Data Warehousing Made Easy: Leveraging Google BigQuery for Seamless Analytics
Published: 2023-09-04 04:00:12

---
title: Data Warehousing Made Easy: Leveraging Google BigQuery for Seamless Analytics
published: true
date: 2023-09-03 22:41:59 UTC
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canonical_url: https://dataroots.io/research/contributions/leveraging-google-bigquery-for-data-warehousing
---

In our previous blogs about Data Engineering on Google Cloud Platforms (GCP), we touched upon how to choose the right database for your data projects. In this blog, we will look at Google BigQuery in detail. We will cover the features of BigQuery, best practices and performance optimisation techniques.

Google BigQuery is a fully managed, petabyte-scale analytics data warehouse that enables businesses to analyse all their data very quickly. It is a cloud-based service that offers a number of benefits over traditional on-premises data warehouses, including:

- **Scalability:** BigQuery can scale to handle any size dataset, making it ideal for businesses that are growing rapidly.
- **Performance** : BigQuery is very fast, even for complex queries.
- **Cost-effectiveness:** BigQuery is a pay-as-you-go service, so you only pay for the data you store and the queries you run.
- **Ease of use:** BigQuery is easy to use, even for non-technical users.

### Features of Google BigQuery

![](https://dataroots.ghost.io/content/images/2023/08/image-5.png)

Google BigQuery offers a number of features that make it a powerful data warehouse solution. These features include:

- **Massive scalability:** BigQuery can store and process petabytes of data.
- **Fast performance:** BigQuery can run complex queries very quickly.
- **Flexible pricing:** BigQuery is a pay-as-you-go service, so you only pay for the data you store and the queries you run.
- **Easy integration:** BigQuery can be integrated with a variety of other Google Cloud Platform services.
- **Strong security:** BigQuery uses a variety of security features to protect your data.
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| Feature / Detail | Description |
| --- | --- |
| Purpose | Fully managed, serverless data warehouse for analytics. |
| Data Storage | Columnar storage with Capacitor format. |
| Query Language | SQL-like language for querying data. |
| Scalability | Highly scalable, can process petabytes of data. |
| Performance | Super-fast queries with distributed architecture. |
| Automatic Indexing | No need to manage indexes; automatic optimization. |
| Data Import | Batch and streaming data ingestion supported. |
| Data Export | Can export data to other storage solutions. |
| Security | Multi-layered security with IAM and encryption. |
| Data Encryption | Data at rest and in transit encryption by default. |
| Cost | Pay-as-you-go pricing based on data processed. |
| Data Formats | Supports various data formats like CSV, JSON, AVRO. |
| Data Partitioning | Tables can be partitioned for better performance. |
| Data Clustering | Clustering helps optimize storage and query efficiency. |
| Data Retention Policy | Customizable data retention periods for tables. |
| Data Transfer Service | Move data from other sources into BigQuery easily. |
| Machine Learning | Integration with BigQuery ML for ML models on data. |
| Data Analysis | Supports Data Studio, Sheets, and other BI tools. |
| Data Governance | Data access controls and audit logs for compliance. |
| Streaming Inserts | Real-time data insertion for immediate analysis. |
| Data Export | Can export query results to various file formats. |
| Geographic Location | Data can be stored and processed in specific regions. |
| Data Resilience | Replication and backups to ensure data durability. |
| API Access | API for programmatic access and integration. |
| Data Cost Controls | Provides cost controls to manage expenses. |

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### Best Practices for Using Google BigQuery

There are a number of best practices that you can follow to get the most out of Google BigQuery. These best practices include:

- **Use the right data types:** When loading data into BigQuery, use the correct data types for your data. This will help to improve performance and reduce costs.
- **Use partitioning and clustering:** Partitioning and clustering can help to improve the performance of queries on large datasets.
- **Use materialised views:** Materialized views can be used to pre-compute queries, which can improve performance.
- **Use BigQuery ML:** BigQuery ML can be used to build machine learning models on top of your data.

