BigQuery for data analysis

Have you ever heard about Google BigQuery and wondered what it is all about? Or would you like to know when and how to use BigQuery for data analysis? This article will surely introduce you to BigQuery and what you should know about it.

What is BigQuery?

Google BigQuery is a cloud-based software for managing, storing, analyzing, modeling, and visualizing data. It allows data of varying sizes, structures, formats, etc. Many companies use Google Bigquery for their various data processing activities. Some of these companies are Spotify, The New York Times, Teads, Groww, Sentry, and others.

What are the benefits of using Google BigQuery?

It is important to note that each analytical tool has pros and cons. In that case, it is essential to understand when it really makes sense to use Google Bigquery.

1. When dealing with large file sizes: It is actually possible to deal with large datasets on traditional data analytics tools, but using BigQuery will help you process your large data file in no time without stress. An example of such is enterprise data. Enterprise data are the data being managed by an organization and could comprise structured, unstructured and semi-structured data
2. For real-time data analytics: With Google BigQuery, it is easy and fast to analyze, run queries, or visualize your data as it is being fetched from the source instantly.
3. When time and resources matter: BigQuery makes it such that all the tools that you will use for processing your data are made available at a spot. You will equally have your analytics done by saving money since your resources are being well managed together at a point
4. For security: Google BigQuery ensures that your data is protected against any harm. This occurs as a result of how BigQuery shares your data across several nodes such that even if one is attacked the other ones will serve as backup for the loss.

What purposes can Google BigQuery serve?

The BigQuery platform can be used to serve many purposes, among which are:

Data visualization

Visuals are more easily and rapidly consumed and deciphered than words or numbers. Visualizing data in Bigquery is interesting. Bigquery allows the integration of several software, remember! You can leverage the various visualization tools to produce visuals that will demonstrate the insights drawn from your data to you in no time.

Some of these tools include Tableau, Looker, Google Data Studio, QlikView, Power BI, etc. With these tools, the time spent on visualization will be reduced, and at the same time produce a good report from the data.

ETL

Extract, transform and load is a term that refers to the process of extracting, scraping data from one or more sources, processing the data and finally loading the processed data onto one or more platforms.

There are various tools that can be used for ETL with BigQuery. Some of them are: Dataflow, Airflow, Stitch, Talend, etc.

Machine Learning

BigQuery ML is the BigQuery tool that is used for building machine learning models. The codes for building models in BigQuery ML are written in Standard SQL queries. It makes it easier for SQL experts to build models.

The BigQuery ML supports several models like linear regression, time series, tensorflow, multiclass logistic regression, and so on.

Data analytics:

BigQuery can be used for performing analytics on large datasets (petabytes, terabytes, etc.) using standard SQL queries. It is used for data cleaning, data exploration, data analytics. It is important to master your SQL skills well so you do not have to run tautological queries since each query comes with specific charges.

Geospatial analytics

Geospatial data are part of the data being welcomed by Google BigQuery, as such, it will be easier, faster and more cost effective to have the analytics and visualization done to the data at the same place.

The analytics of geospatial data is done on the BigQuery command tool using SQL queries. The visualization on the other hand can be done on any of the visualization tools on BigQuery. Bigquery ML is as well available for training models on geospatial data.

What are the data types supported by BigQuery?

It is required to know the data types supported by Google BigQuery in order to know more about how to handle them. There are quite a lot of data types accepted by Google BigQuery and they include:

Numeric: This include integers, whole numbers, float, decimal with common notations like INT64, BIGINT, TINYINT, FLOAT64, etc.

Integers: are numeric values that do not have decimal parts and include positive, zero and negative numbers. Examples include 67, -97, 0, 569, etc.

Floats: are numeric values that store the approximate values of an entity. Examples include 9.098, 89.78, 78.98

Decimals: These are numeric values that store the exact value of an entity. Examples include 56.789 (3 dp), 56.79 (2 dp)

<aside> 💡 dp means decimal places

</aside>

Boolean: This includes data which have two possible values which are either TRUE or FALSE

Geography: The geography data type consists of data points that point to coordinates of the earth. They could be in form of points (which are longitudes and latitudes), polygons, linestring, multipoints, multipolygons, etc.

Date: Date type are data storing date values. However, the date values only consist of day, month and year. This type doesn’t take into consideration the time zone. E.g. 2022-08-06, 20/09/2021 etc

Datetime: Unlike the date type, the datetime type stores the date values along with the time zone. Usually they consist of day, month, year, hour, minute and second. e.g.

2024-02-14 20:09:23

String: Strings are data types that store the character data. It only conforms the UTF-8 encoding. Both input and output are UTF-8 encoded.

Arrays: Arrays consist of elements of the same data type but not of array data type. It however does not support array of arrays.

Time: This data type stores a particular time in a day. This comes in a format that includes hour, minute and second. E.g. 24:09:08

Conclusion

Google BigQuery is a cost effective tool for analyzing and managing large datasets like enterprise data compared to some other cloud tools. It is particularly beneficial for real-time analytics, data management, etc. SQL experts find the use of BigQuery simpler which implies that for one to be able to use the platform excellently, one has to learn and be a proficient user of SQL.