How to Migrate from AWS Glue to Integrate.io:
Step-by-Step Guide

Migrating from AWS Glue to Integrate.io is a structured process that replaces AWS Glue's pay-per-DPU billing and PySpark requirements with an Operational ETL platform featuring 150+ connectors, 220+ visual transformations, and 60-second CDC included.

Integrate.io is an AWS Glue alternative for teams that need predictable operations, low-code pipeline management, and connectivity beyond the AWS ecosystem. Teams that migrate successfully share three characteristics: unpredictable Glue bills, analysts or ops teams owning pipeline maintenance, and a requirement to connect non-AWS systems without custom connectors.

A key advantage of migrating from AWS Glue to Integrate.io is eliminating complex billing structures. Integrate.io's operational model covers everything. AWS Glue charges multiple separate line items.

This guide walks you through migrating from AWS Glue to Integrate.io from auditing your existing environment to cutting over in production without downtime or data loss.

Key Takeaways

• The important thing to know before you migrate from AWS Glue to Integrate.io: the migration is a structured seven-step process, not a weekend project.

• AWS Glue's consumption-based billing spans multiple components (ETL jobs, crawlers, interactive sessions, Data Quality, adjacent services), making total costs difficult to predict

• Integrate.io replaces AWS Glue ETL, ELT, CDC, Reverse ETL, and API Generation on a single Operational ETL platform

• The migration follows seven steps: audit, map connectors, set up, rebuild, parallel test, validate, decommission

• Integrate.io's 220+ drag-and-drop transformations and 150+ connectors cover the majority of AWS Glue use cases without custom Spark code

Why Do Teams Migrate from AWS Glue?

AWS Glue is a capable serverless ETL service for teams deeply embedded in the AWS ecosystem and comfortable writing PySpark or Python. The problems appear when those assumptions break down.

Billing that scales unpredictably

AWS Glue's pricing structure includes multiple billing components (ETL jobs, crawlers, interactive sessions, Data Quality, adjacent services) that can make total costs difficult to predict. According to AWS pricing documentation, the total cost of ownership spans six distinct billing line items that don't appear on the main Glue pricing page. The final AWS bill arrives higher than expected, and that gap widens as workloads scale.

Engineering overhead for non-engineers

AWS Glue job requires Python or Scala PySpark code. For ops teams, Salesforce admins, or analysts who need to move data between systems without writing distributed compute scripts, this creates a permanent dependency on data engineering resources that mid-market teams don't have to spare.

Limited visual transformation layer

AWS Glue Studio supports basic transforms but falls short for complex business logic. Conditional branching, multi-step field calculations, lookup tables, or data validation often require dropping into custom code where a visual tool was supposed to help.

Slow cold starts

AWS Glue Spark jobs take several minutes to initialize, which eliminates them as an option for latency-sensitive or near-real-time pipeline use cases without significant architectural workarounds.

Vendor lock-in

AWS Glue integrates tightly with S3, Athena, Redshift, and Glue Data Catalog. Connecting to non-AWS systems (Salesforce, NetSuite, Shopify, HubSpot) requires custom connectors or Glue Marketplace add-ons. When your stack spans multiple clouds and SaaS applications, that lock-in becomes a structural liability.

Integrate.io is an Operational ETL platform covering ETL, ELT, CDC, Reverse ETL, and API Generation that replaces all five AWS Glue components without requiring a single line of Spark code. If any of these pain points are pushing your team toward a migration, the process below will get you there without disrupting live data.

Before You Begin: Inventory Your AWS Glue Environment

Before touching any configuration in AWS Glue or Integrate.io, document everything currently running. This phase prevents surprises mid-migration.

Document the following:

• All active Glue jobs (name, trigger type, schedule, source, and destination)

• Glue crawlers and their associated data stores

• Glue Data Catalog databases and tables consumed by downstream systems (Athena, Redshift Spectrum, BI tools)

• IAM roles and permissions attached to each Glue job

• S3 paths used for staging and output data

• Downstream dependencies: Step Functions that invoke Glue jobs, CloudWatch alarms, application code reading Glue output, Lake Formation policies referencing Glue catalog tables

• Operational patterns per job, pulled from AWS Cost Explorer filtered by service = Glue

Integrate.io provides a single dashboard for complete pipeline visibility with every job, connector, and schedule in one place. You can retrieve your full job list from the AWS Glue console or via the AWS CLI using aws glue list-jobs. Export to a spreadsheet and tag each job as critical (production pipelines), standard (scheduled batch jobs), or low-priority (ad hoc or exploratory). This inventory becomes your migration checklist, and the prioritization determines the order you tackle them.

Step 1: Audit Your AWS Glue Jobs and Pipelines

With the inventory complete, audit each active Glue job for migration complexity, readiness, and downstream risk before touching any configuration.

For each Glue job, assess:

• Source and destination systems - AWS-native (S3, Redshift, RDS) or external (Salesforce, NetSuite, Shopify)?

