You've pretty much heard of Google BigQuery if you're out looking for a data warehousing solution for your business. 

But let's start with the basics. What is BigQuery?

Google BigQuery is a cloud-based data warehousing solution known for its quick processing time and large data volume capabilities. It can process complex queries involving large amounts of data in just a few minutes.

The enterprise data warehouse solution has several distinguishable properties:

• BigQuery is built using the Google Cloud Platform and has Dremel as its core.
• It is serverless and has no infrastructure handling issues.
• It is highly scalable and can handle a few megabytes to petabytes of data.
• The solution is also always available with minimal downtime.
• Integration of BigQuery with other Google services is easy and efficient.

Considering all these features, Google describes BigQuery as a “zero-infrastructure, flexible, and scalable enterprise data warehouse.” But it's not just another cloud data warehouse meant for data storage.

BigQuery is built to facilitate big data analysis. The platform can process terabytes of data in mere seconds without extensive planning or setup. All this without the need to install or configure any settings.

The users gain direct access to ingest data on to the platform. They are charged based on the amount of data stored or the queries processed. Add to it Google's cloud storage platform and other services, and BigQuery truly becomes a solution for big data pipelines.

You can still process smaller datasets through this platform. But there will be no significant difference as the processing times for such datasets are already small. On the other hand, organizations that require continual processing of large data volumes benefit the most from BigQuery.

Consider this.

A large e-commerce company has massive amounts of customer data in the form of purchase history, feedback, website clicks, etc. To analyze this and run SQL queries, the company needs to set up a data integration process that can handle terabytes or even petabytes of data. 

Choosing a traditional database will only make the process tedious with all the infrastructure setup involved.

But BigQuery is designed for exactly such requirements. The serverless, fast-processing data warehouse will handle all data needs while companies can focus on analytics and business intelligence. 

While BigQuery is relatively easy to implement, understanding how it functions can help you leverage its capabilities according to your requirements. This guide goes in-depth about BigQuery's architecture, its workflow, and the tools that work best with it.

The Architecture of Google Big Query

BigQuery and its data handling capabilities are widely used by today's data-driven firms. But its architecture was inspired by another lesser-known offering by Google: Dremel.

Dremel, a query engine, is a key technology in several Google products, including BigQuery. Unlike BigQuery, Dremel is mostly used internally by Google due to its highly technical features.

BigQuery, on the other hand, was designed as a full-fledged data processing platform that anyone can use, even without technical knowledge. Google has continued to add many features to the Dremel-inspired version, such as SQL query processing and Capacitor. 

How BigQuery handles large data sets

The architecture of Big Query Google is designed to process huge amounts of data in minimal time frames. This is possible through some prominent features in BigQuery's updated architecture:

• Computing: Dremel

The Dremel-inspired tree architecture is the key reason for BigQuery's fast processing times. The branched architecture allows the data processor to separate large data into smaller segments and act on them individually. 

When a query is introduced, it is introduced in the top node of BigQuery's architecture. The node has several branches that lead to “leaf nodes” or smaller processing units. This branched system extends to hundreds of levels till the query is sufficiently divided.

Then each leaf node processes its part of the query and returns the result to the top node. The simultaneous processing of small quantities of data allows BigQuery to process large Google datasets in just a few seconds. 

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• Storage: Capacitor and Colossus

This data organization and storage of BigQuery table are handled by Capacitor, which was introduced in 2016. Earlier, ColumnIO was used to optimize the column data format. However, it limited BigQuery's efficiency due to its inability to work on compressed data.

On the other hand, a capacitor can act directly on compressed data, improving its processing time. Colossus, the new-gen distributed file system by Google, further enhances storage efficiency. 

Colossus helps Big Query Google scale to the required data volumes without integrating any other data storage solution. It also keeps downtime to a minimum by handling data recovery and distribution. This means BigQuery logs keep running through data storage at multiple points even with a single-point failure.

• Network: Jupiter

Though the compute and storage layers are super-fast, they are also separated. BigQuery requires an equally efficient network to maintain communication between the compute and storage parts. This communication is handled by Jupiter.

