Understanding BI BigQuery Engine: A Comprehensive Guide

Arsalan Mohammed • Last Modified: December 29th, 2022


Google Bigquery is one of the most recognized Data warehouse solutions in the market currently that is trusted by many. It offers a wide range of functionality for storing data from various different sources and provides efficiency while retrieving them.

BI Bigquery engine is an analytics service provided by Bigquery that is fast and works flawlessly on data stored in bigquery. This article provides a comprehensive guide of BI Engine.

Table of Contents:

What is Google BigQuery?

bi bigquery: google bigquery
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Google BigQuery was designed as a flexible, fast, and powerful data warehouse that is tightly integrated with other services on the Google Platform. It offers user-based pricing, is cost-effective, and has a serverless model. Google BigQuery’s analytics and data warehousing platform leverages an integrated query engine in addition to a serverless model to enable terabytes of data to be processed in seconds. We also wrote one in-depth article about Google BigQuery architecture.

Google BigQuery allows you to perform data analytics with a lower total cost of ownership, which is about 26% to 34% lower than other alternatives. With no infrastructure to manage or configure, you can focus on gaining meaningful insights using standard SQL. Bigquery offers a flexible pricing model ranging from flat-rate to on-demand options.

Google BigQuery’s column-based storage service has given momentum to the speed of data warehouses and their ability to handle large amounts of data. Relevant columns can only be processed in column-based storage, resulting in faster responses and more efficient use of resources. Therefore, it is advantageous to store the data in columns in the analytic database.

bi bigquery: BigQuery Architecture
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Key Features of Google BigQuery

Here are a few key features of Google BigQuery:

  • Serverless Services: In general, organizations in a data warehousing environment need to determine and specify the server hardware on which the calculations are performed. Second, administrators need to ensure performance, reliability, resilience, and security. The serverless model helps overcome this limitation. In the serverless model, the process is automatically distributed across many machines running in parallel. By using the Google BigQuery serverless model, database administrators and data engineers focus on server provisioning rather than infrastructure. This gives you more valuable insights from your data.
  • SQL and programming language support: Users can access Google BigQuery using standard SQL. In addition, Google BigQuery has client libraries for creating applications that access data using Python, C #, Java, PHP, Node.js, Ruby, and Go.
  • Tree Architecture: Google BigQuery and Dremel can easily be extended to thousands of computers by structuring calculations as an execution tree. The root server receives incoming requests and forwards them to a branch, the so-called mixer. These branches then modify the incoming request and deliver it to the leaf node, also known as the slot. The leaf node then handles the filtering and reading of the data while operating in parallel. The results are returned to the bottom of the tree, the mixer accumulates the results, and finally responds to the query and sends it to the root.
  • Multiple data types: Google BigQuery supports a variety of data types, including strings, numbers, Boolean values, structs, and arrays.
  • Security: Google BigQuery data is automatically encrypted during transfer or storage. Google BigQuery can also isolate jobs and manage the security of multi-tenant activity. Google BigQuery integrates with the security features of other GCP products to give enterprises a complete picture of data security. Users can also share datasets with Google Cloud Identity and Access Management (IAM). Administrators can set permissions for individuals and groups to access tables, views, and records.

Learn more about Google BigQuery.

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What is BI BigQuery Engine?

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If you require a fast in-memory analytics service BI BigQuery Engine is a very good choice.  BI Engine allows you to analyze the data stored in BigQuery. Query response time is less than 1 second, allowing for a high degree of parallelism.

BI Engine can be integrated with well-known Google tools such as Google Data Studio.  BI Engine allows its SQL interfacing capabilities to be integrated with other BI tools such as Power BI, Tableau, Looker, and other applications to accelerate data exploration. other custom applications to accelerate data exploration and data analytics. 

BI Engine allows you to create dashboards that are rich and interactive in nature while maintaining efficiency, scalability, and staying up-to-date.

BI BigQuery Engine Architecture

architecture for BI Engine is shown in the following diagram:

bi bigquery: BI Engine architecture
Image Source: googlecloudimage

BI Engine supports the following:

  1. BigQuery API: The BI engine integrates directly with the BigQuery API.  All the BI solutions, applications and ODBC drivers that work on the BigQuery API using standard mechanisms such as REST and JDBC can use the BI engine without modification.
  2. Vectorized Runtime: With the BI Engine SQL interface, BI Engine introduces a more up-to-date technology called vectorized processing. Vectorized processing in the execution engine allows you to use the latest CPU architecture more efficiently by processing a stack of data at the same time. BI Engine incorporated the use of advanced data encoding technologies like dictionary run-length encoding which compresses the data that is stored.
  3. Metadata: Metadata stores table and view definitions,  schema definitions, and detailed permissions.  
  4. Reservations: BI engine reservations manage memory allocation at the project’s accounting level. BI Engine only stores queries or scanned columns and partitions. The entire table is not cached.
  5. Distributed in-memory engine: The BI engine is a distributed in-memory execution engine that allows customers to allocate very large memory reservations.

