Data Engineer Zoomcamp Week 3: BigQuery Breakdown
Unlocking the Potential of Fast, Cost-Efficient Analytics for Data Engineering Success.
Hello Data Enthusiasts!
Hope this newsletter finds you all in good spirits! Here is a wrap-up to another fun and exciting week in our Data Engineer journey. Week 3 was all about a captivating exploration of Google BigQuery.
BigQuery Demystified: A Personal Perspective
BigQuery, a well-known technology in data processing, was the main topic this week. Before we start I want to briefly explain about BigQuery.
What is BigQuery?
BigQuery is a cloud-based, serverless data warehouse by Google Cloud, designed for fast and cost-effective analysis of large datasets using SQL.
What are The Benefits?
Performance: High-speed querying with parallelisation and columnar storage.
Cost-effective: Pay-as-you-go pricing, no need for dedicated infrastructure.
Integration: Seamless integration with other Google Cloud services.
Security: Robust security features, including encryption and access controls.
Important Things to Know:
Billing Model: Understand pricing for storage and queries.
Data Formats: Support for various data formats impacts query performance.
Partitioning and Clustering: Improve performance with table partitioning and clustering.
Data Transfer: Efficiently load data using tools like bq or Dataflow.
Access Control: Use IAM to manage access to BigQuery resources.
OLAP vs OLTP, Data Warehouses, and the BigQueryÂ
We kicked off the week by understanding the differences between OLAP vs OLTP and understanding the crucial role of data warehouses in modern analytics. The spotlight then shifted to BigQuery. I found it fascinating to explore its capabilities and the big role it plays in our data engineering toolkit.
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Cost, Partitions, and Clustering: The Nuts and Bolts
Cost management came into focus as we looked into optimising operations within BigQuery. Storing our dataset in a Parquet format within a bucket, we witnessed firsthand the impact of choosing between external and standard tables on performance and flexibility.
Using partitioning in BigQuery offers several advantages, including improved query performance by allowing the system to skip unnecessary partitions when processing queries. It's particularly beneficial when dealing with large datasets as it helps narrow down the data that needs to be scanned for a given query, resulting in faster and more cost-effective queries.
BigQuery offers three types of partitions:
1. Partition by integer ranges:
Partitions are created based on the numeric value of a column of type INTEGER.
For example, you can partition a table based on country codes or any other numeric range.
2. Column partition per unit of time:
This is a common partitioning strategy where the table is partitioned based on a column of type DATE, TIMESTAMP or DATETIME.
For example, you can partition a table based on the date of events or the timestamp when the data was inserted.
3. Partition by ingestion time:
BigQuery automatically assigns rows to partitions based on when the data is ingested into BigQuery.
You can choose the level of detail for partitions, such as by hour, day, month or year.
It has a limit of 4,000 partitions.
A special column named _PARTITIONTIME is added, and it contains the timestamp when the data was stored.
Using clustering in combination with partitioning can provide further optimisation for query performance, as both features work together to reduce the amount of data that needs to be scanned during query execution. It's important to choose clustering columns based on the query patterns and the nature of your data to achieve the best performance benefits.
1. Order of Grouped Columns:
The order of the columns used for clustering is indeed relevant. It determines the priority of the columns in the clustering key.
The order is essential when optimising query performance, as it influences how the data is physically organised within the storage.
2. Performance Improvement:
Clustering can significantly improve the performance of queries that use predicates or aggregation functions.
By organising data based on the clustering key, BigQuery can skip over unnecessary data during query execution, reducing the amount of data that needs to be scanned.
3. High Cardinality Columns:
Clustering works well with columns that have high cardinality, meaning columns with many distinct values.
Examples of high cardinality columns include email addresses, categories and names.
4. Supported Column Types:
Clustering can be applied to columns of various types, including DATE, BOOL, GEOGRAPHY, INT64, NUMERIC, BIGNUMERIC, STRING, TIMESTAMP and DATETIME.
5. Maximum Number of Clustered Columns:
There is a limit of a maximum of 4 clustered columns per table. This means you can choose up to four columns to define the clustering key for a table.
Understanding the nuts and bolts of how this powerful engine works was like discovering the hidden magic behind the scenes. Additionally, the introduction of Machine Learning in BigQuery added a whole new layer of excitement.
Performance Testing: A Hands-On Experience
Putting theory into action, we experimented with storing datasets, creating external and standard tables, and optimising queries based on performance metrics. The real-time comparison of their performances provided invaluable insights into making informed decisions.
Conclusion
I'm excited to use what we've learned to solve real-world data problems. Having BigQuery skills in our toolkit opens up a lot of possibilities.
Next week is going to be interesting as we get to learn all about Analytics Engineering and dbt.
So, Data Engineers keep coding and keep exploring.