As 10 melhores ferramentas ETL analisadas para 2026

Análises das melhores ferramentas de ETL

1. 

   1

   Skyvia

   Best for activating warehouse data in apps

   • Free plan + free demo available
   • From $79/month (billed annually)

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   Customer Rating:4.9/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Skyvia is a cloud-based ETL and data integration platform that supports data import, export, replication, synchronization, and reverse ETL across 200+ prebuilt connectors spanning databases, cloud apps, and data warehouses.

   Who Is Skyvia Best For?

   Skyvia is a strong fit for data engineers who need a no-code way to replicate, transform, and push data between cloud apps and warehouses without managing pipeline infrastructure.

   Why I Picked Skyvia

   I picked Skyvia as one of the best because its reverse ETL capability is genuinely useful for closing the loop between your warehouse and your operational apps. In practice, I can pull enriched customer data from Snowflake, map it to Salesforce Contact and Account objects, and push it back on a schedule without writing a line of code. The bidirectional sync and visual Data Flow builder make it straightforward to keep warehouse-transformed records active in the tools your business runs on daily.

   Skyvia Key Features

   • Data replication: Automatically replicates cloud application and database records to a target warehouse or database on a configurable schedule.
   • Control Flow designer: A visual, no-code tool for sequencing integrations with branching logic, shared variables, and error-handling steps.
   • dbt integration: Links dbt Core projects from GitHub or a Git repository and runs transformations on a schedule or within a Control Flow pipeline.
   • PII hashing: Masks or excludes sensitive fields before replication runs, so personal data never reaches the destination in plain text.

   Skyvia Integrations

   Skyvia offers 200+ native integrations, including Snowflake, Google BigQuery, Amazon Redshift, Databricks, SQL Server, Amazon S3, Salesforce, HubSpot, and Google Sheets. It supports custom REST and GraphQL connectors plus REST API access.

   Pros and Cons

   Pros:

   • Broad source coverage reduces connector work
   • Scheduled batch jobs run consistently
   • Visual mapping supports warehouse-to-app activation

   Cons:

   • Error messages can obscure root causes
   • Complex mappings need extra configuration
   Learn more about Skyvia:

   • Check out Skyvia on their website
2. 

   2

   Hevo

   Best for reliable enterprise data pipelines

   • 14-day free trial + Free demo available
   • From $239/month (billed annually)

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   Customer Rating:4.7/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Hevo is a no-code ETL and data integration platform designed for businesses seeking to automate data pipelines. It supports enterprises by enabling connections with over 150 data sources without the need for coding, enhancing data management efficiency.

   Why I picked Hevo: Built on a Kafka-based architecture, Hevo delivers data with low latency, ensuring that your analytics and business decisions are based on the most current information. This real-time processing is crucial for applications that require up-to-date data. With over 150 pre-built connectors, it facilitates data extraction and integration from diverse platforms, supporting businesses that rely on multiple data sources. Additionally, Hevo automatically detects the schema of your source data and replicates it to your destination, keeping your data warehouse in sync even as source data structures change. 

   Hevo Standout Features and Integrations:

   Features include pre-load and post-load data transformations that let you clean, format, and standardize your data either before it lands in your warehouse or after, ensuring it's analytics-ready. Hevo also offers a REST API, allowing you to integrate it into your existing workflows and trigger pipelines programmatically.

   Integrations include MongoDB, Google Analytics, Salesforce, HubSpot, Azure Synapse Analytics, BigQuery, Snowflake, Redshift, Mailchimp, Klaviyo, WordPress, Pipedrive, and more.

   Pros and Cons

   Pros:

   • Automated schema management and error handling
   • Real-time data synchronization
   • The platform supports a wide range of data sources

   Cons:

   • Pricing may be a concern for small businesses
   • Editing established pipelines can be challenging
   Learn more about Hevo:

   • Check out Hevo on their website
3. 

   3

   Adverity

   Best for multi-source data extraction

   • Free demo available
   • Pricing upon request

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   Customer Rating:4.5/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Adverity is a data platform designed to help teams manage and make sense of their data. It offers tools to collect, process, and distribute data, aiming to make it easier for users to work with their information.

   Why I picked Adverity: I picked Adverity because it lets you extract data from multiple sources, transform it based on your own rules, and load it into your preferred destinations. Its connect feature helps bring all your data together, giving you a complete view of what you're working with. The transform feature is useful for cleaning and standardizing data before analysis, which is key to any ETL workflow. You can also automate these steps to keep data pipelines running smoothly. These capabilities make it a solid fit for teams looking to manage ETL processes more efficiently.

   Adverity Standout Features and Integrations:

   Features include monitoring, which helps users keep track of their data's status and quality. This can assist in identifying any issues or inconsistencies in the data. Additionally, the share feature allows teams to distribute their data to various destinations, facilitating collaboration and further analysis.

   Integrations include Google, Meta, TikTok, Google BigQuery, Looker Studio, Facebook Ads, Amazon Advertising, Hubspot, Salesforce, Amazon S3, Snowflake, and Microsoft Azure.

