Best ETL Tools in 2025

Choosing an ETL tool in 2025 isn’t just about moving data, it’s about syncing teams, scaling infrastructure, and enabling fast decisions. Whether you're building pipelines as a data engineer or looking to streamline reporting as an analyst, the ETL space is full of great tools, each with their own strengths.

What is ETL?

ETL stands for Extract, Transform, Load - a core data process used to bring together information from different systems and make it usable for reporting, analysis, and automation. It’s a foundation of modern data infrastructure and one of the most important building blocks for any business working with data.

Here’s how it works:

• Extract: First, data is pulled (or extracted) from various source systems. These can be anything from marketing platforms like HubSpot or Meta Ads to e-commerce tools like Shopify or Stripe. At this stage, the data is often messy, siloed, and structured in different ways depending on the tool.
• Transform: Once the raw data is extracted, it needs to be cleaned, restructured, and transformed. This could mean joining tables, renaming columns, fixing data types, or calculating new fields. The goal is to turn inconsistent data into something usable and trustworthy — often using tools like SQL or visual transformation layers.
• Load: Finally, the transformed data is loaded into a centralized data warehouse, such as Snowflake, BigQuery, or Redshift. Once it’s there, it becomes a single source of truth that teams can use for dashboards, reports, or powering automations.

Another version of this process is often called ELT, which flips the last two steps, bringing raw data into the warehouse first, then doing all the transformations inside the warehouse itself. This approach is common with today’s cloud-native tools because it’s faster, more flexible, and easier to scale.

Many companies also go a step further by enabling reverse ETL, which sends data back from the warehouse into everyday business tools. This means sales teams can see enriched customer data in their CRM, marketing teams can create better segments, and finance teams can automate reporting, all using the same trusted data.

In short, ETL helps you go from raw, scattered information to reliable, actionable insights. And the right ETL tool can save hours of manual work, reduce errors, and enable better decisions across the business.

Below you'll find a complete list of the top ETL tools in 2025, along with pros, cons, fresh reviews, and a final comparison to help you choose the right one.

Top ETL tools listed

1. Weld

Weld is a modern data platform built for teams that want to move fast without sacrificing clarity. It combines both ETL and reverse ETL in a single interface — letting you sync data from 200+ tools like Shopify, HubSpot, and Stripe into your warehouse, then push clean, modeled data back into business tools.

Weld stands out with a fixed monthly pricing model, minimal engineering setup, and an intuitive UI designed for both data teams and business users. It’s a great option for companies that want to get up and running quickly, without managing complex infrastructure.

🔗 weld.app

📝 G2 reviews

Pros:

• ETL + reverse ETL in one
• Flat monthly pricing model
• 200+ connectors (Shopify, HubSpot, etc.)
• AI-powered metric creation

Cons:

• Limited deep customization for complex pipelines
• Focused on cloud data warehouses

2. Airbyte

Airbyte is an open-source ETL platform with hundreds of prebuilt connectors and flexibility for teams who want to self-host or build their own.

🔗 airbyte.com

📝 G2 Reviews

Pros:

• 550+ connectors
• Open-source + managed cloud version
• Capacity-based pricing (2025)
• Python SDK & low-code connector builder

Cons:

• Self-hosted version requires dev resources
• UI less polished than Fivetran

3. Fivetran

Fivetran is a fully managed platform that automates the entire data pipeline and is widely used by enterprise teams who value ease over flexibility.

🔗 fivetran.com

📝 G2 reviews

Pros:

• Fully automated
• 500+ prebuilt connectors
• Schema drift handling

Cons:

• Expensive, per-connection MAR pricing
• Limited transformation flexibility 📝 G2 Reviews

4. Hevo Data

Hevo is a no-code data pipeline tool that's great for startups and growing teams wanting real-time syncs with minimal setup.

🔗 hevodata.com

📝 G2 Reviews

Pros:

• Real-time syncing
• No-code setup
• Easy onboarding

Cons:

• Limited transformations
• Smaller connector library

5. Estuary

Estuary focuses on real-time, event-driven pipelines and is ideal for modern data engineering teams building around streaming use cases.

🔗 estuary.dev

📝 G2 Reviews

Pros:

• Real-time data sync
• Change Data Capture (CDC) support
• Easy UI for event workflows

Cons:

• Smaller community
• Requires event-oriented thinking

6. Matillion

Matillion is an ELT platform with a drag-and-drop interface and strong support for Snowflake, BigQuery, and other enterprise warehouses.

