10 GCP ETL Tools to Consider in 2025

Your data doesn’t live in one place. Orders stream from apps. Events fire through Pub/Sub. CSVs land in Cloud Storage. Marketing metrics sit in half a dozen SaaS tools. To use any of it, you need a repeatable way to pull it together, clean it up, and deliver it to wherever the work happens. That’s ETL: extract, transform, load.

The right mix depends on your team’s skills, how fast you need data, and how much code you want to write. If you’re new to data or exploring how AI fits into your business, the options can feel overwhelming.

This guide cuts through the noise. We highlight 10 GCP‑ready ETL options to consider in 2025—what they do, where they fit, and who tends to adopt them. You’ll also get a quick primer on ETL benefits, the features that matter, and a simple way to choose. No hype. Just practical context so you can pick a starting point and move data with confidence.

What is an ETL tool?

ETL stands for Extract, Transform, and Load—three critical steps in preparing data for analysis:

• Extract: Pull data from various sources, such as CRMs, databases, APIs, or SaaS applications.
• Transform: Cleanse, enrich, and format the data so it’s usable and consistent.
• Load: Move the transformed data into a target system, such as a data warehouse or data lake.

ETL tools automate this process for businesses to integrate structured and unstructured data from multiple systems into a single source of truth. With growing volumes of real-time data and an expanding number of sources, ETL platforms make it possible to prepare data quickly, accurately, and at scale.

Benefits of using ETL tools

Implementing a modern ETL solution offers a wide range of business and operational advantages:

• Automated workflows: Reduce manual data prep and free up time for analysis.
• Real-time insights: Enable up-to-date data flow for dashboards, machine learning models, and operations.
• Improved data quality: Cleansing and standardization improve consistency and accuracy.
• Scalability: Designed to handle growing data volumes and evolving business demands.
• Governance and compliance: Ensure secure and auditable data practices across teams.

Recent industry research underscores why modern, cloud‑native ETL matters: Organizations report sharp growth in AI use and the need for reliable, governed data to power it. For example, McKinsey’s 2024 Global AI Survey finds 71 percent of companies are now using generative AI in at least one business function, up six percentage points from a year earlier.

Key features to look for in a GCP ETL tool

When evaluating ETL platforms for your GCP ecosystem, look for the following capabilities:

• Native GCP integration: Built-in connectors for services like BigQuery, Cloud Storage, and Pub/Sub.
• Real-time + batch support: The ability to support streaming and scheduled data workflows.
• Visual interface or no-code support: Empowers more team members to build pipelines.
• Data transformation logic: Robust data cleansing, mapping, and transformation features.
• Pipeline orchestration: Scheduling, dependency management, and workflow automation.
• Monitoring and alerting: Track pipeline health, latency, and job success rates.
  
• Security and compliance: Support for data encryption, access controls, and logging

The 10 best GCP ETL tools to consider in 2025

1. Domo

A cloud-native data platform that combines data integration, transformation, analytics, and AI. Using Domo’s Magic ETL lets teams build pipelines with a drag‑and‑drop canvas, while DataFlows support SQL- and Python-based transforms when you want code.

How Domo works on GCP

Domo connects to Google Cloud services and applications then moves data back into Domo for transformation and analysis or writes refined outputs back to destinations. Works alongside BigQuery when you prefer to keep query processing in your warehouse.

Strengths of Domo

• Visual pipeline design with versioning and reusable tiles
• No-code/low-code to pro‑code path (Magic ETL, SQL, Python)
• Built-in governance, alerts, and sharing so insights reach business teams fast
• End‑to‑end flow—from ingest to dashboards—that reduces tool sprawl

Typical use cases

Marketing and sales reporting, operations dashboards, executive scorecards, near‑real‑time KPI alerts, and AI-assisted analysis with Domo AI.

Good fit for

Organizations that want one place to prepare data and publish actionable dashboards without stitching multiple tools.

2. Cloud Data Fusion

A fully managed, visual data integration service on Google Cloud built on CDAP. It helps you design, deploy, and monitor batch or streaming pipelines without standing up infrastructure.

How it works on GCP

Pipelines run on GCP services, like Dataproc or Dataflow, depending on your design. Native plugins connect to Cloud Storage, BigQuery, Pub/Sub, and common SaaS sources.

Strengths 

• Visual authoring with reusable plugins and wrangling
• Managed runtime with centralized lineage and monitoring
• Hybrid connectivity for on‑prem and cloud

Typical use cases

Data onboarding to BigQuery, standardizing data sets across business units, and building curated data marts.

