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 ETL tools has increased in this era of Big Data, where data is quickly expanding, thus resulting in a spike in demand for the finest ETL tools in the market. This article provides you with a comprehensive list of some of the best Google Cloud ETL tools and their key aspects which you can use to simplify ETL for your business.
What are Google Cloud ETL Tools?
Google Cloud ETL tools are the tools that Google Cloud provides. 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 8 Google Cloud ETL Tools
Choosing an ETL tool for Google Cloud for your use case can be a make-or-break situation. In this blog, you will consider the following factors while choosing the tools to execute ETL in Google Cloud:
- Overview
- Pricing
- Use Case
1) Hevo Data
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Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.
Sign up here for a 14-Day Free Trial!
Key features of Hevo are,
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
Check Hevo’s in-depth documentation to learn more.
Hevo has a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.
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Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.
– Juan Ramos, Analytics Engineer, Ebury
Check out how Hevo empowered Ebury to build reliable data products here.
2) Google Cloud Data Fusion
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Overview
Google Cloud Data Fusion is a cloud-native data integration tool. It is a fully managed Google Cloud ETL tool 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.
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 here.
Use Case
Google Cloud Data Fusion offers scalable and distributed data lakes on your Google Cloud by integrating data from various siloed on-premise platforms.
It also allows you to have a better understanding of customers by breaking down the data silos and enabling the development of agile and cloud-based data warehouse solutions in BigQuery. Google Cloud Data Fusion offers a unified analytics environment.
3) Talend
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Overview
Talend is a big data and cloud data integration software. Talend is built on Eclipse graphic environment. It also supports scaling massive data sets and advanced data analytics.
It has partnered with leading cloud service providers, analytics platforms, and data warehouses such as Google Cloud Platform, Amazon Web Services (AWS), Snowflake, etc.
Pricing
Talend offers 4 pricing plans that let you put healthy data at the center of your business: Stitch, Data Management Platform, Big Data Platform, and Data Fabric.
Use Case
If you are a company with strict compliance requirements to spread risk across several clouds, then Talend is the correct tool. This Google Cloud ETL tool offers data integration with various on-premise warehouses such as Google Cloud Platform, Amazon Web Services, Microsoft Azure, SAP, etc.
4) Informatica – PowerCenter
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Overview
Informatica is an enterprise on-premise Google Cloud ETL tool that can build enterprise warehouses. It also supports integration with various traditional databases.
It has the capability of delivering data on demand. Some of its key features include advanced transformation, dynamic partitioning, zero downtime, universal connectivity, data masking, etc.
Pricing
Informatica offers a Basic plan at $2000 monthly. Pricing depends on data sources, security features, etc. You can also use their 30-day free trial to learn the ropes.
Use Case
Large organizations which require enterprise-grade security and data governance within on-premise data can use this Google Cloud ETL tool.
5) IBM Infosphere Information Server
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Overview
Information Server is a branch of IBM’s product that revolves around data warehousing and data integration. It’s an enterprise product for large organizations that supports integration with cloud data storage, including Google Cloud, AWS S3, etc.
It offers a solution for the deployment, integration, and management of data warehouses. Infosphere offers massively parallel processing (MPP).
It provides a highly scalable and flexible integration platform that can handle any data of volume.
Pricing
Its pricing includes Information Server Edition and InfoSphere DataStage. Read more about its pricing here.
Use Case
This Google Cloud ETL tool is best suited for large enterprise-grade applications which have on-premise databases.
6) StreamSets
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Overview
StreamSets is a DataOps and real-time Google Cloud ETL tool. It provides data monitoring and supports a variety of data sources and destinations for data integration.
Many enterprises use it to integrate dozens of data sources for analysis. It supports data protectors with data security guidelines like GDPR and HIPAA.
Pricing
StreamSet’s standard plan is free of cost. This Google Cloud ETL tool does not have transparent pricing, so you have to request a quote here to know about the Enterprise Edition.
