ETL Pipeline using Google Cloud Platform - Explained!

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Pipelines often come up in data engineering in many capacities, but what is a pipeline, and what does it mean in this context?

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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.

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In this blog, we will discuss the intricacies of the tools used to construct ETL pipelines as well as the benefits of using said ETL pipelines.

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ETL Pipelines

ETL refers to Extract, Transform, and Load. To build an ETL pipeline, data is extracted from various sources such as transactional databases, web APIs, flat files, etc. Data is then transformed to make it suitable for analysis and reporting. Usually, this involves applying processing techniques like cleaning and standardizing data as well as performing calculations and aggregations. Once the data is ready, it is loaded into a data warehouse – usually with a tool such as BigQuery or Redshift.

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Business Intelligence and Its Role in ETL Pipelines

The entire point of creating ETL pipelines for data is to make it accessible to the data users, which typically refers to the 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.  

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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.  

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How Do You Construct ETL Pipelines?

Typically, data is extracted and transformed by writing Python scripts or using tools such as Talend, Informatica, etc. Next, data orchestration tools are used to automate the process of building and maintaining the 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.  

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

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‍Pro tip: Ready to supercharge your sales and slash costs? Connect with our data engineering experts to learn more on building robust data pipelines using GCP.

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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.  

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• Cloud Pub/Sub is a messaging service that allows users 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 users 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. Cloud Functions are used 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.  

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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. Cloud Pub/Sub is used 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.

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Once data is extracted and transformed using these three services, the Loading stage of the pipeline can be initiated.

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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 can handle a wide range of data types and structures, including structured data stores in tables and semi-structured data such as JSON and Avro.  

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It also supports real-time data streaming and can be used to analyze both batch and streaming data. Since it is fully managed, users 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.  

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Data Visualization with Google Looker Studio

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

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To connect BigQuery to Google Looker Studio, the access between Google Looker Studio and BigQuery dataset needs to be authorized. After connecting BigQuery to Google Looker Studio, the Looker Studio visual editor can create charts, tables, and more using the BigQuery data.  

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For business decisions, the clear, impactful visuals provided by Looker Studio bring insights to life, allowing companies to make smarter decisions. Imagine spotting trends in sales charts, understanding customer behavior through geo maps, or measuring campaign effectiveness with gauge charts. Visualizations unlock the power of data, giving businesses a strategic edge.

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The Benefits of ETL Pipelines

• 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.

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Cloud Computing Platforms & Business Impact

Google Cloud Platform (GCP) stands out with its focus on data analytics, machine learning, and artificial intelligence. GCP's suite of tools, such as BigQuery, Cloud Dataflow, and TensorFlow, enables businesses to process and analyze large datasets efficiently. Google Cloud's Kubernetes Engine simplifies container orchestration, making it easier to deploy and manage containerized applications. GCP's commitment to open-source technologies and its global network infrastructure make it an attractive option for businesses looking to innovate and scale quickly.

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Businesses can easily scale their resources up or down based on demand, ensuring they only pay for what they use. This elasticity is particularly beneficial for handling varying workloads and accommodating growth without significant upfront investments. Additionally, cloud platforms provide high availability and disaster recovery capabilities, ensuring business continuity even in the face of unexpected disruptions.

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Cloud computing platforms also offer advanced security features to protect data and applications. These platforms adhere to strict security standards and provide tools for encryption, identity and access management, and threat detection. By leveraging the security expertise of cloud providers, businesses can enhance their security posture and reduce the risk of data breaches.

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The adoption of cloud computing platforms also fosters innovation by providing access to cutting-edge technologies. Businesses can experiment with new ideas and technologies without the need for significant capital investments. For instance, machine learning and artificial intelligence services on cloud platforms enable businesses to develop intelligent applications that can drive better decision-making and improve customer experiences.

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Data Pilot’s Take

ETL pipelines are like magic bullets for businesses. They streamline data management, leading to two key benefits: increased sales and reduced costs. Data scattered across different systems creates silos, hindering clear analysis and business insights. ETL pipelines centralize data, and provide a clear view of operations, allowing to identify areas for cost reduction, optimizing resource allocation, and making data-driven decisions that save money. The deployment of ETL pipelines on GCP leverages the robust, scalable infrastructure and suite of data processing tools offered by Google Cloud, enabling businesses to handle large volumes of data seamlessly. This not only enhances operational efficiency but also fosters innovation by enabling advanced analytics and machine learning, ultimately driving business growth and competitiveness in a data-driven world.

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Unlock the full potential of your data. Start building your Google Cloud Platform ETL pipeline today and drive your business forward with data-driven insights!

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