• Transformation logic - Standard joins, filters, aggregations? Or custom PySpark business logic?

• Data volume and frequency - Batch processing, near-real-time CDC, or event-driven?

• Schema complexity - Fixed schema, dynamic schema, nested JSON, or semi-structured data?

• Custom dependencies - Does the job call external APIs, reference lookup tables, or use libraries outside the standard Glue environment?

Jobs with standard transformations map directly to Integrate.io's 220+ drag-and-drop transformations. Jobs with deeply custom PySpark logic may require re-architecting, which is often an opportunity to simplify a pipeline rather than faithfully replicate hundreds of lines of code.

Categorize jobs by migration complexity:

Migrate low-complexity jobs first. Building team confidence on simple pipelines before tackling complex, high-stakes ones reduces risk and surfaces platform familiarity issues early.

Step 2: Map AWS Glue Sources to Integrate.io Connectors

Integrate.io supports 150+ connectors across databases, cloud warehouses, SaaS applications, and file systems. Cross-reference your Glue job inventory against Integrate.io's connector library to confirm coverage before rebuilding anything.

AWS Glue job types mapped to Integrate.io equivalents:

Common AWS Glue source/destination mappings:

For sources not covered natively, Integrate.io's REST API connector handles many edge cases. Flag any gaps before moving to Step 3. Integrate.io's solutions engineering team can confirm coverage for your specific stack before you commit to the migration.

Step 3: Set Up Your Integrate.io Environment

Create your Integrate.io account and complete the environment setup before rebuilding any pipelines. Rushing this step and discovering a misconfigured firewall rule or permission issue during pipeline testing costs more effort than setting it up carefully upfront.

Initial setup checklist:

1. Create your Integrate.io account and assign user roles (admin, data engineer, analyst)

2. Configure your destination warehouse (Snowflake, Redshift, BigQuery, or another target)

3. Connect data sources using the connector authentication wizard

4. Add Integrate.io's IP ranges to your source database firewall allowlists (required for on-premises or VPC-hosted databases)

5. Configure failure notifications (email or Slack alerts for job failures or data quality issues)

6. Review compliance settings (SOC 2 Type II, HIPAA, and GDPR configurations if your workloads require them)

Integrate.io includes white-glove onboarding with a dedicated Solution Engineer. Configuration questions during this phase don't turn into multi-day blockers.

Step 4: Rebuild Your Pipelines in Integrate.io

Start with your simple, low-risk pipelines. Build team familiarity with the visual pipeline builder before migrating complex, critical jobs.

Rebuilding ETL pipelines with Transform & Sync:

Integrate.io's Transform & Sync product handles standard ETL workloads in a visual drag-and-drop interface with no Spark code required. For each Glue job being migrated:

1. Open the pipeline builder and select your source connector

2. Add transformation steps matching the logic from the Glue script using the transformation library search to find functions for joins, field mapping, type casting, conditional logic, deduplication, and aggregations

3. Connect the destination connector

4. Set the schedule using a cron expression, fixed interval, or event trigger

5. Enable schema drift detection so new upstream columns don't break the pipeline automatically

Rebuilding CDC pipelines with Database Replication:

For real-time or near-real-time replication (replacing Glue streaming jobs or AWS DMS-based approaches), use Integrate.io's Database Replication product. It delivers 60-second CDC lag using log-based change data capture for PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB, with no polling queries, no table locks, and no performance impact on the source database.

Rebuilding Reverse ETL pipelines:

If you're using Glue to push data back into operational systems (Salesforce, HubSpot, NetSuite), Integrate.io's Reverse ETL pipeline handles this natively with bidirectional sync, field-level mapping, and built-in deduplication logic.

A note on complex Glue jobs: Before replicating a complex PySpark job line by line, evaluate whether the pipeline logic can be expressed using Integrate.io's built-in transformations and visual branching. Many jobs that required custom Spark code in Glue because Glue didn't offer a native transform for the operation can be built in Integrate.io's visual editor in a fraction of the time.

Step 5: Run Parallel Testing

Before cutting over any production traffic, run both AWS Glue and Integrate.io simultaneously for at least one full run cycle.

Parallel testing process:

1. Trigger both the Glue job and the equivalent Integrate.io pipeline on the same schedule

2. Write both outputs to separate staging tables (for example, orders_glue and orders_integrateio)

3. Run row count comparisons across both output tables

4. Run hash-based data comparison on key fields to detect value-level differences

5. Validate NULL handling, data type casting, and timestamp formatting (these are common sources of discrepancy between platforms)

6. Log any differences and trace them back to specific transformation steps

Integrate.io’s visual pipeline builder exposes transformation logic in a way that Glue's PySpark scripts do not, making discrepancies easier to spot during parallel runs. Do not skip parallel testing for critical production pipelines. Even migrations that look straightforward surface edge cases. Duplicate records from deduplication differences, timestamp timezone mismatches, or NULL vs. empty string handling can create data quality incidents downstream if they reach production undetected.

