BigQuery Nodejs Data Simplified: A Comprehensive Guide 101

Node.js, an open-source platform, which when used with Google BigQuery complements its overall value proposition. It not only supports the overall process of extracting key insights from raw datasets but ultimately improves the decision-making process and relevant time to market for any given enterprise. All in all, BigQuery Nodejs data forms the pivot of these efforts by allowing end-users to work with greater flexibility; not only do they enhance the applicability of visualization but also establish a cross-linkage amongst multiple datasets that can help in cross tabbing different sets of information.

Upon a complete walkthrough of this article, you will gain a decent understanding of Google BigQuery and Node.js. This article will also provide you with a comprehensive guide on how to leverage Node.js for querying BigQuery data. Read along to learn more about BigQuery Nodejs data!

Table of Contents

• What is Google BigQuery?
  • Key Features of BigQuery
  • What is a BigQuery Job?
• What is Node.js?
• How to use BigQuery Nodejs Data?
  • Setting up a Service Account in GCP
  • Setting up a Node.js Project in BigQuery
  • Creating a Dataset Table
  • Streaming Data into BigQuery
  • Adding Rows/Records to BigQuery
  • Insert Error in BigQuery
  • Querying Data
• Conclusion

What is Google BigQuery?

More and more organizations are looking to unlock Business Insights from the data. But it can be difficult to Ingest, Store, and Analyze that data as it rapidly grows in scale and scope. Google’s Enterprise Data Warehouse, BigQuery, has been designed to make large-scale Data Analysis accessible to everyone. When the size of recorded data grows to Gigabytes, Terabytes, or even Petabytes, an enterprise surely needs a more efficient system like a Data Warehouse; all that data isn’t very useful unless one has a way to analyze it.

BigQuery has the capability to handle massive amounts of data e.g., logs from outlets of retail chains down to the SQL level or IoT data from millions of utility meters, telecom usage, and vehicles across the globe. Powered by Google, BigQuery is certainly a Platform as a Service (PaaS) offering, with a fully managed data house in a serverless architecture; it enables the organizations to focus on analytics instead of managing the infrastructure.

Key Features of Google BigQuery

Some of the key features of Google BigQuery are as follows:

• Being a serverless architecture, BigQuery operates on the cloud platform, thus facilitating the scalability of analytics automatically. 
• BigQuery allows the users to capture the best of the decision-making insights by forming and implementing Machine Learning algorithms using SQL. It offers real-time analytics based upon high-speed streaming insertion API. The user just needs to incorporate the real-time data and BigQuery can analyze it instantaneously. 
• By design, BigQuery helps one avoid the data silo problem owing to the existence of individual teams in an organization, having their independent data marts as it offers cross-team communication concerning any of the databases.  
• Owing to the integration of the subject tool with Google Cloud’s native identity and access management frameworks, the user can take control of the permissions and relevant access criteria for specific individuals, teams, or ventures thus enabling the safety and security of classified data to keep the classified all while still empowering the cross-team communications. 
• Working with data in BigQuery involves three primary parts – Storage, Ingestion, and Querying. Being a fully managed provision, one doesn’t need to set up or install anything and even doesn’t need the database administrator. One can simply log in to the Google Cloud project from a browser and get started. 
• Data in BigQuery is stored in a Structured table, which means one can use standard SQL for easy querying and data analysis. It is perfect for Big Data because BigQuery manages all the storage and scaling operations automatically for the client. 
• Of course, storing the data alone doesn’t matter if one can’t get into BigQuery in the first place; there are a lot many ways to do that as BigQuery is integrated with many of the Data Analytics platforms. Once the data is in the BigQuery, one can use SQL, having worked with ANSI-compliant relational databases in the past. 
• BigQuery also supports the data transfer service via which users can get data from multiple sources on a scheduled basis e.g., Google Marketing Platform, Google Ads, YouTube, Partner SaaS applications to BigQuery, Teradata, and Amazon S3. 
• Additionally, the user is able to share access with other users, so that they can also derive insights from the relevant datasets. BigQuery provides users the flexibility to bypass the Ingestion and Storage steps, by analyzing BigQuery Public Datasets, these are third-party datasets that have been made public for anyone to query against. 
• Considering the essential nature of data for any organization, BigQuery offers automatic backup and restore options. It also keeps track of the performed changes on a 7-days basis so that comparison with previous versions can be done if necessary and recall any changes accordingly.

