ETL Pipeline using Google Cloud Platform - Everything you need to know! 

Pipelines often come up in the field of Data Engineering in many capacities, but what is a pipeline, and what does it mean in this context?

In Data Engineering, a pipeline consists of multiple processes that help migrate data from the source database to a destination database. The most common pipelines you will see in Data Engineering are ETL and ELT pipelines. 

In this article, we are going to discuss the ins and outs of the tools used to construct ETL pipelines as well as the benefits of using said ETL pipelines.

ETL Pipelines 

ETL refers to Extract, Transform, and Load. In building an ETL pipeline, we extract data from various sources such as transactional databases, web APIs, flat files, etc. We then transform this data to make it suitable for analysis and reporting. Usually, this involves applying processing techniques like cleaning and standardizing the data as well as performing calculations and aggregations. Once the data is ready, we load it into a Data Warehouse – usually a tool such as BigQuery or Redshift.

Business Intelligence and Its Role in ETL Pipelines 

The whole point of creating ETL pipelines for data is to make it accessible to the people using it – this typically means decision-makers within an organization. The data processed through ETL pipelines is usually displayed through a dashboard in the form of charts, graphs, and other visualizations. 

Business Intelligence engineers are often the people who work side-by-side with Data Scientists to create Machine Learning models to create these data visualizations using BI tools. These visualizations help decision-makers understand and interpret the data better and are a vital part of the ETL pipeline, even if they’re not technically in the name. 

How Do You Construct ETL Pipelines? 

We typically extract and transform data by writing Python scripts or using tools like Talend, Informatica, etc. We then use data orchestration tools to automate the process of building and maintaining these data pipelines, often including features for scheduling the ETL process, monitoring it, and handling any errors. Examples of the tools used to do this include Apache Airflow, Perfect, and AWS Glue. 

In the figure below, you can see the Data Pipeline Architecture. Data is being fetched from sources like Active Campaign, ChargeBee, and Facebook on the left block and is being used in applications by decision-makers and in various applications on the right. 

GCP Tools for Building ETL Pipelines – From Gathering Data to Data Transformation and Loading

Google offers services like Google Cloud Pub/Sub, Cloud Scheduler, and Cloud Functions under Google Cloud Platform (GCP) to build a data pipeline. 

• Cloud Pub/Sub is a messaging service that allows you to send and receive messages between independent applications. It can be used to pass data between different components of a data pipeline or to trigger the execution of a data processing job. 
• Cloud Scheduler is a fully managed cron job service that allows you to schedule the execution of jobs or other actions regularly. You can use the Cloud Scheduler to trigger the execution of a Cloud Function at a specific time or regular intervals. 
• Could Functions is a serverless execution environment for building and connecting cloud services. You can use Cloud Functions to execute code in response to specific events, such as a message being published to a Cloud Pub/Sub topic or a file being uploaded to Cloud Storage. 

These three services can be used together to build a data pipeline that ingests data from various sources, processes it, and stores it in a destination of your choice. You can use Cloud Pub/Sub to pass data between different stages of the pipeline, Cloud Scheduler to trigger the execution of processing jobs regularly, and Cloud Functions to implement the logic for each stage of the pipeline.

Once you’ve loaded and transformed the data using these three services, you can proceed to the Loading stage of the pipeline. 

The Change Data Capture technique is applied to Cloud Functions for incremental data, and then the updated data is dumped into Google BigQuery. BigQuery is a fully managed, cloud-native data warehousing and analytics platform offered by Google Cloud Platform (GCP). It allows you to store and analyze large and complex datasets using SQL-like queries and is designed to handle petabyte-scale data with high-performance rates and low latency. It is designed to handle a wide range of data types and structures, including structured data stores in tables and semi-structured data such as JSON and Avro. 

It also supports real-time data streaming and can be used to analyze both batch and streaming data. Since it is fully managed, you don’t have to worry about setting up and maintaining infrastructure or performance optimization. It is also fully integrated with other GCP services like Cloud Storage, Cloud Pub/Sub, and Cloud Functions, which makes it easy to build data pipelines and perform complex analytical tasks. 

Data Visualization With Google Data Studio 

Once your ETL pipeline is up and running, Google Data Studio comes into play. It is a cloud-based visualization and reporting platform that allows one to create interactive dashboards and reports using data from a wide range of sources, including BigQuery. 

To connect BigQuery to Google Data Studio, you first need to ensure that you authorize Google Data Studio to access your BigQuery datasets. After connecting BigQuery to Google Data Studio, you can use the Data Studio visual editor to create charts, tables, and more using your BigQuery data. 

You can also use Data Studio’s built-in formatting and styling options to customize the appearance of your report. 

The Benefits of ETL Pipelines 

There are many benefits to using ETL data pipelines, including the following: 

• Improved data quality: ETL pipelines allow organizations to standardize and validate data as it is being extracted and transformed, ensuring that the data being loaded into the destination system is accurate and consistent. 
• Increased efficiency: ETL pipelines automate the data movement process, freeing up time and resources that would otherwise be spent manually extracting, transforming, and loading data. 

• Enhanced reporting capabilities: By centralizing data in a single location, ETL pipelines make it easier to generate reports and perform analysis, providing a more comprehensive view of the data.

Conclusion 

Overall, end-to-end data engineering pipelines are a critical component of any organization that relies on data to drive business outcomes. By building a complete system for collecting, storing, processing, and analyzing data, organizations can make informed data-driven decisions, optimize their operations, and drive innovation.

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