Data is an integral part of any business or company in a data-driven world where most businesses manage their workflows online. Applications are developed with a modern Cloud architecture to handle large data volumes without lag because the data generated by users is increasing rapidly.
The applications are now decoupled into smaller parts for greater flexibility and work as independent building blocks. PubSub messaging is a new data-driven architecture that delivers instant event updates to distributed applications.
PubSub allows services to communicate asynchronously with latencies in the order of 100 milliseconds. The data sent from PubSub to BigQuery is important for companies as it can contain customer details, operation details, Marketing data, E-Mail data, etc.
Data Warehouses such as Google BigQuery help companies store and analyze this data. PubSub handles high volumes of data simultaneously in an application that helps developers to create apps faster and independent of other data streams.
In this article, you will read about PubSub and its use cases. You will also learn the steps to connect PubSub to BigQuery for seamless data flow.
What is Google BigQuery?
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Google BigQuery is a Cloud Data Warehouse that is a part of the Google Cloud Platform which means it can easily integrate with other Google products and services.
It allows users to manage their terabytes of data using SQL and helps companies analyze their data faster with SQL queries and generate insights.
Google BigQuery Architecture uses the Columnar Storage structure that enables faster query processing and file compression.
Google BigQuery can be accessed through Google Cloud Platform Console or its Web UI interface or by making calls to its Rest API. It offers a flexible pricing model based on pay-per-usage. Users can use Hevo to connect to Google Cloud Platform, REST APIs, and a multitude of sources with Google BigQuery using its No Code interface and automating the process seamlessly.
Key Features of Google BigQuery
Google BigQuery allows enterprises to store and analyze data with faster processing. There are many more features for choosing Google BigQuery. A few of them are listed below:
- BI Engine: BI Engine is an in-memory analysis service that allows users to analyze large datasets interactively in Google BigQuery’s Data Warehouse itself. It offers sub-second query response time and high concurrency.
- Integrations: Google BigQuery offers easy integrations with other Google products and its partnered apps. Moreover, developers can easily create integration with its API.
- Fault-tolerant Structure: Google BigQuery delivers a fault-tolerant structure to prevent data loss and provide real-time logs for any error in an ETL process.
What is PubSub?
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PubSub (Pub/Sub) stands for Publisher-Subscriber messaging, which is a method for asynchronous communication. It is involved in sending messages where the receiver doesn’t know about the sender and vice-versa.
The Publisher in PubSub is the one who publishes messages to a topic with the help of data stream from apps, while the Subscriber is the one who subscribes to a topic or application to receive messages.
PubSub is used for high volume Data Streaming synchronously and data integration pipelines to ingest and distribute data.
Real-time Data Streaming to Google BigQuery for handling handle data queueing & streaming tasks can be achieved using Hevo which also supports built-in connectors for Google Cloud Storage, Jira, Kafka, etc. to streamline data transfer to Google BigQuery.
PubSub acts as an intermediate or middleware service integration or as a queue to parallelize tasks. Publishers send messages or events to the Pub/Sub without knowing if there is a receiver or not.
Similarly, the receiver requests or subscribes to pull messages from PubSub without knowing any Publisher.
Some common Use Cases of Pub/Sub are listed below:
- Parallel Processing: Pub/Sub can connect with Google Cloud Functions that allow developers to send multiple processing requests parallelly to different PubSub workers, such as sending notifications, compressing an image, updating a profile picture, etc.
- Real-time Event Distribution: PubSub allows developers to send data streams from multiple events to applications for real-time processing.
- Replicating Data: PubSub is used to distribute the events when changes in Databases. The events are used to create a state history in Google BigQuery and other Data Storage systems.
Key Features of PubSub
PubSub is useful for gathering information or events from many streams simultaneously. A few features of PubSub are listed below:
- Filtering: PubSub can filter the incoming messages or events from Publishers based on various attributes to reduce the delivery volumes to Subscribers.
- 3rd Party Integrations: It offers OSS Integrations for Apache Kafka and Knative Eventing. Also, it provides integrations with Spunk and Datadog for logs.
- Replay Events: PubSub allows users to rewind the backlog or snapshot to reprocess messages.
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Key Features of Pubsub to BigQuery Data Transfer
- High Availability: Pub/Sub BigQuery replication offers synchronous, cross-zone message replication, and per-message receipt tracking to ensure fast and reliable delivery of the message at any scale.
- At-least-once-delivery: PubSub to BigQuery Data Transfer supports In-order and any-order at-least-once message delivery features with both pull and push modes.
- Security: To follow data privacy norms and keep data secure, PubSub to BigQuery Data Transfer ensures data protection with fine-grained access controls and always-on encryption.
Steps to Connect PubSub to BigQuery
Now that you read about Google BigQuery and PubSub. In this section, you will learn the steps to connect PubSub to BigQuery.
Here, you will learn to manually send messages from PubSub to BigQuery as an example. Similarly, this can be implemented in applications. The steps to connect PubSub to BigQuery are listed below:
Step 1: Creating Google Storage Bucket
- Log in to Google Cloud Platform.
- There 2 ways to use Google Cloud Platforms i.e., through Web UI and Cloud Shell. In this tutorial to connect PubSub to BigQuery, we will use Cloud Shell.
- Open the Cloud Shell Editor by clicking on the shell icon located on top of the screen, as shown in the image below.
Image Source: Self
- It will take some time to start, then click on the “Authorize” button to grant permissions.
- Now choose the existing project or create a new project for PubSub to BigQuery dataflow.