What is a BigQuery Job?

End-user’s actions in BigQuery are referred to as Jobs, which might entail Loading, Querying, Exporting, and Copying a particular dataset. Certainly, resources are required to run or execute these jobs. When a user employs the cloud console or the underlying bq command tool, the requisite resources for Loading, Exporting, and Copying the data are automatically created. These resources can also be created programmatically. When it is done, BigQuery schedules the relevant jobs and executes them accordingly.

There can be different status of jobs, which can be categorized into the following three categories:

• Done: It signifies that the given job is marked complete; if it is done by BigQuery without any errors, it is reported as SUCCESS. If the job gets done coupled with errors, BigQuery reports it as a failure.
• Pending: A particular job is scheduled and is waiting to be run. 
• Running: A given job is in progress. 

What is Node.js?

Node.js is a JavaScript runtime built on Chrome’s V8 JavaScript Engine, as per the official website. Essentially, it is a manifestation of an open-source architecture that enables a cross-platform in a back-end JavaScript runtime environment. Powered by the V8 engine, the utility of Node.js resides in the fact that it can process the JavaScript code outside a web browser. It is primarily used by the developers to formulate command-line tools, in addition to server-side scripting for the formulation of the dynamic web content before it actually gets opened on the user’s display.

Keeping it in view, Node.js can be considered as a “JavaScript everywhere” regime i.e., integrate Application Development (web) around a sole Programming Language, rather than different programming media being used for the associated stakeholders i.e., Server-side and Client-side codes. As far as the nomenclature is concerned, .js is referred to as a standard file extension name in JavaScript but Node.js doesn’t refer to a particular file rather it is a generic product name. Specifically, Node.js is an event-driven architecture aimed to enrich and optimize throughput and scalability in web applications. 

How to use BigQuery Nodejs Data?

The use of Node.js for BigQuery is a seamless process, yet requires certain steps to be followed to query BigQuery Nodejs data. 

1) Setting up a Service Account in GCP

For ensuring that relevant security measures to query BigQuery Nodejs data are in place, it is always recommended to create an extra service account via GCP IAM. This can be done via the following small steps:

• On GCP: Go to “IAM & Admin” → Service Accounts → Create Service Account.
• Create a new account and select the appropriate role, e.g., for this article: BigQuery Data Editor + BigQuery User.
• Select the created service account → Keys → Add Key → Create New Key → Key Type JSON → Download the key.

2) Setting up a Node.js Project in BigQuery

Next is, setting up a crisp Node.js project to query BigQuery Nodejs data which is done via the creation of a new NPM module. The user then needs to install the BigQuery NPM module using npm i –save @google-cloud/bigquery. Once complete, a new JavaScript file is created via which code is integrated to → touch index.js.

3) Creating a Dataset Table

For the creation of a dataset table to query BigQuery Nodejs data, the following sample code can be followed:

4) Streaming Data into BigQuery

Once a dataset table is formulated, it is very easy to stream the data into BigQuery. One caveat is that the service account being used must have the “BigQuery User” role or superior, else an error will pop up due to missing permissions. The following code snippet explains the stream rows in BigQuery:

5) Adding Rows/Records to BigQuery

Adding rows and the associated data into BigQuery is as simple as it can get. The user first needs to define the rows which need to be inserted (matching the table schema) and then can command the BigQuery to insert the rows under the relevant dataset.

6) Insert Error in BigQuery

If an invalid row or data record is inserted into the BigQuery, it will show an error (relevant to the nature of inconsistency). The following snippet shows an error like that:

7) Querying Data

Querying BigQuery Nodejs data is as simple as inserting but the steps involved in it might be different:

• Definition of selected fields (number of rows) via the creation of the relevant SQL Query. 
• Creation and triggering of SQL job that can run the above query asynchronously in the background. 
• Extraction of the returned job object from the above step and waiting for the query results. 

An example of querying data is shown as follows:

Conclusion

This article introduced you to Google BigQuery and the salient features that it offers. You also learned about various operations that you can perform using BigQuery Nodejs data.

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