### Performance Optimisation Techniques for Google BigQuery

There are a number of performance optimisation techniques that you can use to improve the performance of your queries in BigQuery. These techniques include:

- **Use the right index:** Using the right index can improve the performance of queries that access specific columns or rows.
- **Use the right query language:** Using the correct query language can help to improve the performance of your queries.
- **Optimise your queries:** Optimising your queries can help to improve their performance.
- **Use BigQuery's built-in tools:** BigQuery offers a number of built-in tools that can help you to optimise your queries.

Below is a comprehensive table outlining various performance optimisation techniques for Google BigQuery:

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| Performance Optimization Technique | Description |
| --- | --- |
| 1. Data Partitioning | Partitioning tables based on one or more columns can significantly improve query performance. By dividing data into smaller, manageable partitions, BigQuery can scan only the relevant partitions during query execution, reducing the amount of data processed. Date-based, timestamp-based, or integer range partitioning are commonly used strategies. |
| 2. Data Clustering | Clustering can further enhance query performance by physically organizing data within partitions based on specific columns. This reduces the amount of data read during query execution, as similar data is stored together. Choosing appropriate clustering columns can improve performance for both partitioned and non-partitioned tables. |
| 3. Query Optimization | Writing efficient queries is crucial for performance. Avoid using unnecessary wildcards, optimize joins, and use explicit column lists instead of SELECT \* to reduce data transfer and processing overhead. Understanding query execution plans using EXPLAIN syntax can help identify bottlenecks and optimize the query execution process. |
| 4. Denormalization | For read-heavy workloads, denormalizing data and storing pre-aggregated results can speed up queries. While this increases storage requirements, it reduces the need for complex joins and aggregation during query execution. Careful consideration should be given to the trade-off between storage costs and query performance. |
| 5. Partition and Cluster Size | When creating tables, consider the appropriate partition and cluster sizes based on data distribution and query patterns. Smaller partitions allow more parallelism but increase metadata overhead. Larger cluster sizes may improve performance for certain types of queries, especially those that benefit from full column scans. Regularly monitor and adjust these parameters. |
| 6. Table Design | Optimize table schemas based on query patterns. Avoid using nested and repeated fields in tables unless necessary. Flattening data and using nested or repeated fields only when needed can improve performance. Additionally, use the appropriate data types to minimize storage usage and reduce processing overhead during query execution. |
| 7. Cache and Materialized Views | Leverage caching mechanisms and materialized views for frequently accessed queries. BigQuery provides automatic query result caching, which can speed up repeated queries. Materialized views store precomputed results for specific queries, reducing the need to recompute data on-the-fly, further improving performance for specific use cases. |
| 8. Streaming and Batch Loading | Choose the right data loading method based on your requirements. Streaming data is ideal for real-time data ingestion, while batch loading is more efficient for large-scale data uploads. For batch loading, consider using BigQuery Data Transfer Service or data migration tools for optimal performance. |
| 9. Query Scheduling | Schedule resource-intensive or recurring queries during off-peak hours to avoid contention with other workloads. BigQuery allows you to set up reservation slots to ensure dedicated resources for critical workloads, improving performance consistency. |
| 10. Monitoring and Logging | Regularly monitor query performance using BigQuery's built-in monitoring and logging tools. Keep track of long-running queries, query delays, and other performance metrics. Use Stackdriver or any other monitoring tools to set up alerts and notifications to proactively identify and address performance issues. |
| 11. Use BigQuery ML for Predictions | For machine learning tasks, consider using BigQuery ML, which allows you to build and execute machine learning models directly within BigQuery. By leveraging the power of BigQuery's distributed architecture, you can perform predictions and analysis on large datasets with ease, reducing data movement and improving overall performance. |
| 12. Use BI Tools and Data Studio | Utilize BI tools like Data Studio for visualization and data exploration. These tools can optimize data retrieval and display, reducing the need for complex queries and allowing users to interact with data in a more intuitive way, thereby enhancing overall performance. |

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Performance optimisation is an iterative process. Each optimisation technique's effectiveness will depend on your specific data, workload, and business requirements. It is crucial to always consider the trade-offs between performance improvements and associated costs or complexities.