   Pros and Cons

   Pros:

   • Provides the ability to create personalized dashboards
   • Can harmonize data from various sources
   • Automated data quality monitoring

   Cons:

   • The initial setup can be time-consuming
   • Certain configurations may require technical knowledge
   Learn more about Adverity:

   • Check out Adverity on their website
   • Explore best alternatives to Adverity
4. 

   4

   Integrate.io

   Best for extensive data transformations

   • Free demo available
   • From $1,999/month

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   Customer Rating:4.2/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Integrate.io is a cloud-based ETL platform designed to help you organize and prepare data for analytics and business intelligence. It offers a low-code environment that simplifies building and managing pipelines across a wide range of sources, including relational databases, NoSQL stores, file systems, object stores, and ad platforms.

   Why I Picked Integrate.io: One reason I chose Integrate.io is its support for over 220 built-in transformations. This extensive library allows your team to perform complex data manipulations without writing code, making it easier to prepare data for analysis. Its low-code transformation layer also reduces the need for SQL or scripting, offering flexibility for both technical and non-technical users.

   Integrate.io Standout Features and Integrations

   Features include support for operational ETL use cases, such as automating bidirectional Salesforce integrations and enabling B2B file data sharing. It also supports multi-cloud, multi-region deployments across the US, EU, and APAC, which can help distributed teams meet performance and compliance needs.

   Integrations include Salesforce, HubSpot, Google Analytics, Facebook Ads, Amazon Redshift, Google BigQuery, Amazon S3, Snowflake, MySQL, PostgreSQL, Microsoft Azure SQL Database, and MongoDB.

   Pros and Cons

   Pros:

   • REST API access for automation
   • Strong security features and compliance standards
   • Low-code environment reduces need for scripting

   Cons:

   • Potential performance issues with large datasets
   • May still come with a learning curve for non-technical users
   Learn more about Integrate.io:

   • Check out Integrate.io on their website
   • Read my in-depth review of Integrate.io
5. 

   5

   AWS Data Pipeline

   Best drag-and-drop console

   • Free plan available
   • From $0.60/month (for low-frequency activities)

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   AWS Data Pipeline is a managed ETL tool that allows you to transfer data between AWS services and other on-premise resources. It lets you specify the data you want to move, define transformation jobs or queries, and set schedules for performing these transformations.

   Why I picked AWS Data Pipeline: What I really like about AWS Data Pipeline is its user-friendly drag-and-drop console that lets you simplify the process of building and managing your pipelines. It also offers fault-tolerant and customization capabilities that ensure smooth data pipeline operations.

   AWS Data Pipeline Standout Features and Integrations:

   Features of AWS Data Pipeline that stood out to me were its drag-and-drop UI and high fault tolerance. The drag-and-drop capability makes its console quite convenient to use, while the added fault tolerance helps in minimizing the impact of user errors.

   Integrations for AWS Data Pipeline include Redshift, SQL, and DynamoDB. Such connectors are available as pre-built options when using the platform.

   Pros and Cons

   Pros:

   • High fault tolerance and customization capabilities
   • Lets you specify the data you wish to move
   • Easy drag-and-drop console that simplifies processes

   Cons:

   • Doesn’t support third-party data
   • Managing data can be time-consuming
   Learn more about AWS Data Pipeline:

   • Check out AWS Data Pipeline on their website
6. 

   6

   AWS Glue

   Best serverless ETL tool

   • Free plan available
   • From $0.44/DPU-hour

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   AWS Glue provides access to a serverless data integration service that simplifies the process of discovering, preparing, handling, and integrating data from different sources. Its visual interface lets you facilitate the loading of data into your data lakes, ensuring data is readily available for analysis.

   Why I picked AWS Glue: I chose AWS Glue because of its access to more than 70 data sources while efficiently managing your data using a centralized data catalog. It can even scale up or down depending on the current demands of your organization. These capabilities are the reasons why I believe AWS Glue is the best serverless ETL tool on this list.

   AWS Glue Standout Features and Integrations:

   Features of AWS Glue that stood out to me were its access to multiple data stores and ability to build complex ETL pipelines. Being able to tap into different data sources makes business intelligence gathering more convenient while developing complex pipelines can result in more in-depth insights.

   Integrations for AWS Glue include MySQL, Oracle, Redshift, Amazon S3, and more. All these connectors come pre-built when you choose the platform.

   Pros and Cons

   Pros:

   • Easy maintenance and deployment
   • Provides filtering for faulty data
   • Failed jobs in AWS Glue can be retrieved

   Cons:

   • Not the best choice for real-time ETL jobs
   • Lacks compatibility with commonly used data sources
   Learn more about AWS Glue:

   • Check out AWS Glue on their website
7. 

   7

   IBM DataStage

   Best for batch data extractions

   • Free trial + free demo available
   • Pricing upon request

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   IBM DataStage is an ETL tool that allows you to extract, transform, apply business principles, and effortlessly load the data into any desired target. It has a basic version of the software that you can install on-premises and an upgrade that lets you reduce data integration time and expenses.

