In this course, you'll learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different data systems, including object stores, block storage, file systems, and databases, that are built on top of these raw ingredients. You’ll also get a chance to query a Neo4j graph database and perform vector similarity search, a key feature behind generative AI and large language models. You will explore the evolution of data storage abstractions, from data warehouses to data lakes and data lakehouses, while comparing the advantages and drawbacks of each architectural paradigm.
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In this course, you'll learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different data systems, including object stores, block storage, file systems, and databases, that are built on top of these raw ingredients. You’ll also get a chance to query a Neo4j graph database and perform vector similarity search, a key feature behind generative AI and large language models. You will explore the evolution of data storage abstractions, from data warehouses to data lakes and data lakehouses, while comparing the advantages and drawbacks of each architectural paradigm.
In this course, you'll learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different data systems, including object stores, block storage, file systems, and databases, that are built on top of these raw ingredients. You’ll also get a chance to query a Neo4j graph database and perform vector similarity search, a key feature behind generative AI and large language models. You will explore the evolution of data storage abstractions, from data warehouses to data lakes and data lakehouses, while comparing the advantages and drawbacks of each architectural paradigm.
With hands-on practice, you'll design a simple data lake using Amazon Glue, and build a data lakehouse using AWS LakeFormation and Apache Iceberg. In the last week of this course, you’ll see how queries work behind the scenes, practice writing more advanced SQL queries, compare the query performance in row vs column-oriented storage, and perform streaming queries using Apache Flink.
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Engage with peers to discuss course concepts, review material, and support each other's learning.
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Work together on projects and assignments.
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Find a group of students with complementary skills and interests.
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Meet regularly to discuss the course material.
Data Wrangling Drills
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Reinforce your understanding of data wrangling techniques and strengthen your ability to work with data effectively.
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Data Wrangling
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Find a dataset and load it into a pandas DataFrame.
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Perform data cleaning operations such as handling missing values, removing duplicates, and correcting data types.
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Explore the data and identify patterns and insights.
Attend a Data Analytics Workshop
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Deepen your understanding of data analysis techniques and tools through hands-on workshops.
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Attend the workshop and participate actively.
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Apply what you learn to your own projects.
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Intro to Apache Spark
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Expand your big data processing skills by exploring Apache Spark's capabilities for distributed computing.
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Apache Spark
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Follow tutorials on setting up and using Apache Spark.
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Create a Spark DataFrame and perform basic transformations.
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Explore Spark's RDDs and their usage for parallel processing.
Review 'Designing Data-Intensive Applications'
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Gain a comprehensive understanding of data-intensive application design and best practices.
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Secret Colors
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Read chapters on data modeling, storage, and processing.
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Identify key concepts and architectures for data-intensive applications.
Create a Data Glossary
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Strengthen your data management skills by creating a data glossary for a specific domain or project.
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Identify the relevant data terms and definitions.
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Organize the terms into a structured and logical framework.
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Publish the data glossary for reference and use.
Design a Data Lake
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Apply your knowledge of data storage and management by designing a data lake for a specific use case.
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Data Lake
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Define the requirements and scope of your data lake.
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Choose appropriate data storage technologies and tools.
-
Design the data lake architecture and data pipeline.
Form a Study Group
Show steps
Engage with peers to discuss course concepts, review material, and support each other's learning.
Show steps
- Work together on projects and assignments.
- Find a group of students with complementary skills and interests.
- Meet regularly to discuss the course material.
Data Wrangling Drills
Show steps
Reinforce your understanding of data wrangling techniques and strengthen your ability to work with data effectively.
Browse courses on
Data Wrangling
Show steps
- Find a dataset and load it into a pandas DataFrame.
- Perform data cleaning operations such as handling missing values, removing duplicates, and correcting data types.
- Explore the data and identify patterns and insights.
Attend a Data Analytics Workshop
Show steps
Deepen your understanding of data analysis techniques and tools through hands-on workshops.
Browse courses on
Data Analytics
Show steps
- Find a workshop that covers relevant topics.
- Attend the workshop and participate actively.
- Apply what you learn to your own projects.
Intro to Apache Spark
Show steps
Expand your big data processing skills by exploring Apache Spark's capabilities for distributed computing.
Browse courses on
Apache Spark
Show steps
- Follow tutorials on setting up and using Apache Spark.
- Create a Spark DataFrame and perform basic transformations.
- Explore Spark's RDDs and their usage for parallel processing.
Review 'Designing Data-Intensive Applications'
Show steps
Gain a comprehensive understanding of data-intensive application design and best practices.
View
Secret Colors
on Amazon
Show steps
- Read chapters on data modeling, storage, and processing.
- Identify key concepts and architectures for data-intensive applications.
Create a Data Glossary
Show steps
Strengthen your data management skills by creating a data glossary for a specific domain or project.
Show steps
- Identify the relevant data terms and definitions.
- Organize the terms into a structured and logical framework.
- Publish the data glossary for reference and use.
Design a Data Lake
Show steps
Apply your knowledge of data storage and management by designing a data lake for a specific use case.
Browse courses on
Data Lake
Show steps
- Define the requirements and scope of your data lake.
- Choose appropriate data storage technologies and tools.
- Design the data lake architecture and data pipeline.
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