Advanced Google BigQuery Random Sampling Simplified 101
In the Data Management and Analysis Industry, many new platforms and applications have appeared in recent years. At present, most businesses turn to Cloud-based solutions to collect, store and interact with an extensive amount of data. One famous Cloud-based Data Warehouse solution is Google BigQuery.
Table of Contents
You can query random chunks of data from huge Google BigQuery tables using BigQuery Random Sampling. Sampling returns a diverse set of records without incurring the costs of scanning and processing a complete table.
Google BigQuery is a popular Cloud-based Data Warehouse solution that offers scalability, simplicity, and great abstraction for its users. Unlike the competitors, BigQuery is a serverless platform, and it provides services dynamically, so it does not require users to provide hardware or hardware management. SQL, a specialized query language designed specifically for the Database Management System, is the base for querying on BigQuery Random Sampling
This article is a comprehensive guide on BigQuery Random Sampling. You will learn about Random Sampling, its benefits, and various ways to perform BigQuery Random Sampling.
Table of Contents
- Introduction to Google BigQuery
- Understanding Random Sampling
- Understanding Google BigQuery SQL
- Key Advantages of Table Sampling
- Selecting a Randomly Distributed Sample from a BigQuery Table
Introduction to Google BigQuery
Google BigQuery is a Cloud-based Data Warehouse, offering fast SQL queries and interactive analysis of massive datasets. BigQuery is designed on Google Dremel technology and can process read-only data effectively. The Google BigQuery platform uses a column storage paradigm that allows a much faster data scan and the tree architecture model, making the query and aggregate results significantly more accessible and efficient. In addition, BigQuery is a serverless platform, and it is built to be very scalable thanks to the fast deployment cycle and on-demand prices.
Here are a few pricing models of Google BigQuery:
1) On-Demand Analysis
This pricing model offers free 1TB analysis per month, after which users are billed based on the use (byte processed per query). The rate after free analysis is $5.00 per TB. Users can query on data stored in Google BigQuery or external sources such as Cloud Storage, Google Drive, or Bigtable.
2) Flat-Rate Pricing
In this model, users can pay a fixed amount to buy slots instead of paying per byte used. The pricing option varies based on how long the service is intended to be used:
- Flex: Initial 60 seconds
- Monthly: initial 30 days
- Annual: Initial 365 days
As the commitment period increases, the cost per unit time reduces. For example, a month’s cost for 100 slots with a monthly commitment will be $2000, while the same thing as the annual commitment will be $1700.
Understanding Random Sampling
Random sampling is part of the sampling technique where each sample has the same probability of being selected. Randomly selected samples are intended as representations that do not side with the total population. If for, several reasons, the sample does not represent the population, variations are called Sampling Errors.
BigQuery Random Sampling is one of the most simple forms of data collection. Each subset has the same opportunity to be chosen as part of the sampling process in BigQuery Random Sampling. For example, in an organization of 300 employees, the population is the total number of employees in the company, and a group of 30 employees required to complete a survey will be the sample. Every individual has the same probability of entering the sample group. However, there is always the possibility that the group or sample does not represent the population. Here, every random variation is called a Sampling Error.
1) Simple Random Sampling
Simple Random Sampling is a random selection of a small segment of individuals or members of all populations. It gives every individual or population member the same and fair probability to be chosen. It’s the most convenient and simple sample selection technique.
2) Systematic Sampling
Systematic Sampling is the selection of individuals or certain members of the entire population. Selection often follows the Specified Interval (K). The Systematic Sampling method is proportional to the Simple Random Sampling method, just less complicated.
3) Stratified Sampling
Stratified Sampling partitions a population into subclasses with notable distinctions and variances. This Sampling method is helpful because it allows researchers to make more reliable and informed conclusions by confirming that each of their respective subclasses has been adequately represented in the selected sample.
4) Cluster Sampling
Cluster Sampling, which is like the Stratified Sampling method, includes dividing the population into subclasses. Each subclass must describe the characteristics that are comparable to all selected samples. This method requires Random Selection of all sub-class, compared to the sampling of members of each subclass. This method is ideal for studies involving a widespread population.
