Business organizations receive data from various resources, they generate petabytes of data on a regular basis. With traditional databases, it is difficult to keep track of this data and even to access it flexibly and this is where Google BigQuery saves the day by providing the users with optimized results with better performance and availability. BigQuery lets you specify the Schema of a table when you load data into a table and create an empty table. Alternatively, you can use Auto-Schema detection for supported data formats.
Upon a complete walkthrough of this article, you will gain a decent understanding of Google BigQuery along with the salient features that it offers. You will also learn about the different ways that can be used to specify BigQuery Schema. Read along to learn more about BigQuery Schema and its components!
Table of Contents
Introduction to Google BigQuery
BigQuery is an enterprise Data Warehouse built using Bigtable and Google Cloud Platform. It is a serverless platform that doesn’t rely on external management but relies on itself solely. The best part about using this software is its capability to store all sizes of data. It can range from a small 150 row Excel Sheet to several Petabytes of data. Not only this, but it can also perform a complicated query on any set of data within a few minutes.
Before you delve deeper into the nitty-gritty of this serverless Data Storage/Analyzing platform, it is important to note that BigQuery isn’t a Transactional database. It takes approximately 2 seconds to run a simple query like “SELECT” than solving a large data query involving 500 rows. Therefore, it shouldn’t be considered as an OLTP (Online Transaction Processing) database. BigQuery is designed to deal with a massive quantity of data!
Assume a scenario to dive deeper into the concept of BigQuery with the help of an example. Suppose you are a Data Scientist and use a tool like MySQL to analyze Terabytes of data. Being an active user of MySQL, you have to look at needs such as Infrastructure to store large magnitudes of data. Moreover, you would have to figure out several other factors such as RAM and DCOS size to get started with MySQL.
In BigQuery, all you need to do is upload your CSV/JSON file in bulk, and you’re good to go! The platform is Scalable, Serverless and Highly available allowing you to perform all SQL queries in a matter of seconds. Lastly, it doesn’t require you to focus on Infrastructure needs but keeps you hooked on your primary task- analyzing data.
Key Features of Google BigQuery
Some of the key features of BigQuery are as follows:
- Easy Implementation: BigQuery isn’t as complicated as it sounds. It is relatively easy than other Data Storage platforms. As mentioned earlier also, all you need to do is store your bulky data in BigQuery and leave the rest for the Warehouse to do. You don’t need to focus on any Infrastructural needs.
- Speed- The speed of Storing, Processing data is real quick in BigQuery. To be exact, BigQuery can process billions of rows in a few milliseconds and handle the real-time analysis of streaming data.
- Multicloud Functionality- BigQuery provides an analytics solution to organizations across multiple Cloud platforms with its separating Compute and Storage components. This helps BigQuery to run a computation on the location where data is located without the need to move to a different zone.
For further information on BigQuery, click here to check out their official website.
Introduction to BigQuery Schemas
In a literal sense, Schemas are a cognitive Framework that helps organize and interpret information. In the BigQuery software, Information Schemas are a set of Metadata tables that provides comprehensive data on the consumption and performance of the entire organization. The monitoring performance block in BigQuery lets you analyze various patterns of consumption to optimize slot capacity distribution. Additionally, it also identifies the troublesome queries and average query time across all projects in the organization. It’s important to note that BigQuery lets you specify a table’s Schema while uploading data into a table when creating an empty one.
In the later section of this article, you will learn about the key components of BigQuery Schemas.
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4 Key Components of BigQuery Schema
There are a total of 4 key components in BigQuery Schema. They are as follows:
1) Column Description
Each column includes an optional description. If you wish to add a description, then you can add one (but not more than 1024 characters) for a column. This description is helpful to explain what a particular character means.
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2) Column Names
In the Column Name, you are required to specify the parameter for which each column is responsible such as Date, User_Id, Products, etc. The Column Name must contain only the Latin characters/letters (a-z, A-Z), numbers (0-9), or underscores (_), and it must start with a letter or underscore. Additionally, the maximum length of a Column Name is approximately 300 characters. Lastly, you must be aware that the Column Name cannot start by prefixes: _TABLE_, _FILE_, _PARTITION. Duplicate names are not allowed either.
BigQuery supports some Modes. These are particularly: Nullable, Required, Repeated. In the Nullable Mode, null values are allowed. The Required Mode doesn’t allow any null value. Finally, the Repeated Mode contains an array of values of the specified type in the column. It is not necessary to fill the mode. If no Mode is specified, then it automatically takes up the default Nullable Mode.
4) Data Type
BigQuery also lets you specify the Data Type. Some of the major ones that you can use to specify in your schema are INT64, FLOAT64, NUMERIC, BOOLEAN, STRING.
3 Ways to Specify Data in BigQuery
1) Specifying Schemas using API’s
To specify a Schema when loading data, you need to call the jobs.insert method and configure the Schema property in the JobConfigurationLoad resource. On the other hand, if you want to specify a Schema using API while you create a table, you need to call the tables.insert method and configure the Schema property in the Table resource.
2) Specifying Schemas using JSON Files
The next method to specify BigQuery Schemas is using the JSON files method. In the JSON files method, a JSON file consists of a JSON array of the Column Name, Column Mode, Column Data Type and Column Description. You need to create a JSON Schema file and enter the JSON array using an appropriate text editor as follows:
Once you create a JSON file, you can specify it using the bq command-line tool. But remember that you can’t use a Schema file with the Cloud Console or the API. When you supply your JSON Schema file, store it in your locally readable location. You cannot specify your JSON Schema file in Cloud storage or in the drive.
3) Specifying Schemas Manually
Yes, you can specify the BigQuery Schema manually as well. When specifying Schema manually in BigQuery, you can either use a Cloud Console or a bq command-line tool. Manually specifying Schema is only supported by BigQuery when you load CSV and JSON (newline delimited) files. Furthermore, when you load Avro, Parquet, ORC, Firestore Export Data, or Datastore Export Data, the Schema automatically gets retrieved from the self-describing data source. Follow the steps given below to manually specify the BigQuery Schema using Cloud Console:
- Step 1: Open BigQuery Page in Cloud Console.
- Step 2: Expand your Project and select a dataset in the Explorer panel of the software.
- Step 3: Expand the “Actions” option and click on “Open“.
- Step 4: Click “Create Table” in the details panel section.
- Step 5: Select “Empty Table” from the source section on the Create Table page.
- Step 6: Visit the destination section on the Create Table page. Once you do that, choose an appropriate dataset, enter the table you want to create, and verify that the table type is set to Native table.
In this article, you learned about the three ways used to specify BigQuery Schema. You learned everything ranging from the basics of BigQuery to BigQuery Schema and specifying them in the platform. You can use it for various purposes such as setting up end-to-end analytics, evaluating customer acquisition channels, and even segmenting your customers. Integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.
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