You spend hours pulling customer data into S3, cleaning it in Spark, then… exporting CSVs to upload into your tools. It’s 2026—why are you still the human API?
Here’s the unlock. AWS Glue just added write operations for SAP OData, Adobe Marketo Engage, Salesforce Marketing Cloud, and HubSpot connectors. Translation: you can push clean, modeled data back into the exact systems your marketers, lifecycle teams, and sales ops actually use.
That means fewer brittle scripts, fewer point tools, and way fewer late-night “why didn’t this segment update?” pings. You stay in Glue. You model once. You write wherever.
If you’ve been waiting to build reverse ETL without adding another SaaS to your stack—or you’re tired of duct-taping AppFlow, Lambdas, and spreadsheets—this is your green light.
One more reason this matters. Your team already trusts Glue for jobs, schemas, and monitoring. Turning it into your activation engine means less context switching and more shipping.
And yes, this is how you shrink martech latency from “whenever someone uploads a CSV” to “near-real-time enough to hit a promo window.”
AWS Glue now supports write operations for four big martech surfaces: SAP OData endpoints, Adobe Marketo Engage, Salesforce Marketing Cloud (SFMC), and HubSpot. You already could read from many of these; adding writes completes the loop so you can operationalize data—without bouncing between five tools.
See AWS Glue release notes for the full aws glue enhancements list and region availability.
What this unlocks in practice. You can treat your curated zone in S3 as the source of truth and fan out to these destinations in a single pipeline. No more hand-offs to “the person with the Marketo login.” Your Spark transformations, quality checks, and observability now sit in front of the actual business action.
If you run marketing analytics, data engineering, or growth ops, you can now:
Bonus points if your security team prefers fewer external connections and a single audit trail. With Glue, your governance lives where your data already lives.
Activation speed matters. Segments get stale. Offers miss windows. When writes live inside Glue, you tap the same Spark code, catalog schemas, and monitoring you already use.
First-hand example. Say you score leads nightly and calculate LTV cohorts weekly. With Glue writes, you update Marketo lead attributes and SFMC Data Extensions in the same pipeline that computed them—no more CSV uploads from a marketing ops inbox.
Add a real-world twist. Your pipeline can enrich HubSpot contacts with product events while also posting order syncs to SAP via OData—all triggered by the same job parameters and guarded by the same IAM role.
References:
Build a job that:
1) Reads modeled data from your curated zone (e.g., S3/Parquet partitioned by date). 2) Transforms to target schemas (Marketo lead fields, SFMC Data Extensions, HubSpot contact properties). 3) Writes in batches with retries and dead-letter queues (DLQs) for rejects.
This replaces manual uploads or point tools while giving you Spark-scale transforms, Glue job bookmarks, and centralized logging.
Add these practical touches:
APIs differ:
Pro moves:
Example. A lifecycle team promotes “High-Intent Leads” hourly. Your Glue job merges overnight scoring with recent events, upserts 5k leads to Marketo, and flags a campaign membership in SFMC. No swivel-chairing.
Docs to keep handy:
Write support—and its performance—depends on aws glue versions. Runtimes bundle specific Apache Spark, Python, and library versions. Newer versions typically bring:
Before you roll out, verify the connector’s supported Glue versions in the docs. If you’re running legacy jobs on 3.0, test on a newer runtime in a dev endpoint or separate job to confirm compatibility, especially with pandas, Arrow, and auth libraries.
Pro tip. Build a small “compat test” job that imports your usual libs, hits sandbox endpoints, and exercises 5–10 representative writes. Keep it in source control and run it before every runtime upgrade.
AWS periodically sunsets older runtimes. If you’re searching for aws glue 3.0 end of life details, check the official aws glue release notes. Don’t assume “it still runs” equals “it’s supported.” Lack of security updates or bug fixes can bite you during audits.
Migration tips:
Practical rule. If you’re building new reverse ETL today, target 5.x/6.x where available. That sets you up for modern Python/Spark, stronger connector support, and fewer surprise deprecations.
Helpful links:
How to make this concrete:
Auditability checklist:
First-hand example. A B2C subscriptions team used Glue to sync churn-risk flags to HubSpot. They enforced a preflight schema check, rejected 0.8% of rows to a DLQ due to invalid emails, and unblocked the lifecycle team without creating a new toolchain.
Bonus. Keep a lightweight “data contract” doc per destination (owner, object names, required fields, rate limit notes, rollback plan). Nothing fancy—one page that prevents 80% of surprises.
Zooming out. Teams that win with this pattern keep the payloads boring and the monitoring loud. They automate the unglamorous bits—mappings, retries, alerts—and free humans for the creative work (offers, journeys, experiments).
Either works. Glue Studio offers a visual experience and managed connectors. Many teams prototype in Studio and codify production pipelines as Spark or Python shell jobs with the same connectors. Pick one path, add CI/CD, and standardize job parameters.
Through each provider’s OAuth or API token mechanism configured in the connector. Store secrets in AWS Secrets Manager, inject them at runtime, and avoid hardcoding in scripts or job parameters.
Assume they exist and design for them. Batch requests, add exponential backoff with jitter, surface retry counts as CloudWatch metrics, and include dead-letter queues for hard rejects. For large updates, schedule during off-peak windows and chunk by account/region.
AWS deprecates older runtimes over time. The exact date can change by Region and context. Always check the official aws glue release notes for current support windows and plan upgrades to newer aws glue versions.
AppFlow is great for no-code data movement between SaaS and AWS. Glue is built for code-first, large-scale transformations and complex orchestration. With write support in Glue, you can keep heavy transformations and activation in one place—especially when you already run Spark jobs.
Yes. Most tools support sandboxes (Marketo, SFMC, HubSpot). Point your dev Glue job at sandbox endpoints, use synthetic data, and disable downstream automations until validation passes. Gate production with a manual approval in your CI/CD pipeline.
Glue costs scale with job runtime and resources. Good hygiene helps. Prune input data, cache lookups where possible, right-size DPUs, and consolidate writes to avoid tiny, chatty requests. Add CloudWatch budgets and alerts early so there are no mysteries.
Glue triggers and workflows can cover many cases. If you need long-running orchestrations, branched logic, or human approvals, pair Glue with Step Functions. Keep the business logic in your Spark code; let orchestration do orchestration.
To go even faster, template your job:
AWS Glue’s new write operations flip your pipelines from “data at rest” to “data in motion.” You don’t just clean data—you deploy it where it matters: CRM, MAP, and the inboxes that drive revenue.
If you adopt one mindset, make it this. Treat outbound writes like payment flows. Validate inputs, enforce idempotency, and observe everything. Then use Glue’s scale to do the boring parts (batching, retries, logging) so your team can focus on the fun parts (better scoring, richer segments, faster tests).
Start small—one audience, one endpoint. Prove latency drops and error rates. Then templatize the job, snap in a second destination, and let your marketing team ship experiments at the speed of curiosity.
Working with Amazon Marketing Cloud audiences or activating into Amazon DSP as part of this workflow? Explore our AMC Cloud and get inspiration from real-world outcomes in our Case Studies.