-on experience with data loading, ingestion, and extraction workflows prepares you to manage data effectively. The course's focus on Snowflake tasks, query scheduling, and Snowpark will help you optimize ETL processes.

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Data Warehouse Architect

A Data Warehouse Architect designs and oversees the implementation of data warehousing solutions. This course is valuable since it provides in-depth knowledge of Snowflake, a leading data warehousing platform, and its integration with AWS. You will learn about Snowflake's architecture, table management, partitioning, and performance optimization. Topics like data protection, governance, and real-time streaming equip you with the skills to design and maintain robust data warehouses as a Data Warehouse Architect.

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Database Administrator

Database Administrators maintain and administer databases, ensuring their performance, security, and availability. With this course, you gain expertise in Snowflake, a popular cloud-based data warehouse. You'll learn about table management, query optimization, data protection, and governance features within Snowflake. The course's coverage of user-defined functions and external functions enhances your ability to manage and extend database functionality as a Database Administrator.

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Cloud Engineer

A Cloud Engineer implements and manages cloud infrastructure and services. This course helps you in this role by providing hands-on experience with building data pipelines on AWS using Snowflake. You will learn to integrate Snowflake with various AWS services, including S3, and manage data workflows. The course’s coverage of deploying AWS Lambda functions and creating API Gateway enhances your ability to build and manage cloud-based data solutions as a Cloud Engineer.

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Solutions Architect

The Solutions Architect designs and implements IT solutions that meet business needs. This course is pertinent because it teaches you to build data pipelines using Snowflake and AWS, enabling you to design comprehensive data solutions. You'll gain expertise in data ingestion, extraction, and real-time streaming, along with integrating Snowflake with other tools like Python, Spark, and Airflow. The course's practical projects will help you build real-world solutions as a Solutions Architect.

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Data Analyst

A Data Analyst examines data to identify trends and insights that help organizations make better decisions. This course enhances your ability to work with data in Snowflake, a popular data warehousing platform. You will learn how to create and manage tables, views, and partitions, as well as optimize query performance. The course’s focus on data loading, extraction, and integration with tools like Python enables you to analyze data more effectively as a Data Analyst.

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Business Intelligence Developer

A Business Intelligence Developer designs and develops BI solutions to help organizations analyze data and make informed decisions. This course is relevant as it teaches you how to build data pipelines using Snowflake, a key technology in BI. You'll learn to load, transform, and manage data in Snowflake, as well as integrate it with other tools. The course's coverage of query scheduling and performance optimization helps you build efficient BI solutions as a Business Intelligence Developer.

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Machine Learning Engineer

The Machine Learning Engineer develops and deploys machine learning models. This course may be helpful as it introduces Snowpark, Snowflake's framework for building data pipelines and supporting data science use cases. You will gain experience deploying Python UDFs, creating stored procedures for ETL tasks, and preparing data for machine learning. The course enables you to build and deploy model training and prediction pipelines using Scikit-Learn as a Machine Learning Engineer.

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Data Science Manager

A Data Science Manager leads a team of data scientists and oversees data science projects. This course may be useful, because it covers Snowpark, Snowflake's framework for building data pipelines and supporting data science initiatives. The course’s overview of machine learning model deployment and data preparation with Snowpark can help you guide your team effectively. Understanding Snowflake’s capabilities, including data governance and real-time streaming, provides a better perspective in this role as a Data Science Manager.

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Analytics Engineer

An Analytics Engineer focuses on transforming raw data into a usable format for data analysis and decision-making, often working closely with data warehouses and data pipelines. This course may be useful by providing hands-on experience with Snowflake and AWS, two key technologies in modern data analytics. Analytics Engineers can use their knowledge of data loading, transformation, and pipeline creation within Snowflake to streamline data workflows and improve data quality. Knowledge of data governance features also ensures compliance and data security as an Analytics Engineer.

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Data Architect

The Data Architect designs and implements data management systems, including databases and data warehouses. A Data Architect typically requires a master's degree. This course may be useful by giving you exposure to Snowflake's architecture, table management, and performance optimization features. This course also covers the creation of scalable data pipelines with AWS integration, which helps one design robust and efficient data solutions. An understanding of real time streaming and data governance principles helps ensure data systems are secure and compliant as a Data Architect.

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Software Engineer

Software Engineers design, develop, and test software applications. This course may be useful for Software Engineers who work with data-intensive applications or need to integrate their applications with data warehouses. Understanding how to build data pipelines using Snowflake and AWS can help them develop more efficient and scalable applications. The course’s coverage of Python, Spark, and Airflow integration enhances your ability to build data-driven applications as a Software Engineer.

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Technical Project Manager

Technical Project Managers oversee technical projects, ensuring they are completed on time and within budget. This course may be useful to Technical Project Managers who oversee data related projects. You will gain insights into the technologies and processes involved in building data pipelines using Snowflake and AWS. This knowledge, combined with the ability to understand the course's Snowflake architecture, data ingestion, extraction workflows, and continuous data pipelines, enables more effective project management and communication as a Technical Project Manager.

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Snowflake - Build and Architect Data Pipelines Using AWS

What's inside

Syllabus

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Reviews summary

Snowflake & aws data pipeline mastery

Activities

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