8 Best Google Analytics (GA4) ETL Tools to Consider in 2025

Google Analytics provides exceptional report generation based on customer interactions, but there are times when more detailed and nuanced data analysis is needed. To dive deeper into your data and extract meaningful insights, leveraging specialized ETL tools can make a significant difference. These tools facilitate the extraction, transformation, and loading of data, allowing for enhanced analysis and reporting.

In this blog, we will explore some of the best ETL tools for Google Analytics, designed to help you unlock deeper insights and make data-driven decisions more effectively.

Overview of Google Analytics

Google Analytics is a comprehensive web analytics service offered by Google that tracks and reports on website traffic and user interactions. It provides valuable insights into how users engage with your website or app, helping businesses make data-driven decisions to improve their digital presence and marketing strategies.

Key Use Cases

• Traffic Analysis: It can be used to monitor and analyze website traffic, including the number of visitors, page views, and session durations.
• Audience Insights: You can gather demographic and geographic information about users, including age, gender, and location.
• Conversion Tracking: You can use it to track and measure conversions such as purchases, form submissions, or other key actions.

8 Best Google Analytics ETL Tools

Here, we have curated a list of the top 8 Google Analytics ETL tools:

1. Hevo Data

Hevo Data is a cloud-native ELT/ETL platform that lets teams ingest and route data from multiple sources into a unified data warehouse. It’s designed to handle the backend pipeline setup, so users can focus on analyzing data instead of managing connectors.

When you use GA4 as a source, Hevo can continuously replicate event-level and property-level data into your preferred destination (e.g., BigQuery, Snowflake, Redshift). Its near-real-time ingestion ensures minimal latency between GA4 collection and downstream analytics.

Hevo’s event-centric ETL architecture is designed to accommodate GA4’s dynamic data model. With automated metadata management, Hevo keeps your GA4 data structured, consistent, and analytics-ready at all times. It intelligently handles schema evolution, ensuring that new events or parameters don’t break existing pipelines.

Key features:

• No-code pipeline setup: Hevo enables you to configure data pipelines through intuitive GUI forms and dropdowns, eliminating the need for coding. It reduces friction when onboarding new GA4 properties across multiple clients or business units.
• Pre-built connector library: Hevo offers over 150 pre-built connectors across various databases, APIs, and SaaS systems, including GA4 as a data source. The extensive library minimizes the engineering effort of building and maintaining custom GA4 connectors.
• Native GA4 connector: Hevo can ingest GA4 data directly using the Google Analytics Data API, making it compatible with the schema and metrics exposed by GA4. You can extract event-level data, dimensions, and metrics without relying on intermediary exports.
• Real-time data sync: Hevo enables near-real-time GA4 data replication with sync intervals. Hevo’s low-latency pipelines keep data consistent for timely reporting and insights.
• In-flight transformations: Hevo supports in-flight formatting before loading. You can apply Python-based or drag-and-drop transformations to shape GA4 event data.

Pros:

• Supports both historical and incremental data loads.
• Customizable data transformation during pipeline execution.
• Strong data monitoring and alerting features.
• Scales seamlessly with growing data needs.
• Automatic schema mapping and change detection.

Pricing:

• Starts as a free tier with limited connectors up to 1 million events
• Starter: $239/month up to 5 million events
• Professional: $679/month up to 20 million events
• Business: Custom pricing

2. Talend

Talend (owned by Qlik) is an open-source, robust ETL and data integration platform designed to extract, transform, and load data from multiple sources, including Google Analytics 4 (GA4), into data warehouses like BigQuery, Snowflake, or Redshift.

Talend connects through Google’s APIs to extract granular event, session, and user-level data. It transforms raw GA4 data into structured, analytics-ready formats, handling complex schema mappings, timestamp conversions, and user property normalization.

What makes Talend stand out for GA4 data pipelines is its flexibility and governance strength. Its ability to blend GA4 data with CRM, ad platform, or eCommerce data gives businesses end-to-end visibility into user behavior across channels.

