Bigquery is a widely used data warehouse that is able to process and store raw data efficiently. It provides various function to help in performing the tasks efficiently. Windows functions are used to process the data and perform analytical tasks on the same.
This article gives a guide on BigQuery Dense_rank and rank functions in detail.
Table of Contents
What is BigQuery?
Google BigQuery is the product offered by Google Cloud Platform, which is serverless, cost-effective, highly scalable data warehouse capabilities along with built-in Machine Learning features. Google Bigquery supports ANSI SQL, allowing users to execute SQL queries on massive datasets to manage business transactions, perform data analytics, and many more.
BigQuery is getting popular nowadays, and many companies like Twitter use BigQuery to forecast the exact volume of packages for its various offerings.
BigQuery also supports Streaming data sources along with batch data.
Key Features of BigQuery:
- Scalable Architecture: BigQuery has a scalable architecture and offers a petabyte scalable system that users can scale up and down as per load.
- Faster Processing: Being a scalable architecture, BigQuery executes petabytes of data within the stipulated time and is more rapid than many conventional systems. BigQuery allows users to run analysis over millions of rows without worrying about scalability.
- Fully Managed: BigQuery is a product of Google Cloud Platform, and thus it offers fully managed and serverless systems.
- Security: BigQuery has the utmost security level that protects the data at rest and in flight.
- Real-time data ingestion: BigQuery can perform real-time data analysis, thereby making it famous across all the IoT and Transaction platforms.
- Fault Tolerance: BigQuery offers replication that replicates data across multiple zones or regions. It ensures consistent data availability when the region/zones go down.
Learn more about BigQuery.
Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 150+ data sources (including 30+ free data sources) like Asana and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
GET STARTED WITH HEVO FOR FREE[/hevoButton]
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
Check out why Hevo is the Best:
SIGN UP HERE FOR A 14-DAY FREE TRIAL
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
What is Window Function?
Window functions are analytical functions that enable users to create and execute analytical queries efficiently. The window function operates on a partition or window of defined limit and returns the result for each window. For example, if the user wants to sum the salary of all the employees based on department, the window function will first all the rows for the same department and then perform the sum against the department.
The aggregations are calculated based on the rows present in that particular window, and it is based on three main concepts –
- Partition clause that defines the partition value to create a window
- Over clause to perform ordering of the rows within each window/partition.
- Window frames are defined relative to each row to further restrict the rows’ set.
What is the Dense_Rank function?
The Bigquery Dense_Rank function is similar to other window functions except that it doesn’t skip any ranking number if there is a tie in the preceding rankings. For example – if there are two records with the Dense_Rank assigned as 1, then the next increment for the third record will start from 2 (Not ‘3’, as with the Rank function).
How to use the Google BigQuery DENSE_RANK Function?
The implementation of Bigquery Dense_Rank in Google BigQuery is simple as it has an implementation in SQL.
Consider the following table in BigQuery –
The syntax for the Bigquery Dense_Rank function is
DENSE_RANK () OVER
PARTITION BY <col name>
ORDER BY <col name>
Consider the above Orders table. Let us try to implement the Bigquery Dense_Rank function in the Orders table and see the outcome –
The above table shows ties between two rows, the next counter value assigned to the third row 2.
BigQuery DENSE_Rank vs RANK Function: What is the Difference and similarities?
The differences and similarities between BigQuery Dense_Rank and Rank functions are –
- BigQuery Dense_rank and Rank are part of the window function, and both provide ranking to the rows based on the input parameters that define the window of implementation.
- BigQuery Dense_Rank doesn’t skip the rows when there are ties in between rows; however, in the case of the Rank function, it will skip the counter value assigns the next available counter. For example, in the above example, for the third row, the rank function will assign the number 3.
Let us understand the above two features with an example applied on the Orders table –
This blog post discussed what BigQuery Dense_Rank is and how to use it effectively with BigQuery. We have also discussed the similarities and Differences between Dense_Rank and Rank function and understood an example.
Snowflake is a trusted data warehouse that a lot of companies use and store data as it provides many benefits but transferring data into it is a hectic task. The Automated data pipeline helps in solving this issue and this is where Hevo comes into the picture. Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations that you can choose from.
visit our website to explore hevo[/hevoButton]
Hevo can help you Integrate your data from numerous sources and load them into a destination to Analyze real-time data with a BI tool such as Tableau. It will make your life easier and data migration hassle-free. It is user-friendly, reliable, and secure.
SIGN UP for a 14-day free trial and see the difference!
Share your experience of learning about BigQuery Dense_Rank in the comments section below.
No-code Data Pipeline For Your Data Warehouse