Good fit for

Teams that want a managed, GCP‑native integration layer with visual design and governance.

3. Cloud Dataflow

A serverless data processing service for streaming and batch, based on Apache Beam. It separates pipeline logic from execution so you can run the same code for real‑time and scheduled jobs.

How it works on GCP

You author Beam pipelines (Java/Python). Dataflow handles autoscaling, windowing for streams, stateful processing, and fault tolerance. Tight integration with Pub/Sub, BigQuery, and Cloud Storage.

Strengths 

• Unified stream + batch model
• Strong for event processing, change‑data capture (CDC), and enrichment
• Autoscaling workers to handle bursts

Typical use cases

Clickstream processing, IoT telemetry, fraud detection features, and up-to-date marketing attribution.

Good fit for

Data engineering teams that want code‑driven pipelines with fine‑grained control over streaming semantics.

4. Cloud Dataproc

A managed Spark, Hadoop, and Hive service. Spin up ephemeral or long‑running clusters to run big data transforms using open‑source tools you already know.

How it works on GCP

Use templates or custom images; integrate with Cloud Storage for cost‑efficient data lakes and BigQuery for warehousing. Autoscaling and job orchestration optimize cost and performance.

Strengths 

• Familiar OSS ecosystem (Spark SQL, PySpark, Hive)
• Suited to large‑scale joins, ML feature prep, and complex batch jobs
• Per‑job clusters for isolation and predictable spend

Typical use cases

Historical reprocessing, feature engineering at scale, and data lake curation.

Good fit for

Teams with Spark/Hadoop skills that want managed clusters without managing YARN or HDFS.

5. Cloud Pub/Sub

A global messaging service for event ingestion and delivery. It’s the backbone for streaming ETL—decoupling producers and consumers with durable topics and subscriptions.

How it works on GCP

Publish events from apps or services; subscribe with Dataflow, Cloud Functions, or custom consumers. Exactly‑once processing with Dataflow reduces duplicates downstream.

Strengths

• High throughput, low latency delivery
• Replayable subscriptions and dead‑letter queues
• Regional and global topologies

Typical use cases

Event‑driven pipelines, microservices communication, log aggregation, and near‑instant analytics.

Good fit for

Teams building streaming ETL or event architectures that need reliable delivery and scale.

6. Cloud Composer

A managed Apache Airflow service for orchestration. Define Directed Acyclic Graphs (DAGs) to schedule and coordinate multi‑step pipelines.

How it works on GCP

Uses GKE under the hood, with operators for BigQuery, Dataflow, Dataproc, Cloud Storage, and more. Centralized logging and monitoring help track job health.

Strengths 

• Python‑based DAGs for reproducible workflows
• Rich library of GCP operators and sensors
• Cross‑tool orchestration across GCP and external systems

Typical use cases

Daily warehouse loads, data quality checks, dependency management across ingestion, transform, and publish steps.

Good fit for

Teams that need enterprise scheduling and governance across many jobs and environments.

7. Talend on GCP

An enterprise data integration and data quality suite with visual design, strong governance, and broad connectivity.

How it works on GCP

Prebuilt connectors for BigQuery, Cloud Storage, and Pub/Sub. Jobs can run on Talend’s managed runtime or on your cloud infrastructure, feeding curated data sets into BigQuery.

Strengths 

• Built‑in data quality, profiling, and stewardship
• Robust metadata management and lineage
• Large connector ecosystem for hybrid arrangements

Typical use cases

Regulatory reporting, master data consolidation, standardized pipelines across business units.

Good fit for

Enterprises that value governance and quality controls alongside integration.

8. Fivetran for BigQuery

An automated ELT service that keeps source systems and BigQuery in sync with managed connectors and schema evolution.

How it works on GCP

Choose a connector, authorize, and select tables; Fivetran handles ingestion, incremental updates, and normalization into BigQuery.

Strengths 

• Fast setup with minimal maintenance
• Broad SaaS coverage (marketing, sales, finance)
• Useful for centralizing analytics data quickly

Typical use cases

Marketing attribution models, revenue dashboards, product analytics with event data.

Good fit for

Teams that want turnkey data movement into BigQuery with low overhead.

9. Stitch (Talend Stitch)

A lightweight ELT platform designed for quick setup and developer‑friendly control.