Key features:

• Visual job design: Talend’s ETL job design is visual, enabling users to map, join, filter, and transform data flows without writing code. For GA4 ETL, this accelerates pipeline prototyping and iteration.
• Data governance: Talend embeds data quality checks and governance rules as part of the integration process, catching anomalies early. You can enforce metric consistency and flag irregular changes before downstream consumption.
• Hybrid deployment: Talend supports deployment on-prem, in private clouds, or fully in the public cloud, along with hybrid models. That lets GA4 ETL pipelines run close to your data platform or warehouse without being tied to a single environment.

Pros:

• An extensive connector library simplifies GA4 data extraction.
• Handles large data volumes with a modular architecture.
• Modular architecture supports both batch and streaming workflows.

Cons:

• Steep learning curve for non-technical users initially.
• Enterprise-level features require costly licensing tiers.
• Documentation inconsistency across versions complicates adoption.

Pricing:

Follows a consumption-based model that depends on data volume, number of job runs, and execution time.

3. Apache Spark

Apache Spark is an open-source, distributed analytics engine designed for large-scale data processing, supporting APIs in Scala, Java, Python, and SQL. Spark’s core abstractions (DataFrame, Dataset, Structured Streaming) make it a go-to platform for data engineering pipelines.

Spark can ingest GA4 data via connectors (such as a JDBC driver) to pull raw event and dimension data into its processing engine. The tool writes the refined GA4 data into downstream systems (e.g., data warehouse, data lake, BI stores) with high throughput and fault tolerance.

Spark supports both real-time and batch processing, allowing GA4 pipelines to scale with growing data needs. Moreover, with GA4-specific JDBC bridges, Spark pushes down supported operations to Google’s API, minimizing data transfer and accelerating transformations.

Key features:

• PySpark support: Apache Spark offers PySpark to build scalable ETL pipelines. PySpark ensures that GA4 data transformations, aggregations, and analyses can be executed in distributed environments with minimal overhead.
• Connector support: Apache Spark can integrate with GA4 using connectors like the CData Google Analytics Connector. These connectors facilitate the extraction of GA4 data, enabling seamless ETL workflows.
• ML capabilities: With built-in libraries for machine learning and graph processing, Spark allows users to perform advanced analytics on GA4 data for predictive modeling.

Pros:

• Offers APIs in Python, Scala, Java, and R.
• Compatible with cloud and big data platforms.
• High-speed in-memory computation for faster analytics.

Cons:

• Requires skilled developers for complex transformations.
• Debugging distributed jobs can be challenging.
• Initial cluster setup can be time-consuming.

Pricing:

Apache Spark is free and open-source software.

4. Informatica PowerCenter

Informatica PowerCenter is an enterprise data integration platform designed to move, transform, and orchestrate GA4 data pipelines across systems. It integrates with both on-premises and cloud environments.

PowerCenter can extract raw event-level GA4 exports, apply complex transformation logic, and load the result into target warehouses, data lakes, or BI systems. It also supports parallelism and incremental loads, which keep your GA4 datasets up to date with minimal latency.

What distinguishes Informatica PowerCenter is its enterprise-grade robustness and extensibility. It handles large data volumes, supports diverse connectors, and provides deep monitoring and metadata capabilities not typically found in lightweight ETL tools.

Key features:

• Data transformation: PowerCenter offers a robust suite of data transformation tools, including no-code cloud connectors. Users can apply complex business rules, standardize data formats, and handle hierarchical data structures, such as JSON and XML.
• Data processing: Designed for enterprise environments, PowerCenter supports high-throughput data processing. It efficiently handles large volumes of GA4 data, ensuring timely ETL operations.
• Metadata management: PowerCenter provides robust metadata management features, including lineage tracking and impact analysis. These features simplify data flow and dependencies for transparency and compliance.

Pros:

• Facilitates troubleshooting and performance tracking.
• Ensures data lineage and governance compliance.
• Flexible scheduling and real-time data handling.

Cons:

• Requires significant training and experience.
• Initial deployment can be time-consuming.
• Primarily designed for on-premises environments.

Pricing:

Informatica follows a consumption-based pricing model, charging users based on actual usage.