Jupiter transfers petabytes of data from storage to compute in a few seconds to aid its quick processing capabilities. It also supports parallel dataset processing, which is the core of BigQuery's architecture. 

Jupiter's data processing speed equals one petabit/sec and bandwidth to accommodate thousands of machines. 

What is BigQuery's Data Storage Process?

BigQuery's data processing efficiency is also largely linked to its storage format. The platform stores data in the column format instead of the commonly used row-only format. Each data category stored in a row is segregated by columns to keep individual Google datasets separated.

When a query is introduced, the platform extracts only the required columns instead of extracting the entire row. This reduces data processing time leading to faster query resolution. 

The columnar data storage also has two other advantages:

• Data compression: The data in each column is grouped under similar factors, allowing for a high compression level. Row-only data storage formats typically achieve up to 3 times compression, whereas BigQuery data warehouse can compress data up to 10 times. 
• Parallel Processing: The separation of data allows the computation layers to divide and process large data groups into smaller parts. Row-based data formats don't allow for quick data navigation and aren't suitable for Dremel's tree architecture.

These factors assist BigQuery's other architectural components, such as Borg and Colossus, which are crucial for its query execution process.

How does the query get executed?

When a query is introduced into the BigQuery platform, it executes it in several steps:

• The query is introduced into the root server. This is the server responsible for both: the direction of query through the system and the returning of results to the client.
• The root server pushes the query into the next branches through Dremel. Each branch level or node rewrites the query to modify it and prepare it for parallel processing.
• The query sections reach thousands of leaf nodes where they are processed. The leaf nodes return the results to mixers integrating results from multiple nodes.
• The integrated results are returned to the root server, which returns the result to the client.

Borg, a cluster management system by Google, overlooks the entire process. Borg allows BigQuery to allocate slots for each query and monitors parallel processing. It also ensures the reliability and availability of BigQuery through its fault tolerance mechanism.

When to use BigQuery?

Though BigQuery handles all types and volumes of data, it makes more sense to use BigQuery in certain use cases, such as:

• Running complex queries: BigQuery can perform basic filtration and grouping. But it is designed to handle much more than that. You can use BigQuery to perform advanced queries, including big data analytics and machine learning. These queries usually take several seconds or even minutes to process and aren't suitable for traditional warehouses.

• Using stagnant data: The platform's inbuilt caching feature allows you to reproduce results from a previous query in seconds. If you periodically run the same queries without changing the data, then using BigQuery saves you considerable time. It also saves you money as Google doesn't charge for using cached queries. 

• Rapid scaling: If your data processing requirements are small but rapidly growing, then BigQuery is the right data warehousing choice. BigQuery helps you easily scale without getting additional infrastructure. The platform charges more as you increase data volume and the number of queries. 

• Processing real-time data: Real-time data processing requires immediate query results. When this data is huge and complex, say from IoT sensors, then the usual relational databases fail to keep up. For continual processing of such data, BigQuery is a better choice.

BigQuery suits almost any use case involving complex analysis and big data. But depending on the requirements, certain analytical tools can help you optimize the results.

What Analytic Tools Work With Google BigQuery?

Third-party analytical tools can simplify BigQuery processes like data loading, visualization, and processing. Here's a list of analytical tools you can use with BigQuery for optimal results.

1. SprinkleData

SprinkleData is a no-code data integration platform with real-time replication data ingestion and transformation capabilities. The platform is a complete solution for data migration from multiple pipelines into the BigQuery data warehouse. With connectors for over 100 data pipelines, you will have a seamless data transfer process at par with BigQuery's capabilities.

2. Tableau

Tableau is a data visualization platform compatible with BigQuery. It simplifies your BigQuery analytics and data processing with desktop visualizations, dashboards, and more. Its easy drag-and-drop feature lets you create and share intuitive insights with decision-makers directly through Google Big Query.

3. Microstrategy

A powerful data analytics solution, Microstrategy promises data experiences without performance limits. The platform teams up with BigQuery to create simple analytics solutions that can be easily accessed. It also enables you to create business intelligence queries and ensure data security without involving complex IT knowledge.

