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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides hands-on labs and interactive materials to reinforce learning
Taught by Google Cloud Training, an expert in the field of cloud computing
Emphasizes practical skills in querying partitioned datasets and creating dataset partitions, which can reduce query costs
Specifically designed for self-paced learning, allowing learners to progress at their own pace
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Reviews summary
Hands-on bigquery partitioning for cost & performance
According to learners, this course is a highly practical and effective lab for mastering date-partitioned tables in BigQuery. Students frequently highlight its hands-on approach, which provides a clear and direct path to understanding how to improve query performance and reduce costs. Many find the instructions precise and easy to follow, making complex BigQuery concepts accessible. While some more experienced users might find the content focused and prescriptive, it consistently delivers on its promise of providing immediately applicable skills for professionals working with cloud data.
Course is focused specifically on partitioning, potentially lacking broader optimization.
"Good for what it covers, but it's very focused on just partitioning. I was hoping for a broader look at BigQuery optimization."
"While thorough on its specific topic, I found myself wishing for more context or advanced use cases beyond just date partitioning."
"It's a compact lab, meaning it delivers exactly what it promises, but don't expect a deep dive into all aspects of BigQuery."
Ideal as a foundational course for those new to BigQuery partitioning.
"For experienced users, it might feel a bit basic, but it's a great starting point for anyone new to partitioning."
"I had minimal prior experience with BigQuery partitioning, and this course laid a solid foundation for me."
"It’s an excellent introductory lab that doesn't assume extensive prior knowledge of BigQuery internals."
Directly addresses critical BigQuery aspects: query performance and cost optimization.
"It directly addresses performance and cost, which is vital for BigQuery users."
"A significant number of reviews highlighted the course's effectiveness in teaching how to optimize queries and reduce billing."
"The primary takeaway for me was the clear connection between partitioning and tangible cost savings. Very valuable."
Instructions are praised for their clarity, making complex topics easy to grasp.
"The instructions were easy to follow, and I appreciated the focus on practical benefits like cost reduction."
"Excellent self-paced lab. It guides you through creating and querying partitioned tables step-by-step. The instructions are precise..."
"I found the explanations clear and the overall flow of the lab very intuitive, even for new BigQuery users."
Delivers hands-on skills directly applicable to real-world BigQuery scenarios.
"This lab was incredibly helpful for understanding date partitioning. The hands-on exercises were clear and directly applicable."
"I immediately saw how to apply this to reduce query costs in my projects."
"I learned practical skills that were immediately useful for managing my datasets efficiently."
Occasional reports of slow lab environment provisioning, though recent reviews suggest improvement.
"The lab environment was sometimes a bit slow to provision, which was frustrating."
"Found the instructions a bit confusing initially, but it improved later. The lab environment had some issues for me."
"A few older reviews mentioned setup issues, but recent feedback suggests the lab environment has become more stable and responsive."
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 Creating Date-Partitioned Tables in BigQuery with these
activities:
Review SQL basics
Show steps
Improve understanding of the fundamental concepts of SQL, which will facilitate comprehension of how to query partitioned datasets.
Browse courses on
SQL
Show steps
-
Review SQL syntax and concepts
-
Practice writing SQL queries
Query partitioned datasets
Show steps
Enhance proficiency in querying partitioned datasets, which will enable efficient data retrieval and improved performance.
Show steps
-
Create a partitioned dataset
-
Write queries to retrieve data from the partitioned dataset by partition
-
Optimize queries to improve performance
Collection of resources on data partitioning
Show steps
Consolidate knowledge by compiling various resources on data partitioning, providing a comprehensive reference point for future use.
Browse courses on
Data Partitioning
Show steps
-
Gather relevant articles, tutorials, and documentation on data partitioning
-
Organize the resources into a cohesive collection
Two other activities
Expand to see all activities and additional details
Show all five activities
Design and implement a data partitioning strategy
Show steps
Apply the learned principles to create a data partitioning strategy for a specific dataset, promoting better organization and performance.
Browse courses on
Data Partitioning
Show steps
-
Analyze the dataset and identify potential partitioning keys
-
Design a partitioning strategy based on the identified keys
-
Implement the partitioning strategy in BigQuery
-
Evaluate the effectiveness of the partitioning strategy
Assist fellow learners in understanding partitioning concepts
Show steps
Enhance comprehension and solidify knowledge by sharing insights with others, fostering a collaborative learning environment.