5. Apache NiFi

Apache NiFi is an open-source ETL platform that enables seamless extraction, transformation, and loading of Google Analytics data into data warehouses. It provides a visual, web-based interface that allows data engineers to design, monitor, and manage GA4 data flows efficiently.

By leveraging advanced processors, NiFi connects to the Google Analytics API, retrieves data, and transforms it into structured formats compatible with target systems. This capability enables organizations to centralize their analytics data, ensuring consistency and accessibility for advanced analysis.

Apache NiFi offers robust data provenance tracking, which provides a complete audit trail of every Google Analytics data point as it moves through the ETL pipeline. For organizations handling sensitive or complex analytics data, NiFi is a reliable and transparent platform.

Key features:

• Real-time data processing: NiFi supports real-time data ingestion and processing for the continuous flow of data from GA4 into your preferred destination.
• Scalable architecture: NiFi supports secure communication protocols like TLS and SSH, ensuring that data transfers are encrypted and protected. Its architecture is designed for scalability and adapts to changing workload.
• OAuth support: To connect with Google Analytics 4, NiFi utilizes OAuth 2.0 authentication for secure access to GA4 APIs.

Pros:

• Scalable architecture handles varying data volumes.
• Supports multiple data formats and protocols.
• Drag-and-drop interface for easy pipeline design.

Cons:

• Limited pre-built GA4-specific transformations.
• Requires manual configuration for advanced tasks.
• UI can become cluttered with large pipelines.

Pricing:

There are no licensing fees or commercial charges for using or deploying NiFi in your own environment.

6. Airbyte

Airbyte is an open-source ELT platform designed to streamline data integration workflows. It offers pre-built connectors for Google Analytics, enabling seamless extraction of GA4 data.

The GA4 connector in Airbyte supports both OAuth and Service Account Key Authentication methods, ensuring secure access to GA4 properties. It allows users to define replication start dates and customize data request intervals, providing flexibility in data extraction.

Airbyte’s open-source nature, combined with a robust connector ecosystem, empowers users to integrate data from various sources. This extensibility, coupled with its community-driven approach, makes Airbyte a compelling choice for organizations seeking a customizable and scalable ELT solution for GA4 data integration.

Key features:

• Custom report configuration: Users can define custom reports by specifying dimensions and metrics through JSON configurations. It enhances the granularity and relevance of the data being synchronized.
• Authentication: Airbyte supports authentication via Google Cloud service accounts, providing secure access to GA4 properties. Authentication eliminates the need for manual login processes, streamlining the setup and maintenance of data pipelines.
• PyAirbyte: Airbyte’s Python library, PyAirbyte, facilitates the extraction of GA4 data within Python environments. Integration supports advanced data processing and analytics workflows, catering to teams that prefer Python-based data pipelines.

Pros:

• Provides over 600 pre-built connectors, including GA4.
• Supports both batch and near real-time data syncing.
• AI-assisted connector creation.

Cons:

• Debugging can be difficult.
• Requires technical expertise for optimal use.
• Some connectors are in early development stages.

Pricing:

Airbyte offers a free self-hosted version, a 14-day trial for its cloud platform, and flexible Team and Enterprise plans tailored to specific business needs.

7. Stitch Data

Stitch (owned by Qlik) is a cloud-based ETL platform designed to streamline the process of extracting, transforming, and loading (ETL) data from Google Analytics 4 into cloud data warehouses like Snowflake, Google BigQuery, and Amazon Redshift.

Stitch enables the extraction of GA4 reports through selected metrics and dimensions. The platform supports advanced scheduling options, including anchor and cron-based scheduling, to automate data replication, ensuring timely access to up-to-date datasets.

Stitch’s integration with GA4 leverages the Singer open-source framework, providing a standardized and extensible approach to data extraction. Stitch’s lightweight and developer-friendly interface makes it a compelling choice for organizations seeking a straightforward ETL solution for their GA4 data integration needs.

Key features:

• No-code setup: The platform offers a no-code interface for setting up data pipelines, making it accessible to users without technical expertise. The tool simplifies the ETL process, reducing the need for specialized coding skills.
• Seamless integration: Stitch Data integrates effortlessly with various data warehouses, ensuring that GA4 data is centralized for in-depth analysis. Integration supports efficient data management and accessibility across platforms.
• Data replication: Stitch Data supports advanced scheduling options for data replication, allowing users to automate data extraction at specified intervals.