🔗 matillion.com

📝 Gartner Reviews

Pros:

• Visual UI for data pipelines
• dbt integration
• Scalable for enterprise workloads

Cons:

• Usage-based pricing can spike
• Higher learning curve for small teams

7. Segment

Segment is a customer data platform with strong event tracking, ETL-lite features, and native support for marketing and product analytics stacks.

🔗 segment.com

📝 G2 Reviews

Pros:

• Great for behavioral data
• 300+ integrations
• Built-in user identity resolution

Cons:

• Not a full ETL solution
• Expensive at scale

8. Keboola

Keboola is a modular data operations platform with pipeline orchestration, transformations, and Git-based version control.

🔗 keboola.com

📝 G2 Reviews

Pros:

• Git integration
• Transformations + orchestration built-in
• Modular architecture

Cons:

• Less intuitive for beginners
• Smaller user base

9. Talend

Talend is one of the most mature enterprise data integration tools on the market, with both open-source and commercial offerings.

🔗 talend.com

📝 Gartner Reviews

Pros:

• Great governance features
• Hybrid deployment options
• Enterprise-ready

Cons:

• UI feels dated
• Higher complexity

10. Meltano

Meltano is an open-source ELT platform built for engineers and GitOps workflows, powered by Singer taps and focused on developer control.

🔗 meltano.com

📝 G2 Reviews

Pros:

• CLI-first + version-controlled
• Open-source & modular
• Dev-friendly for custom pipelines

Cons:

• Steep learning curve for non-devs
• Requires manual deployment

11. Rivery

Rivery offers both ETL and reverse ETL with strong orchestration and support for SQL + Python transformations.

🔗 rivery.io

📝 G2 Reviews

Pros:

• Code + no-code hybrid
• Supports reverse ETL
• Flexible pricing

Cons:

• UI can be overwhelming
• Smaller community

12. Azure Data Factory

ADF is Microsoft’s cloud-native ETL tool built for Azure-first teams with extensive integrations across the Microsoft ecosystem.

🔗 azure.microsoft.com

📝 Gartner Reviews

Pros:

• Scales well in Azure environments
• Rich native connectors
• SSIS support

Cons:

• Complex interface
• Azure-specific

13. AWS Glue

AWS Glue is Amazon’s fully managed serverless ETL platform, ideal for structured and semi-structured data within the AWS ecosystem.

🔗 aws.amazon.com/glue

📝 G2 Reviews

Pros:

• Serverless and auto-scaling
• Strong Glue Data Catalog
• Deep AWS integration

Cons:

• Hard to debug
• Steep learning curve

14. Skyvia

Skyvia is a no-code integration tool best suited for data replication, backup, and small-team reporting needs.

🔗 skyvia.com

📝 G2 Reviews

Pros:

• Easy to use
• Cloud-based
• Affordable plans

Cons:

• Basic transformation support
• Limited monitoring

15. Portable.io

Portable.io focuses on long-tail connector support and makes it easy for teams to integrate niche apps quickly.

🔗 portable.io

📝 G2 Reviews

Pros:

• Fast connector deployment
• Handles obscure data sources
• Lightweight setup

Cons:

• Smaller user base
• Less built-in transformation logic

16. Integrate.io

Formerly Xplenty, Integrate.io is a visual ETL tool that balances ease of use with flexibility across sources and destinations.

🔗 integrate.io

📝 G2 Reviews

Pros:

• Visual pipeline designer
• Supports scheduling and triggers
• Connects to most popular warehouses

Cons:

• Limited transparency in pricing
• Users report UI limitations

17. Dataddo

Dataddo is a no-code data integration platform geared toward business users and marketers.

🔗 dataddo.com

📝 G2 Reviews

Pros:

• Simple setup
• Affordable pricing
• Reverse ETL support

Cons:

• Not made for deep engineering use cases
• Less control over pipeline logic

18. dltHub

dltHub is a Python-native ELT tool that makes it easy to build maintainable pipelines directly in code.