Advantages Of BI BigQuery Engine

BI Engine has the following advantages:

  • Fast: Match performance with business speed by reducing time to insight Today, it is difficult to generate reports fast enough to manage your business with operational normative business intelligence. The team is also struggling with the sluggish dashboard and outdated data. BI Engine minimizes load times for data stored in BigQuery, improves parallelism, and provides query response times in seconds. By integrating BI Engine with BigQuery Streaming, you can perform real-time data analysis on streaming data without sacrificing write speed or data freshness.  
  • Simplified architecture: Get started quickly without having to manage complex data pipelines or servers. Traditional solutions for BI require you to transfer the data from the data warehouse where the data is stored to a BI platform so as to support interactive analysis quickly. This usually requires a complex ETL pipeline for data movement. The time it takes to complete these ETL jobs can delay reporting and keep data in critical decision support systems fresh. BI Engine performs in-place analysis in BigQuery. This allows the users to skip the movement of data or create data transformation pipelines that are complex in nature.  
  • Smart tuning:  BI engine provides configuration settings. Also, BI Engine provides a self-optimization design that tries to optimize queries automatically by transferring data between in-memory storage provided in the BI engine, the data in the cache of BigQuery query, and the main storage of BigQuery. This optimizes the performance of the dashboard and reduces load times. Cloud console provides the BigQuery administrators with easy tools that can be used to add and remove BI engine storage capacity.

Limitations of BI BigQuery Engine

  • BI Engine does not fully optimize some types of queries in BigQuery views. If the query is not optimized, Data Studio will show you the detailed reason.
  • Each BI Engine project per location can cache up to 100GB in the main memory.
  • 500 partitions per table are supported by BI Engine for Data Studio.
  • BI Engine for Data Studio supports up to 150 million rows of query data, depending on the complexity of the query.
  • For limitations that other BI tools face, see BigQuery BI Engine SQL interface overview on the official page.
  • A complete list of optimized functions in custom queries can be viewed by seeing Optimized SQL functions and operators.

Supported Regions of BI BigQuery Engine

BI BigQuery Engine is supported in all the regions that support BigQuery. 

Pricing of BI BigQuery Engine

If you’re using BI BigQuery Engine, pricing is based on the BI Engine capacity you purchased for your project. BI BigQuery engine can be purchased in two ways

  •  Purchasing capacity on demand
  • Pooling BI Engine capacity when signing up for BigQuery flat rates

BI BigQuery Engine stores BigQuery metadata and tabular data in memory. The amount of data stored is limited by the capacity purchased. There is no charge for reading the data If the query you ran retrieves results from the data stored in BI BigQuery Engine.

IF a query result exceeds the size of BI BigQuery Engine while executing a query, the Self-tuning feature of BI BigQuery Engine utilizes a bigquery slot to execute the query. When you run a query using the BigQuery slot, you will be charged BigQuery’s on-demand query fee for your query job. When you run a query using a slot, all BigQuery assignments and limits apply to your query job.

BI BigQuery Engine Free Tier provides Google Data Studio users with up to 1GB of free space. All the Data Studio users can use this free tier without registration. This free space is for testing purposes only and should not be used to run production workloads. There is no SLO guarantee for this free tier.

$0.0416 per GB is the flat-rate pricing is that can be used for high-volume customers who prefer a stable, monthly cost.

Query Optimization and Acceleration in BI BigQuery Engine

BigQuery and, as an extension, BI BigQuery Engine splits the query plan created for  SQL queries into subqueries. Subqueries include several operations, such as Data scanning, filtering, or aggregation. Often the shard’s execution unit. BI BigQuery Engine executes All SQL queries that are supported by BigQuery, but optimization only happens on certain subqueries.

In particular, BI BigQuery Engine’s SQL interface is optimized for leaf-level subqueries that scan data from memory and perform operations such as filtering, calculation, aggregation, sorting, and certain types of joins. Other subqueries that BI BigQuery Engine hasn’t yet fully accelerated will return.


This article gives a comprehensive guide on the BI Bigquery engine in detail.

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