Pros:

• Active maintenance with regular updates.
• Offers transparent pricing models.
• Provides detailed extraction logs.

Cons:

• Limited advanced transformation capabilities.
• Limited customization for complex workflows.
• No support for self-hosting options.

Pricing:

Stitch uses a usage-based pricing model, primarily determined by the number of data rows processed and transferred each month.

• Standard: $100 monthly
• Advanced: $1,250 monthly
• Premium: $2,500 monthly

8. Integrate.io

Integrate.io is a low-code ETL platform designed to streamline data integration processes. It offers native connectors for Google Analytics, enabling seamless extraction of web analytics data.

The platform simplifies the ETL workflow by providing pre-built components for data extraction, transformation, and loading. Users can configure data pipelines through an intuitive interface to accelerate the process of consolidating GA4 data with BI sources, enhancing analytical capabilities.

Integrate.io’s flexible transformation capabilities allow users to apply custom data cleaning and normalization rules to GA4 data as it moves through the pipeline. This flexibility helps analytics teams tailor the ETL process to meet specific requirements by turning raw GA4 metrics into actionable insights with precision and control.

Key features:

• Low-code data transformation: The platform provides a low-code environment for data transformation, allowing users to clean, normalize, and enrich GA4 data without extensive coding.
• Real-time data replication: Integrate.io supports real-time data replication with sub-60-second latency for near-instantaneous updates of GA4 data in data warehouses. Minimal latency powers real-time dashboards and AI/ML initiatives.
• Bidirectional data sync: Integrate.io provides bidirectional data synchronization capabilities, facilitating seamless transfer of data between GA4 and other systems. Data is consistently updated across platforms, enhancing data consistency and accuracy.

Pros:

• Intuitive design simplifies complex data workflows.
• Offers round-the-clock assistance via multiple channels.
• Enables automatic API creation from datasets.

Cons:

• Complicated workflows can be difficult to manage.
• Error logs can be challenging to interpret.
• No on-premise deployment option.

Pricing:

Starting at $1,999 per month, the plan provides complete platform access, 60-second pipeline refresh intervals, and unlimited connectors. Users can also customize their plan and add extra features based on specific needs.

How To Choose The ETL Tool For Google Analytics?

Choosing the appropriate ETL tool to perform the analysis will solve half of the problem. We have listed a few aspects to help you decide.

1. Easy to Install/Integrate: An ETL tool should be user-friendly that can easily integrate with the existing architecture. Cloud-based tools are most beneficial in terms of integration and deployment of the ETL pipeline to production.
2. Monitoring and Management: ETL tools perform a continuous operation on the pipeline, which needs to be monitored and controlled. It should have proper supervision and controlling mechanisms to send alerts in case of failure.
3. Wide Range of Data Transformation: Any ETL tool’s critical aspect is to possess vast libraries of functions to perform calculations and transform the data from various sources to derive the analytics when loaded into the data warehouse. Business entirely depends on the metrics that denote success or failure.
4. Real-Time ETL Streaming: In this fast-paced world, everyone wants to analyze the data in real-time or near real-time. A right ETL should have the capability to generate reports on analysis in real-time. 
5. Data Security and Reliability: This is a crucial feature to consider to provide security to the data over the internet. Hence, the ETL tool should hold the data securely and be able to reliably load data to your target database and recover from unexpected failures.

Read More About: Moving Data From Google Analytics To Data Warehouse

Conclusion

• There are various options available in the market when it comes to Google Analytics ETL tools. Each of them has a set of features and related pros and cons.
• When it comes to fully managed ETL, you can’t find a better alternative than Hevo. It is a No-code Data Pipeline product that will help you move data from 150+ data sources including 40+ Free sources such as Google Analytics to your destination. It is straightforward to set up as you can get the tool up and running in a jiffy.
• Enhance your Google Analytics reporting with Hevo Data by following our comprehensive guide, which offers clear and actionable steps for seamless data integration and improved analytics.

FAQs on Google Analytics ETL