🔗 dlthub.com

📝 G2 Reviews

📖 User Experience Review

Pros:

• Built for Python teams
• Code-first with open-source backing
• Great for custom setups

Cons:

• No GUI
• Still maturing

19. Rudderstack

RudderStack is an open-source, customer data platform for developers. It allows you to collect and deliver customer event data to a variety of destinations across your growth, product, and marketing stack.

🔗 https://www.rudderstack.com/

📝 [G2 Reviews] (https://www.g2.com/products/rudderstack/reviews)

Pros:

• Developer-focused and highly flexible
• Reliable event capture and fast warehouse integration
• Robust support and onboarding for SMBs/mid-market

Cons:

• Less intuitive for non-tech users
• Reverse ETL and cohort building still lag competitors like Hightouch
• Documentation and alerts need improvement, some users report steep onboarding

20. Informatica

Informatica is an enterprise-grade ETL tool known for its data integration, data quality, and data governance solutions. It has been named a leader by Gartner for data integration. It offers a comprehensive suite for data integration, data quality, and data governance, and supports cloud and on-premises deployments

🔗 https://informatica.com

📝 [Gartner reviews] (https://www.gartner.com/reviews/market/data-preparation-tools/vendor/informatica)

Pros:

• Enterprise-grade capabilities
• Strong data quality features
• Cloud integration; scalable.
• More than 1,200 pre-built connectors

Cons:

• Can be expensive for small to mid-sized businesses
• Complex features and architecture require specialized skills
• Can demand significant infrastructure and setup

21. CloverDx

CloverDX is an enterprise-grade ETL/ELT platform that emphasizes flexibility, automation, and scalability in designing complex data workflows. It supports both code-based and GUI-driven development, making it suitable for both developers and data engineers. It is also known for its transformation, data quality, and data migration capabilities. CloverDX helps to deliver a seamless onboarding process for clients, saving hours of manual work with automated conversion from any file format.

🔗 https://www.cloverdx.com/

📝 Gartner Reviews

Pros:

• AI enabled transformations
• User-friendly, self-service interface for business users
• The platform handles the entire data life cycle, covering data ingestion, processing, delivery, and consumption

Cons:

• Licensing costs
• May require a steeper learning curve compared to simpler/no-code ETL tools/ requires expertise for complex transformations

22. Microsoft SQL Server Integration Services (SSIS)

SQL Server Integration Services is a platform for building enterprise-level data integration and data transformations solutions. It helps manage ETL processes. Use Integration Services to solve complex business problems. For example: Copy or download files, load data warehouses, cleanse and mine data and manage SQL Server objects and data.

🔗 https://learn.microsoft.com/sql-server-integration-services

📝 Trustradius reviews

Pros:

• Works natively with SQL Server, Azure, and Power BI (ideal for Microsoft-centric environments)
• Perform complex transformations to prepare high-quality, analytics-ready data
• Integrate advanced logic using C# or VB.NET for tailored data processing
• Built-in features for logging, error capture, and process recovery ensure reliable performance

Cons:

• Comes with only 20+ connectors; others (e.g., Oracle, SAP BI) require separate downloads
• Limited or no native support for non-Windows platforms or cloud-native workflows
• The SSIS GUI (in Visual Studio) can be unintuitive for new users, and it may require strong understanding of SQL Server and the SSIS architecture
• Designed for batch ETL processes; real-time data integration is not natively supported

24. IBM InfoSphere DataStage

IBM DataStage is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak for Data offers automated integration capabilities in a hybrid or multicloud environment.

🔗 https://www.ibm.com/products/datastage

📝G2 reviews

Pros:

• Advanced mapping tools as a key strength, particularly within banking environments
• The platform’s reporting capabilities are highly valued for their role in simplifying user data and account management

Cons:

• It offers slightly fewer than 100 connectors and has limited native support for modern SaaS applications
• IBM InfoSphere DataStage carries a significant cost, with pricing typically tailored to the organization's size and needs
• While powerful, DataStage is complex and may require substantial ramp-up time, especially for smaller teams or users unfamiliar with enterprise-grade ETL tools
• Setting up and managing DataStage can be time-intensive, often requiring specialized expertise to configure and maintain performance across large, multi-platform environments

25. Oracle Data Integrator (ODI)

Oracle Data Integrator (ODI) is a comprehensive data integration platform.  It provides a unified solution for building, deploying, and managing data warehouses and other data-centric architectures, including those used in Service-Oriented Architecture (SOA) and business intelligence environments. ODI focuses on high-volume, high-performance data integration, event-driven integration, and SOA-enabled data services. 

