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Creating Date-Partitioned Tables in BigQuery
Google Cloud Training
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/4d5b89/creating
What's inside
Syllabus
Creating Date-Partitioned Tables in BigQuery
Good to know
Good to know
Know what's good ,
what to watch for , 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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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.
Cloud Architect
Cloud Architect
Cloud Architects can use partitioned tables to design and implement scalable data solutions in the cloud.
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.
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 Quality Analyst
Data Quality Analyst
Data Quality Analysts can use partitioned tables to improve the quality of data and reduce data errors.
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.
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.
Software Engineer
Software Engineer
Software Engineers can use partitioned tables to improve the performance of data-intensive applications.
Statistician
Statistician
Statisticians can use partitioned tables to improve the performance of statistical analyses.
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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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
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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