   Why I picked IBM DataStage: I decided on IBM DataStage because it is an integration tool that excels at integrating data from a vast range of enterprise and external sources. I like how it is well-equipped to handle the processing and transformation of large data volumes, thanks to its scalable parallel processing approach.

   IBM DataStage Standout Features and Integrations:

   Features of IBM DataStage that stood out to me were its data science and automated load balancing. I found that its data science feature allows me to quickly derive insights from my data, while the automated load balancing helps me get the most throughput.

   Integrations are available with Amazon S3, Azure, BDFS, BigQuery, and FTP Enterprise. You can also link up and transfer data with data sources like IBM Db2 Warehouse on Cloud and IBM Netezza. All of these integrations are pre-built into the platform.

   Pros and Cons

   Pros:

   • Access to AI services
   • Reduced data movement costs
   • Workload balancing allows users to run workloads faster

   Cons:

   • Editing columns can be tedious
   • Lacks automation for error handling and recovery
   Learn more about IBM DataStage:

   • Check out IBM DataStage on their website
8. 

   8

   Hadoop

   Best for large data storage

   • Free to use

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   Hadoop is an open-source ETL tool that lets you store and process data. Instead of relying on a single computer, the software allows you to cluster multiple devices together, enabling fast analysis and storage of huge datasets.

   Why I picked Hadoop: I chose Hadoop because it provides access to extensive storage capacity capable of accommodating any type of data. The tool offers immense processing power, allowing you to handle an extraordinary number of concurrent tasks or jobs.

   Hadoop Standout Features and Integrations:

   Features of Hadoop that stood out to me were its cluster job scheduling and access to common Java libraries. I found that these capabilities allowed the platform to quickly process large datasets, which matches well with its significant data storage capacity.

   Integrations for Hadoop include MySQL, PostgreSQL, and Oracle. All these connectors are pre-built into the platform.

   Pros and Cons

   Pros:

   • Highly scalable ETL tool
   • Can deal with any kind of dataset
   • Can process a huge amount of data simultaneously

   Cons:

   • Not ideal for smaller datasets
   • Java framework can be easily exploited
   Learn more about Hadoop:

   • Check out Hadoop on their website
9. 

   9

   Oracle Data Integrator

   Best for integrations

   • Free demo available
   • Pricing upon request

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   Oracle Data Integrator caters to various data integration needs. It handles everything from large-scale batch loads with high performance to real-time event-driven integration and even SOA-enabled data services.

   Why I picked Oracle Data Integrator: I chose Oracle Data Integrator because it offers a range of pre-built connectors that you can use to effortlessly link various databases. It allows you to readily connect with Hadoop, EREPs, CRMs, XML, JSON, LDAP, JDBC, and ODBC, right out of the box.

   Oracle Data Integrator Standout Features and Integrations:

   Features of Oracle Data Integrator that stood out to me were its active integration platform and its ability to allow developers to create their own mappings through standard business logic. The active integration feature allows data-based, event-based, and service-based data integrations, while the latter lets them produce code for a wide range of data processing technologies.

   Integrations are available with data warehouse platforms such as Oracle, Teradata, IBM DB2, Sybase, and Exadata. You can also use it to work with other technologies such as ERPs, XML, and LDAP. All of these integrations are pre-built into the platform.

   Pros and Cons

   Pros:

   • Supports all platforms, hardware, and operating systems
   • Automatically detects faulty data before application insertion
   • Efficient architecture that uses both source and target servers

   Cons:

   • User interface can be complex
   • Difficult to learn and requires training
   Learn more about Oracle Data Integrator:

   • Check out Oracle Data Integrator on their website
10. 

    10

    Microsoft SSIS

    Best for combining data

    • No free trial
    • $1.913/hour (Enterprise)

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    Microsoft SQL Server Integration Services, simply called Microsoft SSIS, is an enterprise tool that integrates, transforms, and migrates data within Microsoft’s SQL Server database. It offers integration-related capabilities, such as data analysis, cleansing, and performing ETL processes to update data warehouses.

    Why I picked Microsoft SSIS: A major reason why I chose SSIS is because of its versatility, supporting various databases like SQL, DB2, and Oracle. Such flexibility allows users to combine data from different sources, while its graphical interface makes it easier to accomplish data warehousing tasks and transformations.

    Microsoft SSIS Standout Features and Integrations:

    Features of Microsoft SSIS that stood out to me were its graphical tools and built-in catalog database. The graphical tools make it easy to develop packages, while the SSIS catalog database makes it easy to manage projects, parameters, and environments. I also liked how the platform lets you import data from different sources and transform them the way you want.

    Integrations are available for Microsoft SSIS with ADO, DQS, Excel, FTP, HTTP, WMI, and more. These connectors come pre-built into the platform. You can also download additional types of connection managers from their website.

    Pros and Cons

    Pros:

    • Provides users with a number of documentation features
    • UI is easy to use and configure
    • A flexible ETL and data transformation tool

    Cons:

    • Only supports Microsoft Windows
    • Lacks integration options with other tools
    Learn more about Microsoft SSIS:

    • Check out Microsoft SSIS on their website