Use Case
It allows companies to use their on-premise or cloud provider for defining a real-time data pipeline. If you want to use several Saas offerings, then StreamSet is not recommended.
7) Stitch Data
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Overview
Stitch Data is a cloud-first and extensible data integration platform. It provides integration with 90+ data sources. It maintains SOC 2, HIPAA, and GDPR compliance while providing businesses with the power to replicate data easily and cost-effectively.
Moreover, this Google Cloud ETL tool also provides you with the power to scale your ecosystem reliably.
Pricing
Stitch was acquired by Talend, and you can check out the pricing plan on Talend’s pricing page.
Use Case
You can use Stitch data when you want better insights into data analytics. This Google Cloud ETL tool allows data migration within minutes. It doesn’t require API maintenance, scripting, cron jobs, or JSON.
8) Apache Airflow
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Overview
Airflow is a modern platform that designs, creates, and tracks workflows. It is an open-source Google Cloud ETL tool.
It supports integration with cloud services, including Google Cloud Platform, Azure, and AWS. It offers a user-friendly interface and provides clear visualization.
Scaling becomes very easy with Airflow due to its modular structure.
Pricing
Apache Airflow is free of cost and open source.
Use Case
Airflow is a platform to programmatically create, schedule, and monitor workflows. It uses Directed Acyclic Graphs (DAG) for the workflow. It is also used for training ML models, sending notifications, tracking systems, and powering functions within various APIs.
9) Dataflow
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Overview
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.
Pricing
The pricing varies depending on the resources used. You can check here for more information.
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.
10) Dataproc
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Overview
Dataproc collaborates with GCP ETL tools to oversee data management through a diverse array of tools and frameworks, such as Apache Airflow and Spark. It offers a solution for executing open-source data analytics without encountering scalability issues. Additionally, Dataproc adopts a cost-effective, serverless approach to managing Google Compute and Kubernetes clusters.
Pricing
The pricing varies depending on the resources used. You can check here for more information.
Use Case
Google asserts that Dataproc has the potential to significantly reduce the total cost of ownership by up to 54% when compared to on-premises solutions. This makes Dataproc an attractive option for organizations aiming to streamline data analytics operations while minimizing costs.
11) Dataprep
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Overview
GCP Dataprep is a cloud-based tool for visually exploring, cleaning, and preparing raw data (structured and unstructured) for analysis, reporting, and machine learning. It operates in a serverless environment, eliminating the need for hardware deployment or management. With its intuitive interface, users can perform data transformations without writing code, as the platform recommends the next ideal steps.
Pricing
The pricing varies depending on the resources. You can check here for more information.
Use Cases
Dataprep automatically recognizes data schemas, types, potential joins, and anomalies like missing values, outliers, and duplicates. This streamlines the data quality assessment process, enabling users to quickly dive into exploration and analysis tasks. Additionally, Dataprep efficiently selects the appropriate Google Cloud processing engine based on data volume and location, ensuring rapid data transformation.
Conclusion
In this blog, you have learned about the Google Cloud Platform, ETL tools, and the best Google Cloud ETL tools in detail. You can choose any of the mentioned Google Cloud ETL tools according to your requirement. ETL is the most crucial part of your data analysis. If anything goes wrong in this step, then you will suffer data loss.
If you are looking for a real-time and fully automated data pipeline, then try Hevo. Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs.
Hevo’s native integration with Google Cloud offerings like MySQL, PostgreSQL, and MSSQL Server ensures you can move your Google Cloud data without the need to write complex ETL scripts. You can also have a look at the unbeatable Hevo pricing that will help you choose the right plan for your business needs.
Want to take Hevo for a spin? Sign up for a 14-day free trial and start replicating your google cloud data with the feature-rich Hevo suite firsthand.
Share your experience of using the best Google Cloud ETL tools in the comment section below.
Driven by a problem-solving ethos and guided by analytical thinking, Oshi is a freelance writer who delves into the intricacies of data integration and analysis. He offers meticulously researched content essential for solving problems of businesses in the data industry.