🔗 https://oracle.com/dataintegrator

📝 G2 reviews

Pros:

• ODI supports a wide range of data sources and targets, including both Oracle and non-Oracle technologies
• ODI utilizes Knowledge Modules (KMs) that enable reusable integration strategies, simplifying development and maintenance
• ODI provides robust error monitoring capabilities, aiding in the identification and resolution of issues during data integration processes

Cons:

• ODI integrates best with Oracle technologies; setup can be less intuitive for non-Oracle environments and requires additional configuration (e.g., drivers for Snowflake or adapters for applications)
• While cost-effective for large deployments, the licensing and implementation costs can be a barrier for smaller projects or organizations with limited budgets
• ODI is primarily designed for batch data integration and may not suit real-time or near real-time data integration needs

26. SAP Data Services

SAP Data Services is an enterprise-grade data integration, ETL, and data quality platform. It’s part of SAP Business Technology Platform and functions on-premises or in private cloud/IaaS environments. SAP Data Services can integrate and transform structured/unstructured data from SAP and non-SAP sources (e.g. relational databases, files, big data platforms).

🔗 https://sap.com/data-services

📝 Gartner Reviews

Pros:

• It is deeply integrated with SAP systems, making it ideal for data migration and integration within the SAP ecosystem
• While some find the initial setup complex, the user interface is generally considered user-friendly, especially for those with some technical expertise
• Can handle large data volumes and is designed to scale with growing business needs

Cons:

• While cost-effective for large deployments, the licensing and implementation costs can be a barrier for smaller projects or organizations with limited budgets
• While versatile, its extensibility may be limited compared to other open-source tools, especially in non-SAP environments

27. Google Cloud Data Fusion

Cloud Data Fusion simplifies data integration by enabling businesses to build and manage pipelines that extract, transform, and load data from various sources. Its visual, user-friendly interface supports both technical and non-technical users. Its key advantages include pre-built transformations, reusable components, and support for real-time data processing. Compared to other ETL solutions, it stands out for its intuitive design, scalability, and collaborative features.

🔗 https://cloud.google.com/data-fusion

📝 Gartner Reviews

Pros:

• Foster teamwork and maintain data quality standards with a centralized platform that enables shared pipeline creation, version control, and access management
• Handle growing data volumes and evolving business needs seamlessly with a scalable architecture that accommodates both batch and real-time processing
• Seamlessly integrates with Google Cloud Services such as BigQuery, Google Cloud Storage, and Pub/Sub
• Pricing is usage-based, allowing flexibility as data demands grow

Cons:

• Reliance on pre-built plugins and connectors can restrict flexibility for highly customized or niche use cases
• Despite having a visual interface, concepts and more complex data transformations can be initially challenging for those unfamiliar with data pipelines or Apache Beam
• The pricing model can become expensive for large-scale or complex deployments, especially with large-scale data operations

28. Stitch

Stitch is a cloud-based ETL tool that simplifies data integration by extracting data from various sources and loading it into a warehouse or cloud storage. It features an easy setup, 130+ pre-built connectors, and a real-time data pipeline. Automatic schema mapping reduces manual work and errors. Its intuitive interface suits both technical and non-technical users. Stitch enables fast, reliable, and scalable data movement.

🔗 https://www.stitchdata.com/

📝 G2 Reviews

Pros:

• Stitch is built on the Singer framework, enabling users to tap into a wide range of open-source connectors. This offers flexibility for integrating data across platforms such as Meltano, Airbyte, and Estuary
• Stitch retains encrypted logs for up to 60 days, allowing users to monitor data flows and troubleshoot issues
• For those in the Qlik ecosystem, Stitch integrates seamlessly with other Qlik products, delivering a unified and streamlined data management experience

Cons:

• Stitch supports just over 140 data sources and 11 destinations, which is lower compared to other platforms. While there are over 200 singer taps in total, their quality levels vary
• Price can quickly rise quickly from basic plan to advanced ($1,250+ per month) and premium ($2,500 per month). It can also pose restrictions on the volume of data that can be processed, which can be a concern for businesses dealing with large datasets

29. Qlik Replicate

Qlik Replicate (previously known as Attunity Replicate), is a data replication and ingestion tool that automates the process of moving data between various databases, data warehouses, and big data platforms. It supports both snapshot and incremental replication, including real-time transactional and batch-optimized replication. Qlik Replicate utilizes log-based change data capture technology and provides a user-friendly interface for managing data replication tasks.

