A quick starter guide to building serverless ETL pipeline using DataFlow, BigQuery and Cloud Storage

Explore how to streamline your data processing workflow using a robust serverless solution.

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Let’s assume you ply your trade for an e-commerce giant. We are talking about millions of data points from several sources, including online transactions, POS systems, and customer interactions. All this data is meaningless unless you draw some impactful insights.

You must perform Extract, Transform and Load (ETL) operations to leverage and analyze your data. However, setting up traditional data pipelines can burn a hole in your pocket because it’s expensive to provision and maintain dedicated servers for your data analysis. Moreover, as your data volumes grow, scalability and resource management become challenging.

Serverless ETL pipelines offer a cost-effective and hassle-free alternative with automatic scaling and reduced operational overhead. Keep reading to discover how revolutionary GCP technologies like DataFlow, BigQuery and Cloud Storage are transforming ETL pipelines.

Breaking Down Serverless ETL Pipelines

Serverless ETL pipelines feature a complete paradigm shift in how you can execute ETL operations. By leveraging serverless computing platforms like DataFow, here are some massive advantages:

1. Cost Efficiency: With serverless technologies, you pay as you go. You pay only for consumed resources (used during data processing, rather than upfront server provisioning CAPEX costs).
2. Scalability: Google Cloud Dataflow offers automatic scaling and elasticity for ETL pipelines. As data loads fluctuate, serverless platforms like DataFlow proactively increase or decrease active computational resources.
3. Decreased GTM: With serverless architecture, your development cycles get a boost. Your organization can promote agility by quickly releasing incremental product versions.
4. Reliability: The Google Cloud Platform (GCP) has a vast global network of Data Centers (DCs) to reduce any instances of a single point of failure. Additionally, with incredibly high uptimes and redundancy, GCP has covered your ETL demands.

Exploring How Google Cloud is the Missing Piece of Your ETL Pipeline Puzzle

DataFlow, BigQuery, Cloud Storage and Pub/Sub are a few Google Cloud services that help with serverless ETL pipelines:

• Cost Efficiency: With serverless technologies, you pay as you go. You pay only for consumed resources (used during data processing, rather than upfront server provisioning CAPEX costs).
• Scalability: Google Cloud Dataflow offers automatic scaling and elasticity for ETL pipelines. As data loads fluctuate, serverless platforms like DataFlow proactively increase or decrease active computational resources.
• Decreased GTM: With serverless architecture, your development cycles get a boost. Your organization can promote agility by quickly releasing incremental product versions.
• Reliability: The Google Cloud Platform (GCP) has a vast global network of Data Centers (DCs) to reduce any instances of a single point of failure. Additionally, with incredibly high uptimes and redundancy, GCP has covered your ETL demands.

Additionally, other Google Cloud services like Bigtable, Dataprep, Dataproc, etc., combine with DataFlow, BigQuery and Cloud Storage to form a comprehensive ecosystem for big data analysis within GCP.

How to build an ETL Data Pipeline using DataFlow

Here’s a high-level overview of how you can build ETL data pipelines:

1. Define Data Sources & Destinations

Identify your input data sources for data extraction. Additionally, define the destinations where you want to push the transformed data. Your input and output destinations can include Databases (DBs), APIs, files, or other data storage systems.

Your dataset name, input & output BigQuery tables are the three vital input parameters you must define.

2. Data Extraction

Utilize DataFlow to extract data from your identified sources. DataFlow provides connectors like the I/O connector to read data from a BigQuery resource and then write back the transformed data to another BigQuery resource.

This step involves filtering your input data to focus on specific data segments. For instance, if you want to emphasize only the first 10,000 records of a particular dataset, you can specify relevant arguments and save your BQ table into a Pcollection.

Pcollection is short for process collection. Multiple parallel computational resources process a Pcollection.

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3. Data Transformation

Once your dataset is ready, it’s time to implement transformations to clean, filter, aggregate and manipulate data according to your business case.

You can use the Apache Beam framework to build pipelines, which you can run on platforms like DataFlow.

4. Data Loading

Now, it’s time to load your processed data into BigQuery. Define your schema and mapping between the transformed data and BigQuery tables.

Choose an appropriate DataFlow sink to load data into the right BigQuery destination. Specify your BigQuery attributes like project ID, dataset ID, and table ID.

5. Data Storage

You can store the raw or intermediate data in Cloud Storage. Additionally, you can store files, backups and other data as part of your backup or archival procedures.

6. Pipeline Execution

Invoke your pipeline execution by calling the run() function. Monitor your pipeline’s progress and look for resource utilization and potential errors.

The above steps describe how to build an ETL data pipeline using DataFlow. Interestingly, you can migrate your Google Collab notebook into a scalable ETL solution on Google Cloud.

Is all this information going over your head? Contact us for a free consultation on leveraging DataFlow for your ETL pipelines.

Potential Roadblocks While Building ETL Pipelines on DataFlow

Here are a few friction points you may encounter while designing ETL pipeline on DataFlow:

1. Data Quality and Prepping: ETL pipelines often have silos of data with missing values and inconsistencies.
2. Data Transformations: Building complex data transformations within DataFlow requires a firm grasp of the Apache Beam Model and related APIs.
3. Performance Optimization: Tweaking your DataFlow pipelines helps with efficient data processing. You must identify and address performance bottlenecks and improve parallel processing efficiency by applying appropriate data partitioning strategies.

Wrapping Up

Serverless ETL pipelines promise you cost efficiency, scalability and improved performance. You can build a robust and efficient ETL pipeline workflow by combining GCP products like DataFlow, BigQuery and Cloud Storage.

Originally published at https://www.d3vtech.com.