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Understanding Google BigQuery SQL
SQL (Structured Query Language) is a specialized query language domain to manage data in RDBMS (Relational Database Management System) or Data-Warehouses such as Google BigQuery.
The foundations of SQL are Relational Algebra and Tuple Relational Calculus. SQL was also one of the first commercial languages to employ a Relational Model proposed by Edgar F. Codd. At present, SQL is not only used by database administrators but also by developers who make data integration scripts and Data Analysts preparing and running analytical queries.
There are four sub-languages in SQL:
- Data Definition Language (DDL): The Database Structure or Scheme is defined using the Data Definition language. This statement determines the details of implementing a Database Schema that is usually hidden from the user.
- Data Query Language (DQL): DQL statement is used to ask for Data Stored in the Schema Object. The aim of the DQL command is to get the Scheme Relationship based on the query provided for it.
- Data Manipulation (DML): DML statement is used to manage the data contained in the schema object.
- Data Control Language (DCL): DCL statement is used to control access to the data contained in the database (authorization).
Key Advantages of Table Sampling
Here are a few advantages of Table Sampling:
1) Data Analysis with Less Error Risk
Random Sampling allows researchers to carry out Data Analysis on collected data with lower error margins. The entire process is randomized, and the random samples reflect the entire population. This allows data analysis to provide accurate insight into the concerned subject matters in BigQuery Random Sampling.
2) Less Bias
As individuals who form a subset of larger groups are randomly selected, each individual in the large dataset has the same probability of being chosen. The randomness of selection creates a balanced subset that brings the most significant individual to represent a larger group as a whole.
As the name suggests, producing Simple Random Samples is far more complicated than other methods, such as Stratified Random Sampling. As mentioned, individuals on the subset are selected randomly, and there are no additional steps in BigQuery Random Sampling.
Selecting a Randomly Distributed Sample from BigQuery Tables
Here are the two methods to select tables in BigQuery Random Sampling:
1) Use the RAND Function
Before TABLESAMPLE is added, the RAND function is used as an alternative to retrieve a Random Sample subset. The querying cost is enormous as Google BigQuery will scan the entire table to generate one random number for each record.
2) Use the TABLESAMPLE Clause
You can use the TABLESAMPLE clause to select a Pseudo-Random Number of Rows from a table. It is based upon a percentage or several rows and an optional seed number—if you need repeatable results.
Unlike the ‘LIMIT’ clause, TABLESAMPLE returns a random subset of data from a table. Since Google BigQuery does not cache the results of queries that include a TABLESAMPLE clause, the query might return different results each time. You can combine selection conditions with the TABLESAMPLE clause.
The following example samples about 50% of the table and then applies a WHERE clause:
The next example combines a TABLESAMPLE clause with a JOIN clause:
You’ll receive an empty result when none of the sampled rows of two joined samples meets the join condition. You can specify the percentage as a query parameter.
You can see how to pass the percentage to a query by using the ‘bq’ command-line tool:
The TABLESAMPLE clause randomly selects a percentage of data blocks (Google BigQuery organizes tables into data blocks) from the table and reads all the rows in the selected blocks. The number of data blocks limits the Sampling Granularity.
Whenever tables or table partitions are larger than about 1 GB, Google BigQuery splits them into Blocks. Smaller tables might comprise a Single Data Block. In such a scenario, the TABLESAMPLE clause reads the entire table. If the table is empty and the sampling percentage is greater than zero, table sampling always returns some results.
The Data Blocks can be of different sizes, so the exact fraction of sampled rows might vary. You can sample individual rows by using the WHERE rand() < K clause instead of the TABLESAMPLE clause.
However, Google BigQuery will have to scan the entire table with the WHERE rand() < K clause, increasing your cost. You can work in your budget and still benefit from Row-Level Sampling by combining both techniques.
The following example combines both the techniques where the query reads approximately 20% of the data blocks and then randomly selects 10% of the rows from those blocks:
This is how Google BigQuery Random Sampling is done!
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