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Comparing Adverity with AWS Glue and Weld

Carolina Russ
Carolina Russ6 min read
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What is Adverity

Adverity is a standout data integration platform transforming siloed marketing data into actionable insights. With more than 600 marketing data connectors, it is designed for marketers to make better decisions and improve performance across all campaigns and channels.

Pros

  • Data extraction and loading
  • Flexibility and customization
  • Scalability
  • User-friendly interface
  • Unlimited data and connections

Cons

  • Expensive, especially for small businesses
  • Steep learning curve
  • Reporting capabilities could be improved
  • Initial setup complexity
  • Performance issues with large datasets

A reviewer on Capterra:

What I like about Adverity

Ease of use, various data connection points readily available for integration and extraction, ranging from Social platforms to various DSPs. Users can easily set up a frequent data update and even connect with other dashboards like Data Studio.

What I dislike about Adverity

Read full review

What is AWS Glue

AWS Glue is a fully managed, serverless ETL service provided by Amazon Web Services. It automatically discovers and catalogs metadata (Glue Data Catalog), generates ETL code in PySpark, and runs jobs on demand or schedules them. Glue integrates natively with AWS data stores (S3, Redshift, RDS, DynamoDB) and third-party sources via JDBC.

Pros

  • Serverless—no infrastructure to manage; Glue provisions compute as needed (Apache Spark under the hood).
  • Built-in Data Catalog for schema discovery, versioning, and integration with Athena and Redshift Spectrum.
  • Supports Python (PySpark) and Scala ETL scripts with mapping and transformation APIs for complex logic.
  • Deep integration with AWS ecosystem (CloudWatch monitoring, IAM for security, S3 triggers).

Cons

  • Cost can be unpredictable for long-running or high-concurrency jobs (billed per Data Processing Unit-hour).
  • Debugging PySpark jobs in Glue requires jumping between AWS console logs and code; local testing is limited compared to local Spark.
  • On-premises or multi-cloud data sources require additional setup (Glue has JDBC connectors but network config can be complex).

AWS Glue Documentation:

What I like about AWS Glue

Glue’s automatic schema discovery and code generation speed up ETL development—once you point it to a data source, it builds tables in the Data Catalog and scaffolds PySpark jobs for you.

What I dislike about AWS Glue

Managing large-scale Glue jobs can be tricky—job concurrency limits and developer debugging in PySpark jobs require more AWS expertise.
Read full review

What is Weld

Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.

Pros

  • Premium quality connectors and reliability
  • User-friendly and easy to set up
  • AI assistant
  • Very competitive and easy-to-understand pricing model
  • Reverse ETL option
  • Lineage, orchestration, and workflow features
  • Advanced transformation and SQL modeling capabilities
  • Ability to handle large datasets and near real-time data sync
  • Combines data from a wide range of sources for a single source of truth

Cons

  • Requires some technical knowledge around data warehousing and SQL
  • Limited features for advanced data teams

A reviewer on G2 said:

What I like about Weld

First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.

What I dislike about Weld

Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.
Read full review

Adverity vs AWS Glue: Ease of Use and User Interface

Adverity

Adverity offers a user-friendly interface but has a steeper learning curve due to its broad feature set, which might be overwhelming for beginners.

AWS Glue

AWS Glue Studio provides a visual job authoring interface where you can drag-and-drop nodes to transform data, but deeper customizations still require PySpark code. The console UI can be intimidating for new users.

Adverity vs AWS Glue: Pricing Transparency and Affordability

Adverity

Adverity’s pricing is on the higher side, especially for small businesses, which may find the cost prohibitive. It is ideal for larger enterprises that need extensive data integration capabilities.

AWS Glue

Glue charges per Data Processing Unit (DPU)-hour; for example, running a small job for one hour costs ~$0.44 * number of DPUs used. While serverless, large or long-running jobs can become costly if not optimized.

Adverity vs AWS Glue: Comprehensive Feature Set

Adverity

Adverity supports a wide range of connectors and provides robust data extraction, transformation, and loading capabilities. However, the platform's comprehensive feature set can be a double-edged sword, offering great functionality but also requiring significant effort to master.

AWS Glue

Features include automated schema discovery (Glue Data Catalog), PySpark/Scala job generation, job scheduling & triggers, DataBrew for visual data prep, and Glue Workflows for orchestration. Also supports streaming ETL via Glue streaming jobs.

Adverity vs AWS Glue: Flexibility and Customization

Adverity

Adverity is highly customizable, offering flexibility to adapt to various data needs and integration scenarios, making it a strong choice for businesses that require tailored data solutions.

AWS Glue

Glue allows custom PySpark scripts, supports Python libraries via wheel files, and you can integrate with AWS Lambda for custom triggers. However, debugging and local runs can be challenging compared to self-managed Spark.

Summary of Adverity vs AWS Glue vs Weld

WeldAdverityAWS Glue
Connectors200++600+50+
Price$99 / Unlimited usage$500 / month$0.44 per DPUs-hour (development endpoints) + per-job costs
Free tierNoNoYes
LocationEUITAWS Global (multi-region)
Extract data (ETL)YesYesYes
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL)YesNoNo
TransformationsYesYesYes
AI AssistantYesNoNo
On-PremiseNoNoNo
OrchestrationYesNoYes
LineageYesNoYes
Version controlYesNoNo
Load data to and from ExcelYesNoYes
Load data to and from Google SheetsYesNoNo
Two-Way SyncYesNoNo
dbt Core IntegrationYesNoYes
dbt Cloud IntegrationYesNoNo
OpenAPI / Developer APIYesNoNo
G2 Rating4.84.54.1

Conclusion

You’re comparing Adverity, AWS Glue, Weld. Each of these tools has its own strengths:

  • Adverityadverity supports a wide range of connectors and provides robust data extraction, transformation, and loading capabilities. however, the platform's comprehensive feature set can be a double-edged sword, offering great functionality but also requiring significant effort to master.adverity’s pricing is on the higher side, especially for small businesses, which may find the cost prohibitive. it is ideal for larger enterprises that need extensive data integration capabilities..
  • AWS Gluefeatures include automated schema discovery (glue data catalog), pyspark/scala job generation, job scheduling & triggers, databrew for visual data prep, and glue workflows for orchestration. also supports streaming etl via glue streaming jobs. glue charges per data processing unit (dpu)-hour; for example, running a small job for one hour costs ~$0.44 * number of dpus used. while serverless, large or long-running jobs can become costly if not optimized. .
  • Weldweld integrates elt, data transformations, and reverse etl all within one platform. it also provides advanced features such as data lineage, orchestration, workflow management, and an ai assistant, which helps in automating repetitive tasks and optimizing workflows.weld offers a straightforward and competitive pricing model, starting at $99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
Review the detailed sections above—connectors, pricing, feature set, and integrations—and choose the one that best matches your technical expertise, budget, and use cases.

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