5 Best GCP ETL Tools you can use

Are you confused about which tool to use for ETL from your Google Cloud account? Are you struggling to match your requirements with the ETL tool? If yes, then this blog will answer all your queries.

The usage of GCloud tools has increased in this era of Big Data, where data is quickly expanding, thus resulting in a spike in demand for the finest GCP ETL tools in the market.

This article provides you with a list of the top 5 GCP ETL tools and their key aspects, which you can use to simplify ETL for your business.

What are Google Cloud ETL Tools?

• This includes Cloud data fusion, Cloud data flow, Dataprep, Dataproc etc. They have their pros and cons in terms of the features they provide and the use cases they support.
• Therefore, it’s best to consider all the popular vendors before finalizing a few of the options provided by Google Cloud.

Top 5 GCP ETL Tools

1) Cloud Data Fusion

Cloud Data Fusion is a cloud-native data integration tool. It is a one of the fully managed GCP ETL tools that allows data integration at any scale.

It is built with an open-source core, CDAP for your pipeline portability. It offers a visual point and clicks interface that allows code-free deployment of your ETL/ELT data pipelines.

Apart from native integration with Google Cloud Services, it also offers 150+ pre-configured connectors and transformations at zero additional cost. 

Key Features

• Google Cloud Integration: Simplifies security and enables fast data analysis with tools like Cloud Storage, Dataproc, BigQuery, and Spanner.
• Pre-built Transformations: Supports both batch and real-time data processing.
• Collaborative Data Engineering: Allows creation, validation, and sharing of custom connections and transformations, boosting productivity and code quality.

Pricing

Google Cloud Data Fusion pricing depends on the interface instance hours. The Basic Edition allows free 120 hours per month per account. Know more about Cloud Data Fusion pricing. 

Use Case

1. Modern data lakes: Centralize data from on-premises silos on Google Cloud for better scalability, visibility, and lower operational costs.
2. Agile data warehousing: Break down data silos to build unified customer views in BigQuery, improving customer experience, retention, and revenue.
3. Unified analytics: Consolidate disparate on-premises data marts into a cohesive analytics environment, enhancing data quality, security, and self-service while reducing TCO and repetitive work.

2) Dataflow 

Dataflow, a managed service within GCP, facilitates the execution of Apache Beam data pipelines. Primarily designed for batch processing, Apache Beam offers features like automatic partitioning of sources and data types, scalability to handle diverse workloads, and flexible scheduling to ensure cost-effectiveness.

Key Features

• Ready-to-use Real-time AI: Enables real-time reactions with near-human intelligence to large torrents of events through out-of-the-box ML features and ready-to-use patterns.
• Autoscaling of Resources and Dynamic Work Rebalancing: Minimizes pipeline latency, maximizes resource utilization, and reduces processing cost per data record by automatically partitioning data inputs and rebalancing worker resource utilization.
• Monitoring and Observability: Allows users to observe data at each step of a Dataflow pipeline, diagnose problems, and troubleshoot effectively with samples of actual data and compare different runs of the job to identify problems easily.

Pricing

You pay for Google Dataflow based on the resources your jobs actually use, billed per second. The specific way resources are measured depends on your chosen pricing model. Know more about Data pricing.

Use Case

While Dataflow isn’t classified as a GCP ETL tool due to its absence of data transformation capabilities, it serves a crucial role in gathering data from various sources and transferring it to designated destinations efficiently.

Additonally, Google Dataflow acts as the engine for processing real-time data streams used in machine learning tasks on Vertex AI and TensorFlow Extended. This allows for functionalities like fraud detection and real-time personalization.

3) Dataproc

• Google Cloud Dataproc is a fully managed and scalable service designed for running a wide range of open-source tools and frameworks such as Apache Hadoop,
• Apache Spark, Apache Flink, and Presto. It enables data lake modernization, ETL processes, and secure data science at scale within the Google Cloud ecosystem, all at a fraction of the cost compared to on-premises solutions.

Key Features

• Serverless Deployment: Dataproc offers serverless deployment, logging, and monitoring, reducing the need for infrastructure management and enabling faster data processing.
• Integration with Vertex AI Workbench: Dataproc integrates with Vertex AI Workbench to enable data scientists and engineers to build and train models 5X faster compared to traditional notebooks.
• Containerization with Kubernetes: Dataproc allows containerizing Apache Spark jobs with Kubernetes for job portability and isolation.
• Enterprise Security: Dataproc supports enterprise security features such as Kerberos, default at-rest encryption, OS Login, VPC Service Controls, and customer-managed encryption keys (CMEK).
• Integration with Google Cloud Ecosystem: Dataproc integrates seamlessly with other Google Cloud services like BigQuery, Vertex AI, Spanner, Pub/Sub, and Data Fusion, providing a comprehensive data platform.