🔗 https://www.qlik.com/us/products/qlik-replicate

📝 Gartner Reviews

Pros:

• It leverages log-based CDC technology to capture changes in source data and replicate them to the target in real-time or near real-time, reducing technical expertise needed
• Qlik Replicate supports a wide range of databases, data warehouses, and big data platforms, both on-premises and in the cloud, making it versatile for streaming data to destinations like Teradata, Vertica, SAP Hana, and Oracle Exadata, options that many modern ELT tools do not offer
• It offers a user-friendly, browser-based interface for configuring and managing replication tasks

Cons:

• The tool focuses primarily on CDC sources and replicates data mostly into data warehouse environments. No built-in ELT/transformations, only replication. Users need a separate tool for data transformations
• There can be a high complexity for setting up advanced replication scenarios (e.g., multi-target replication, filters)

30. Striim

Striim is a real-time data integration and complex streaming platform known for its low-latency, enterprise-grade features, and comprehensive integrations. Striim uses Change Data Capture to move data in real-time and handle analytics. Over time, it has grown to support many connectors for different use cases. It is well-known for its CDC features and strong support for Oracle databases. Striim competes with tools like Debezium and Estuary, especially in scalability. It is a top choice for environments that need both real-time and batch data processing.

🔗 https://www.striim.com/

📝 G2 Reviews

Pros:

• Similar to other top vendor like Estuary, Striim is built to handle large-scale data replication, making it a suitable choice for organizations with extensive data processing needs
• Striim combines stream processing with data integration, enabling organizations to handle both data replication and real-time analytics within the same platform, as well as perform incremental batch replication, loading snapshots and syncing changes at scheduled intervals

Cons:

• It’s stream processing platform makes it more challenging to learn compared to other ETL/ELT tools. It also operated by Tungsten Query Language which is not as user-friendly as SQL
• Build and script the entire CDC process yourself, which can be powerful but also more complex and time-consuming, writing custom TQL custom scripts adds complexity

31. Apache Kafka

A distributed event streaming platform that enables real-time data pipelines and streaming applications. Kafka is often used alongside ETL/ELT tools for handling high-throughput, low-latency data ingestion, streaming, and integration.

🔗 https://kafka.apache.org/

📝 Trustradius reviews

Pros:

• Widely used across industries for real-time analytics, event streaming, monitoring, and data integration - good choice for managing streaming data
• Uses horizontal scaling, and is powerful in handling BigData

Cons:

• Deploying and managing Kafka clusters demands considerable technical expertise
• Kafka's distributed architecture can lead to increased management effort and operational costs at scale
• Kafka handles streaming well, but needs tools like Kafka Connect, or custom code with Kafka Streams to do data extraction, transformation, and loading tasks

32. Debezium

Debezium is an open-source Change Data Capture tool originated from RedHat. It is a set of distributed services that capture row-level changes in databases so that applications can see and respond to those changes. Debezium records in a transaction log all row-level changes committed to each database table. The primary use of Debezium is to enable applications to respond almost immediately whenever data in databases change.

🔗 https://debezium.io/

Pros:

• Built on Kafka Connect, Debezium integrates natively with Apache Kafka, making it easy for teams already using Kafka to incorporate CDC into their data pipelines
• In Debezium they are used to capture the current state of a database table in smaller, manageable chunks, rather than doing a full-table snapshot all at once. This is especially useful when dealing with large datasets

Cons:

• Debezium relies heavily on Kafka and Kafka Connect, which require significant setup, maintenance, and expertise, especially in distributed or production environments
• Debezium is designed for CDC from databases to Kafka. If you need batch data loads, non-CDC sources, or direct integration with tools outside the Kafka ecosystem, you’ll need custom pipelines or third-party tools
• Debezium doesn’t support historical data replay out-of-the-box. If Kafka retention expires or snapshots are needed, custom logic must be built to backfill or "time travel" through data

33. SnapLogic

SnapLogic is a more general application integration platform that combines data integration, iPaaS, and API management features. Its roots are in data integration, which it does very well.