Show steps
-
Identify opportunities to help fellow learners with partitioning concepts
-
Provide clear explanations and examples
Review SQL basics
Show steps
Improve understanding of the fundamental concepts of SQL, which will facilitate comprehension of how to query partitioned datasets.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review SQL syntax and concepts
- Practice writing SQL queries
Query partitioned datasets
Show steps
Enhance proficiency in querying partitioned datasets, which will enable efficient data retrieval and improved performance.
Show steps
Show steps
Show steps
- Create a partitioned dataset
- Write queries to retrieve data from the partitioned dataset by partition
- Optimize queries to improve performance
Collection of resources on data partitioning
Show steps
Consolidate knowledge by compiling various resources on data partitioning, providing a comprehensive reference point for future use.
Browse courses on
Data Partitioning
Show steps
Show steps
Show steps
- Gather relevant articles, tutorials, and documentation on data partitioning
- Organize the resources into a cohesive collection
Two other activities
Expand to see all activities and additional details
Show all five activities
Design and implement a data partitioning strategy
Show steps
Apply the learned principles to create a data partitioning strategy for a specific dataset, promoting better organization and performance.
Browse courses on
Data Partitioning
Show steps
Show steps
Show steps
- Analyze the dataset and identify potential partitioning keys
- Design a partitioning strategy based on the identified keys
- Implement the partitioning strategy in BigQuery
- Evaluate the effectiveness of the partitioning strategy
Assist fellow learners in understanding partitioning concepts
Show steps
Enhance comprehension and solidify knowledge by sharing insights with others, fostering a collaborative learning environment.
Show steps
Show steps
Show steps
- Identify opportunities to help fellow learners with partitioning concepts
- Provide clear explanations and examples
Career center
Learners who complete Creating Date-Partitioned Tables in BigQuery will develop knowledge and skills
that may be useful to these careers:
Data Architect
Data Architect
Data Architects can use partitioned tables to design and implement scalable data solutions.
Data Engineer
Data Engineer
Data Engineers can use partitioned data to simplify maintenance and improve query performance.
Database Designer
Database Designer
Database Designers can use partitioned tables to design and implement scalable and efficient databases.
Cloud Architect
Cloud Architect
Cloud Architects can use partitioned tables to design and implement scalable data solutions in the cloud.
Database Administrator
Database Administrator
Database Administrators can use partitioned tables to improve query performance and scalability.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists can use partitioned tables to improve data governance and compliance.
Data Analyst
Data Analyst
Data Analysts working with big data can use their ability to partition data to save time and money when processing data.
Data Quality Analyst
Data Quality Analyst
Data Quality Analysts can use partitioned tables to improve the quality of data and reduce data errors.
Information Security Analyst
Information Security Analyst
Information Security Analysts can use partitioned tables to improve the security of data.
Data Scientist
Data Scientist
Data Scientists and those working as Machine Learning Engineers can partition data to overcome challenges in the data science lifecycle.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists can use partitioned tables to improve the performance of data visualizations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts can use partitioned tables to improve the performance of quantitative models.
Statistician
Statistician
Statisticians can use partitioned tables to improve the performance of statistical analyses.
Software Engineer
Software Engineer
Software Engineers can use partitioned tables to improve the performance of data-intensive applications.
Business Analyst
Business Analyst
Business Analysts can use partitioned tables to gain insights from data more quickly and efficiently.
For more career information including salaries, visit:
OpenCourser.com/course/4d5b89/creating
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Creating Date-Partitioned Tables in BigQuery.
The book provides in-depth knowledge of SQL performance optimization, which can benefit BigQuery users.
The book covers Apache Hadoop, which is used by BigQuery in its data processing engine.
The book can help beginners understand SQL, the language used to query BigQuery data.
The book covers using R for big data analytics, which can be used with BigQuery's external data sources.
The book covers using Python for data analysis, which can be used with BigQuery's BigQuery API.
Provides a beginner's guide to BigQuery. It covers the basics of BigQuery, including data ingestion, querying, and analysis. It valuable resource for anyone who wants to learn more about BigQuery and how to use it effectively.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4d5b89/creating
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