Pricing

Dataproc pricing is based on the number of vCPU and the duration of time they run.

Use Case

1. On-prem to cloud migration: Move Hadoop and Spark clusters to Dataproc for cost management and elastic scaling.
2. Data science environment: Create custom setups with Spark, NVIDIA RAPIDS, and Jupyter notebooks, integrating with Google Cloud AI services and GPUs to accelerate ML and AI development.

4) Pub/Sub

• Google Cloud Pub/Sub is a fully managed, scalable messaging service for ingesting and streaming events to various Google Cloud services like BigQuery, data lakes, or operational databases.
• It offers secure, encrypted data transmission with fine-grained access controls, and supports both pull and push delivery modes.

Key Features

• Stream Processing Integration: Connects seamlessly with Dataflow for reliable and expressive real-time data processing.
• Ordered Delivery: Ensures messages arrive in the order they were sent, simplifying development of stateful applications.
• Simplified Streaming Ingestion: Offers native integrations for easily sending data streams directly to BigQuery or Cloud Storage.

Pricing

The pricing of Google Cloud Pub/Sub is based on the amount of data sent, received, and published in the Pub/Sub.

• First 10 GB: The first 10 GB of data per month is offered at no charge.
• Beyond 10 GB: For data volumes beyond 10 GB, the pricing is $40 per TB.

Use Cases

1. Stream analytics: Ingest, process, and analyze real-time data using Pub/Sub with Dataflow and BigQuery for instant business insights, accessible to both data analysts and engineers.
2. Microservices integration: Act as messaging middleware for service integration or microservices communication, with push subscriptions to serverless webhooks or low-latency pull delivery for high-throughput streams.

5) Google Cloud Composer

• Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, allowing users to author, schedule, and monitor pipelines using Python across hybrid and multi-cloud environments.
• It integrates seamlessly with various Google Cloud products like BigQuery, Dataflow, and AI Platform. Composer’s managed nature frees users from infrastructure concerns, letting them focus on workflow management.

Key Features

• Hybrid and Multi-Cloud: Orchestrates workflows across on-premises and public cloud environments.
• Open Source: Built on Apache Airflow, providing freedom from lock-in and portability.
• Easy Orchestration: Configures pipelines as directed acyclic graphs (DAGs) using Python, with one-click deployment and automatic synchronization.
• Rich Connectors and Visualizations: Offers a library of connectors and multiple graphical representations for easy troubleshooting.

Pricing

Google Cloud Composer uses a consumption-based pricing model. This means you only pay for the resources you use, billed by:

• vCPU/hour: Covers the compute power used by your workflows.
• GB/month: Accounts for storage used.
• GB transferred/month: Represents the amount of data moved within your workflows.

Best Practices for using Google Cloud ETL Tools

• Leverage built-in integrations: Whenever possible, use pre-built connectors offered by GCP services to connect to data sources and destinations. This saves time and avoids configuration issues.
• Stay within the GCP ecosystem: If possible, stay within Google Cloud Platform for your ETL workflows. This simplifies management, billing, and data security.
• Optimize for cost: Choose the right tool based on your needs. Consider serverless options like Dataflow for flexible, pay-per-use processing, or Dataproc for large-scale batch jobs.
• Design for maintainability: Break down complex workflows into smaller, reusable tasks. This improves maintainability and simplifies debugging.
• Automate wherever possible: Use Cloud Scheduler or Cloud Functions to automate your ETL pipelines for a hands-off approach.
• Monitor and log your pipelines: Track the health and performance of your pipelines with Cloud Monitoring and Logging. This helps identify and troubleshoot any issues.

Helpful Resources on Google Cloud ETL Tool

• Explore Best Cloud ETL Tools

Conclusion

• In this blog, you have learned about the GCP ETL tools.
• You can choose any of the mentioned GCP ETL tools according to your requirements. ETL is the most crucial part of your data analysis. If anything goes wrong in this step, then you will suffer data loss.

Frequently Asked Questions on GCP ETL Tools