SnapLogic is a more general cloud-based integration platform as a service that provides tools for both ETL and ELT data integration patterns. It's designed to help organizations connect and integrate various data sources, including on-premises and cloud-based systems, for data warehousing, business intelligence, and other analytical purposes.

🔗 https://www.snaplogic.com/

📝 Gartner Reviews

Pros:

• SnapLogic supports application, data, and API integration within a single platform, providing a comprehensive solution for enterprise data management
• Visual drag-and-drop interface for building data pipelines, making designing and deploying complex integrations easier
• Can easily scale with enterprise needs, and can handle larger volumes of data across multiple systems

Cons:

• While SnapLogic claims over 700 snaps, these are for building functionality. There are nearly 100 snaps for connecting to different sources and destinations. This may meet your needs, but it is less than some other ELT or ETL tools, so evaluate whether you have all the necessary connectors
• SnapLogic’s pricing model is a little more complex than some others and is designed more for enterprise deals. It is not as well suited for starting small with pay-as-you-go pricing for cloud data warehouse deployments
• While user-friendly, complex integration pipelines can still require some learning and understanding of the platform

34. Singer

Singer is an open-source standard for building simple, composable ETL data pipelines. By using a standardized format for writing scripts to extract data from sources Taps – extract data from sources (e.g., APIs, databases) Targets – load data into destinations (e.g., data warehouses). Taps and targets communicate using a JSON-based format via stdin/stdout, making them interoperable and modular. It’s particularly popular among developers and data engineers who need a flexible, customizable solution for creating data pipelines.

🔗 https://www.singer.io/

📝 Stackshare reviews

Pros:

• Around 200 prebuilt taps and targets available, covering popular databases, SaaS applications, and other data sources, which can be easily integrated into your ETL pipeline
• Given it’s open-source nature, Singer allows developers to create custom taps and targets or modify existing ones to fit their unique data integration requirements
• The constant support and drive from the community ensures ongoing development

Cons:

• Singer was popular during the time Stitch was successful, but after Talend (and later Qlik) acquisitions, it became one of several overlapping tools within Qlik’s portfolio
• Requires technical expertise to set up and maintain, as it is primarily command-line and code-based, debugging and error handling can be more complex because of its open-source nature
• Limited automation capabilities compared to modern managed ETL solutions, requiring more manual intervention and custom scheduling

35. Coalesce

Coalesce is a low-code, cloud-native data transformation platform combining the power of native SQL with a visual, column-aware GUI. Designed for modern data teams, it automates complex workflows, metadata, and column lineage while enabling version-controlled, template-based development. Its intuitive interface boosts productivity and governance without sacrificing flexibility. Ideal for data engineers and architects, Coalesce accelerates data preparation for analytics and AI.

🔗 https://coalesce.io/

📝 Gartner Reviews

Pros:

• Intuitive, low-code interface that empowers users to build and manage data pipelines with minimal coding, while still allowing full SQL access for advanced users
• It accelerates data transformation and delivery, reducing manual coding and boosting productivity
• Template-based architecture ensures pipelines scale effortlessly with data volume and organizational growth

Cons:

• Even though the platform is low-code, it requires basic understanding of data warehousing and SQL to take full advantage of the platform
• Coalesce currently works best with Snowflake (using other platforms—like Databricks or Microsoft Fabric Coalesce might not work as smoothly or at all yet) This can be a drawback for organizations that use a mix of data platforms

🔍 Comparison Summary (2025)

FAQ

What is the difference between ETL and ELT?

ETL (Extract, Transform, Load) transforms data before loading it. ELT loads raw data into the warehouse first, then transforms it. ELT is more common with modern cloud data stacks.

Is open-source better than managed ETL tools?

It depends on your team. Open-source (like Airbyte or Meltano) offers control and flexibility but needs engineering time. Managed tools (like Weld or Fivetran) offer speed and simplicity.

Which ETL tools support reverse ETL?

Weld, Rivery, Dataddo, and Fivetran support reverse ETL, pushing data back into tools like HubSpot, Salesforce, or Google Sheets.

Which tool is best for streaming data or CDC?

Estuary and AWS Glue are strong choices for real-time use cases and Change Data Capture.

How do I know which type of pricing fits me the best?

Finding the best pricing model for your needs can be challenging and is based on many factors, such as the volume of your data, syncing frequency, budget or monthly active rows.