4. Power BI

Power BI is a data visualization tool developed by Microsoft. It seamlessly integrates with Google BigQuery to analyze data, create dashboards, and view reports. The platform also handles data preparation and lets you view reports on mobile devices.

Google BigQuery Pricing

Google uses a pay-as-you-use model to determine the pricing of BigQuery. You only need to pay for the resources used, not the infrastructure, setup, or maintenance costs. 

Two main factors determine the pricing of BigQuery for your requirements:

• Storage: Google charges a flat rate of $0.02 every month for 1 GB of active data storage. Additionally, Google also charges different prices depending on the type of data. The tech-giant charges $0.01 per GB per month after 90 days for long-term data storage capacities.

• Analysis: The charge for 1 TB of query processing is $5. While this is the on-demand pricing, Google also offers monthly query slot packages starting at $2000 per month for 100 slots.

You can calculate the total pricing for your business requirements by multiplying the base price with the required data storage and analysis capacity. As you pay on the go, BigQuery becomes one of the more economical choices among data processing platforms.

Getting Data Into BigQuery

While BigQuery has powerful data processing capabilities, clean data input is crucial for the platform. To fully leverage its capabilities, you need an equally powerful data-loading solution such as SprinkleData.

SprinkleData is an end-to-end data integration platform that extracts, transforms, and loads data into the target warehouse. The platform facilitates real-time data replication, automatic schema mapping, and live process monitoring, making it the ideal choice for loading data into your BigQuery warehouse. 

With SprinkleData, you can combine seamless data integration with powerful analytics of Bigquery to fuel your business intelligence decisions. 

Learn more about SprinkleData products by visiting our website.

Frequently Asked Questions

1. What is Google BigQuery? Google BigQuery is a cloud-based data warehousing solution known for its quick processing time and large data volume capabilities.

2. How does BigQuery differ from traditional databases? BigQuery is serverless, highly scalable, and designed for processing large datasets without extensive setup or infrastructure handling.

3. What are some key features of BigQuery? BigQuery is known for being zero-infrastructure, flexible, scalable, and always available with minimal downtime.

4. What types of queries can BigQuery handle? BigQuery can handle complex queries involving large amounts of data in just a few minutes, including big data analytics and machine learning.

5. How does BigQuery store and process data? BigQuery stores data in a columnar format and uses a tree architecture inspired by Google's Dremel, allowing for parallel processing and quick data retrieval.

6. What are some use cases for BigQuery? BigQuery is suitable for running complex queries, using stagnant data with its caching feature, rapid scaling, and processing real-time streaming data too, such as IoT sensor data.

7. What are some third-party tools compatible with BigQuery? Tools like SprinkleData, Tableau, Microstrategy, and Power BI can be integrated with BigQuery for data integration, visualization, and analysis.

8. How is BigQuery priced? BigQuery follows a pay-as-you-go model, charging for storage and analysis resources used without additional infrastructure or setup costs.

9. How can data be loaded into BigQuery? Data can be loaded into BigQuery using powerful data-loading solutions like SprinkleData, which facilitate real-time data replication and automatic schema mapping load data in.

10. What are the advantages of using BigQuery for data analysis? BigQuery offers fast processing times, scalability, integration with other Google services, and the ability to handle large volumes of data without extensive setup.

11. Can BigQuery handle geospatial data? Yes, BigQuery can analyze and visualize geospatial data efficiently, making it suitable for applications involving location-based analytics.

12. Does BigQuery offer data security features? Yes, BigQuery provides access management controls and ensures data security through Google's robust infrastructure and compliance certifications.

13. Can machine learning models be executed in BigQuery? Yes, BigQuery supports the execution of machine learning models, allowing for advanced analytics and predictive modeling directly within the platform.

14. How does BigQuery handle query execution? BigQuery executes queries in several steps, distributing them across multiple nodes for parallel processing and utilizing technologies like Dremel and Borg for efficient execution.

15. Is BigQuery suitable for businesses of all sizes? Yes, BigQuery is designed to accommodate all the data needs of businesses ranging from startups to large enterprises, offering flexibility, scalability, and cost-effectiveness in data processing and analysis.

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