Step 6: Validate Data Quality and Cut Over

Once parallel testing shows consistent output across multiple runs, your data is ready for the production cutover, but only after passing every item on this checklist.

Pre-cutover validation checklist:

• Row counts match within ±0.1% across multiple parallel runs

• Key metric aggregates (revenue totals, event counts, record IDs) match between platforms

• Schema matches downstream expectations on column names, data types, and nullable constraints

• Pipeline run time meets SLA requirements

• Failure alerts and monitoring are configured and tested in Integrate.io

• Rollback plan is documented (steps to re-enable the Glue job if Integrate.io fails post-cutover)

Cutover approach by job priority:

After cutting over, update any downstream systems that reference Glue job output locations (Athena queries, BI dashboards, application code, Lake Formation policies) to point to Integrate.io's output destinations.

Step 7: Decommission AWS Glue

Once all pipelines are running in Integrate.io and validated through production traffic, begin decommissioning.

Decommission checklist:

1. Disable, don't delete, migrated Glue jobs (keep them available as a rollback option)

2. Remove or disable Glue crawlers that are no longer needed

3. Archive Glue scripts to S3 or a code repository before deletion

4. Delete IAM roles specific to Glue jobs only after confirming no other services reference them

5. Clean up Glue Data Catalog entries that no longer feed any downstream system

6. Remove capacity reservations or provisioned DPUs if applicable

7. After a hold period with no rollbacks triggered, permanently delete the disabled jobs

Check AWS Cost Explorer after migration. It's common for a stray crawler or interactive session to continue generating charges if the cleanup is incomplete.

Is This Migration Right for Your Team?

Integrate.io is an AWS Glue replacement for teams that need low-code ETL, multi-cloud connectivity, and predictable operations. Not every team should migrate. Here's how to evaluate it honestly:

• If your Glue bills are unpredictable and the total keeps climbing beyond compute into crawlers, Data Quality, and adjacent AWS services, Integrate.io provides a clear operational ceiling across unlimited pipelines.

• If your pipelines are maintained by analysts or ops teams, not dedicated data engineers with Spark expertise, Integrate.io's true low-code builder removes the PySpark requirement entirely and lets non-engineers build and maintain pipelines without engineering support.

• If you're moving to a multi-cloud or hybrid architecture, AWS Glue runs only on AWS. Integrate.io connects to AWS, Azure, GCP, and on-premises systems from the same platform.

• If you need real-time CDC, Glue's batch-oriented Spark jobs require significant architectural work for sub-minute replication. Integrate.io's Database Replication delivers 60-second log-based CDC without polling queries or source database performance impact.

AWS Glue remains a strong fit for engineering teams deeply embedded in the AWS ecosystem, with Spark expertise and primarily infrequent, high-volume batch workloads. The migration above is designed for teams where those assumptions no longer hold.

If your primary challenge is operational predictability, analyst self-service, or moving beyond AWS lock-in, Integrate.io is worth a conversation.

Frequently Asked Questions

Does Integrate.io Cover All AWS Glue Connectors?

Integrate.io supports 150+ connectors including all major cloud data warehouses (Snowflake, Redshift, BigQuery), databases (PostgreSQL, MySQL, SQL Server, Oracle), SaaS applications (Salesforce, NetSuite, HubSpot, Shopify), and file systems (S3, SFTP). For AWS Glue workloads, especially those connecting to non-AWS systems, Integrate.io has native connectors. Integrate.io's solutions team can confirm coverage for your specific stack before you commit.

Can Integrate.io Replace AWS Glue for Real-Time CDC?

Yes. Integrate.io's Database Replication product delivers 60-second CDC lag using log-based change data capture with no polling queries, no table locks, and no performance impact on the source. It supports PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB as sources, with Snowflake, Redshift, BigQuery, and other warehouses as destinations.

What happens to the Glue Data Catalog after migration?

The Glue Data Catalog stays in place after migration and continues serving Athena, Redshift Spectrum, and other downstream consumers until you explicitly decommission it. Clean up catalog entries selectively as you retire each Glue job and confirm its downstream dependencies have been updated.

Do I need to know PySpark or Python to use Integrate.io?

No. Integrate.io is a low-code platform where all pipeline logic is built in a visual drag-and-drop interface using 220+ pre-built transformations. Technical users can write custom SQL transformations for complex calculations where needed, but the platform is designed so that ops teams, Salesforce admins, and analysts can build and maintain pipelines without engineering support.

Is AWS Glue being discontinued?

AWS Glue is not being discontinued. However, AWS Data Pipeline (a related AWS data orchestration service) has reached end-of-life, and teams dependent on it are actively migrating. AWS Glue itself remains an active service, but many teams are proactively migrating due to operational unpredictability and the engineering overhead of maintaining PySpark-based pipelines rather than any planned AWS deprecation.