How it works on GCP

Connect sources with prebuilt integrations; load raw data into BigQuery where you can transform with SQL or external tools.

Strengths 

• Straightforward pipeline setup
• Clear pricing and modular workflows
• Good for smaller teams that iterate quickly

Typical use cases

Startup analytics stacks, point‑solution reporting, and proof‑of‑concept pipelines.

Good fit for

Teams that want a simple path to land data in BigQuery for downstream modeling.

10. Hevo Data

A no‑code data pipeline platform for real‑time and batch ingestion with built‑in transformations.

How it works on GCP

Prebuilt connectors stream or batch‑load data into BigQuery and Cloud Storage. Transformations can run in‑flight or post‑load.

Strengths 

• Real‑time sync options for operational dashboards
• Visual transforms and data mapping
• Helpful for teams standardizing many SaaS feeds

Typical use cases

Operational analytics, customer 360 views, and near‑real‑time reporting.

Good fit for

Business and analytics teams seeking no‑code pipelines with streaming support.

Choosing the right ETL tool for your GCP environment

Picking a tool is about more than features in isolation—it’s about fit. Use the checklist below to align options with your data, people, and pace of change.

Start with outcomes

Define what “good” looks like. Are you optimizing for near‑real‑time signals, daily batch reliability, or governed self‑service analytics? Write down the SLAs you’re seeking for latency, freshness, and uptime.

Map the workload

• Data sources and gravity: Where does data originate (apps, SaaS, on‑prem)? Will you transform inside BigQuery (ELT) or outside then load (ETL)?
• Processing mode: Streaming, micro‑batch, or scheduled batch.
• Complexity: Simple replication vs heavy joins, aggregations, and feature engineering.
• Volume and burstiness: Average vs peak throughput; expected growth over 12–24 months.

Match tool to team

• No‑code/low-code first: Data Fusion, Domo, Integrate.io, and Hevo help non‑engineers contribute quickly.
• Code‑driven: Dataflow (Beam) and Dataproc (Spark/Hadoop) suit engineering teams that want full control.
• Orchestration: Composer (Airflow) coordinates multi‑step jobs across services.

Prioritize governance and quality

Look for lineage, profiling, validation, and stewardship. Talend is strong here; Data Fusion provides managed lineage across pipelines. Whatever you choose, standardize on naming, environments, and promotion paths (dev → test → prod).

Security and compliance

Evaluate IAM roles, VPC‑SC support, encryption in transit/at rest, PII handling, audit logs, and how secrets are managed. Confirm that vendors support your regulatory requirements, including measures like HIPAA, SOC 2, or GDPR.

Connectivity and extensibility

Catalog required connectors (databases, APIs, files, SaaS) and verify directionality (ingest + writeback). Check SDKs, webhooks, and support for custom transforms (SQL/Python) so you don’t hit a ceiling.

Scalability and performance

Understand how each service scales (serverless autoscaling vs cluster sizing), concurrency limits, and back‑pressure behavior in streams. For BigQuery‑centric stacks, consider pushing more work into SQL to reduce data movement.

Cost model and operations

Model TCO across compute, storage, egress, and licensing. Ask for cost guardrails: autoscaling limits, job timeouts, partitioning strategies, and incremental load patterns. Plan for on‑call, alerting, and SLAs.

Common patterns that work

• GCP‑native streaming analytics: Pub/Sub → Dataflow → BigQuery. Great for clickstream, IoT, or fraud features where seconds matter.
• Visual, governed integration: Data Fusion → BigQuery with Composer for orchestration. Useful when multiple teams contribute pipelines and you want centralized lineage.
• Big data transformations: Cloud Storage data lake → Dataproc (Spark) → BigQuery. Suits large joins, historical reprocessing, or ML feature prep at scale.
• End‑to‑end analytics: Connector(s) → Domo Magic ETL → dashboards and alerts, with options to push results back to apps. Helpful when you want prep + BI + sharing in one place.

Final Thoughts

In the era of real-time analytics and AI-driven decision-making, choosing the right ETL tool is foundational to your data strategy. Google Cloud offers a powerful suite of ETL tools, both native and partner-based, that can help your organization build robust, scalable, and efficient data pipelines.

Whether you’re just beginning your analytics journey or refining a mature data infrastructure, these 10 GCP ETL tools provide flexible and future-ready options for 2025 and beyond.

Ready to unify your data and act on it in real time? Explore how Domo’s data platform can help you turn data into impact—no matter where it lives. Start free today