- Now on Cloud Shell and create a new Bucket in Google Cloud Storage by typing the command given below.
gsutil mb gs://uniqueName
- Here, provide a unique name to the Bucket in place of “uniqueName” in the above command.
- It will create a new Bucket in Google Cloud Storage.
- In the new tab of the browser, open Google Cloud Platform. Next, open Google Cloud Storage and check if the new Bucket is present or not.
Step 2: Creating Topic in PubSub
- Go to the Cloud Shell again and create a new topic in PubSub by typing the command given below.
gcloud pubsub topics create Topic01
- Here, provide a topic name of your choice in place of “Topic01” in the above command.
- It will create a new topic in PubSub.
- In the new tab of the browser open Google Cloud Platform and there open PubSub and check if the new topic with a provided name is present or not.
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Step 3: Creating Dataset in Google BigQuery
- Now, create a new dataset in Google BigQuery by typing the command in Cloud Shell given below.
bq mk dataset01
- Here, replace the name of the dataset of your choice with “dataset01“.
- It will create a new dataset in Google BigQuery, that you can check in Google BigQuery Web UI.
- Now, let’s create a new table in the current dataset to get messages from PubSub to BigQuery in that table.
- Here, let’s take a simple example of a message in JSON (JavaScript Object Notation) format, given below.
{
"name" : "Aditya",
"language" : "ENG"
}
- You can create any message you want but it should be in JSON format.
- Also, it’s an example. Generally, the PubSub topic is connected to an application using programming languages and scripts.
- According to the above example, the message has 2 fields “name” and “language” as “STRING” type.
- To create a table according to the above message structure, type the command given below.
bq mk dataset01.table01 name:STRING,language:STRING
- Here, “dataset01” is the name of your dataset, and “table01” is the name of the table you want to provide. Then, “name” and “language” as fields with their datatypes.
Step 4: Connecting PubSub to BigQuery Using Dataflow
- In the new tab of the browser, open Google Cloud Platform and go to search for “Dataflow” and open it. It will open the Dataflow service by Google.
- Here, click on the “+CREATE JOB FROM TEMPLATE” option to create a new PubSub BigQuery Job, as shown in the image below.
Image Source: Self
- Now, provide the Job name and choose the “PubSub Topic to BigQuery” option in the Dataflow template drop-down menu, as shown in the image below.
Image Source: Self
- Copy the “Topic name” from the PubSub topic you just created and the Table ID from the Google BigQuery Table Info.
- Paste both the values in their respective fields in Dataflow.
- Go to Google Cloud Storage, and open the current project Bucket. There navigate to the “CONFIGURATION” tab and copy the value against “gsutil URI“.
- Paste the value in the “Temporary location” field in Dataflow.
- Now, click on the “RUN JOB” button to connect PubSub to BigQuery.
- Now, go to PubSub and click on the “+ PUBLISH MESSAGE” button.
- Copy and paste the above message in JSON or provide your message based on the fields you added in the table.
- Click the “PUBLISH” button. It will publish the message from PubSub to BigQuery using Dataflow and Google Cloud Storage.
- You can check data in the table in Google BigQuery.
That’s it! You successfully connected PubSub to BigQuery.
Use Cases of PubSub to BigQuery Connection
Some of the common use cases of BigQuery PubSub Connection are listed below.
Stream Analytics
Google Cloud comes with a streaming analytics solution that allows its users to ingest, process, and analyze event streams in real-time to make data more organized, useful, and accessible from the instant it is generated.
The availability and accessibility of fresh data are possible via the PubSub service with Dataflow and Google BigQuery.
It manages all the resources needed to handle fluctuating volumes of real-time data for live business insights.
Moreover, PubSub to BigQuery reduces the complexity and makes data streams available to Data Engineers and Analysts.
Asynchronous Microservices Integration
PubSub serves as a messaging middleware for traditional service integration or a simple communication medium for modern microservices used in applications.
The PubSub push subscription feature delivers the events to serverless webhooks on Cloud Run, App Engine, Cloud Functions, or even on the custom environments such as Google Kubernetes Engine.
Moreover, it also provides a low-latency pull delivery option when exposing webhooks is not an option or for efficient handling of throughput streams.
Benefits of Connecting PubSub to BigQuery
PubSub powers modern applications with an asynchronous streamlined data flow. Connecting PubSub to BigQuery allows companies to get all the data, messages, or events in a Data Warehouse. A few benefits of Connecting PubSub to BigQuery are listed below:
- Connecting PubSub to BigQuery allow companies to transfer data to Data Warehouse to run analytics on data and generate insights.
- PubSub ensures reliable delivery of the message at any scale and message replication simple.
- It allows users to integrate PubSub with many other apps and services through Google BigQuery and publish data.
Conclusion
In this article, you learned about the steps to connect PubSub to BigQuery. You also read how PubSub to BigQuery data flow allows companies to store user data and manage applications’ data streams at any scale. PubSub allows developers to manage data streams in real-time without lag.
PubSub ensures data delivery by maintaining a queue and following the one-to-many, many-to-one, and many-to-many Pubsliher-Subscriber model.
Companies store valuable data from multiple data sources in Google BigQuery. However, when it comes to loading with BigQuery, you need to be an expert to set up ETL pipelines from scratch and manually configure several details.
Moreover, most of the time, the data is not available in the right format and you will need data engineering and BigQuery administration skills to transform the data.
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Aditya has a keen interest in data science and is passionate about data, software architecture, and writing technical content. He has experience writing around 100 articles on data science.