### Technical Details

In addition to the best practices outlined above, there are a number of technical details that data engineers and ML engineers should be aware of when using Google BigQuery. These details include:

- **The BigQuery query language:** The BigQuery query language is a SQL-like language that is used to run queries in BigQuery. The BigQuery query language supports a number of features that can help to improve the performance of your queries, such as partitioning and clustering.
- **The BigQuery storage format:** BigQuery uses a proprietary storage format that is optimised for performance. The BigQuery storage format is designed to store data in a way that minimises the amount of disk space that is used and maximises the speed at which queries can be run.
- **The BigQuery execution engine:** BigQuery uses a distributed execution engine to run queries. The BigQuery execution engine is designed to scale to handle any size dataset and to run queries as quickly as possible.

### Guide to Getting Started 

![](https://dataroots.ghost.io/content/images/2023/08/image-4.png)

In this technical guide, we'll walk you through the process of setting up BigQuery and performing basic data warehousing tasks. Let's dive in!

**Step 1:** Creating a Google Cloud Project and Enabling BigQuery API

1. Sign in to your Google account and navigate to the Google Cloud Console ([https://console.cloud.google.com/](https://console.cloud.google.com/?ref=dataroots.ghost.io)).
2. Create a new project or select an existing one.
3. In the Google Cloud Console, click on the "Navigation menu" > "BigQuery."
4. If the BigQuery API is not enabled for your project, click on the "Enable" button to enable it.

**Step 2:** Setting up the BigQuery Web UI

1. After enabling the BigQuery API, click on the "Go to BigQuery" button to open the BigQuery Web UI.
2. You'll see the Query Editor, where you can run SQL-like queries on your datasets.

**Step 3:** Creating a Dataset

1. In the BigQuery Web UI, click on your project name (top-left corner) to open the project dropdown.
2. Click on "Create Dataset."
3. Provide a Dataset ID (e.g., "my\_dataset") and choose the data location.
4. Click "Create Dataset" to create a new dataset.

**Step 4:** Uploading Data to BigQuery

1. In the BigQuery Web UI, select your dataset from the left-hand panel.
2. Click on "Create Table."
3. Choose "Upload" as the data source.
4. Select your data file (CSV, JSON, etc.) and configure the schema.
5. Click "Create Table" to upload the data.

**Step 5:** Running Queries in BigQuery

1. In the Query Editor, enter your SQL-like query.
2. For example, to retrieve all rows from a table named "my\_table," use `SELECT *FROM `project_id.dataset_id.my_table`;`
3. Click the "Run" button to execute the query.

**Step 6:** Advanced Queries with BigQuery

1. BigQuery supports complex queries for data transformations and aggregations.
2. For example, to calculate the total sales amount per product, use `SELECT product_name, SUM(sales_amount) AS total_sales FROM `project_id.dataset_id.sales_table` GROUP BY product_name;`

**Step 7:** Managing BigQuery Resources

1. In the BigQuery Web UI, click on "Query History" to view your previous queries.
2. Click on "Job History" to monitor the status of running or completed jobs.

### Conclusion

You have successfully set up Google BigQuery and performed basic data warehousing tasks! With BigQuery's powerful querying capabilities and scalability, you can now explore and analyze large datasets effortlessly. Start leveraging the potential of Google BigQuery to unlock valuable insights from your data!

By following the best practices and performance optimisation techniques outlined in this blog post, you can get the most out of BigQuery and improve the performance of your queries.

We hope you found this blog post informative. Keep an eye out for our blogs on data engineering on GCP!