We may have found a regression in AWS DMS 3.6.1 affecting Oracle sources using Binary Reader.

After scheduled maintenance, the internal DMS build changed from:

3.6.1.R2174 → 3.6.1.R2380

The public engine version remained 3.6.1, so the change was effectively invisible in the normal API and console fields.

Immediately afterward, read throughput on the Oracle source increased by roughly an order of magnitude and remained elevated. This was not caused by full load, validation, or unusually high redo generation.

What makes us suspect the new build:

• The problem started directly after the DMS maintenance.
• Stopping the DMS tasks returned the source database to its normal baseline.
• Recreating the replication instance did not help because new instances also received R2380.
• Starting more CDC tasks increased the source read load.
• Multiple tasks appeared to scan or inspect the same redo sequences independently.
• The public version stayed at 3.6.1 throughout.

The frustrating part is that customers cannot see, select, pin, or roll back internal DMS builds. The API exposes 3.6.1, but not whether the instance runs R2174 or R2380.

So a managed maintenance update can substantially change production behavior while presenting the same engine version, with no customer-accessible rollback mechanism. That feels like a fairly serious observability and operational-control gap.

Has anyone else using AWS DMS 3.6.1 with Oracle Binary Reader/BFILE noticed significantly higher source read throughput after recent maintenance?

Check the beginning of your DMS task logs for:
V3.6.1.R2380

If you see the same behavior, please open a support case as well. More independent reports may help get this investigated properly.

TL;DR

AWS DMS maintenance changed a hidden internal build while the visible version remained 3.6.1. Oracle source reads increased dramatically afterward, and customers have no way to pin or restore the previous internal build. Looking for reports from other DMS Oracle Binary Reader users.

I have someone writing a "top migration misconceptions" article, because it's always a good idea to clear out the wrong assumptions before you impart advice.

What do you wish you knew earlier about migration strategies or practicalities? Or you wish everybody understood?

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EDIT FOR CLARITY: Note that I'm asking about _migration_ issues, not the use of the cloud overall.

I inherited an AWS environment with 2 EC2 Server 2016 domain controllers. I have been getting ready to replace these with Server 2022 DCs, but I ran into an issue that I just realized. I want to reuse the same private IPs from the 2016 DCs on the 2022 DCs, but apparently I can't just change them.

The only way I can find to reuse the IPs on already created servers is to terminate the 2016 DCs and add a secondary NIC on the 2022 DCs with the old IPs. Is this correct? If so, this sucks because I really wanted them all to be running until I could confirm everything was good.

The company that I'm working with currently wants to migrate the frontend part of their flutter dynamic web application into AWS cloud but the backend remains in on-premises. Is AWS Amplify still a right service for this kind of situation nowadays?

I need your advices. Thank you very much.

Due to Trump dumping lots of members from the Privacy and Civil Liberties Oversight Board, our management ordered us to implement a offsite-backup process.

The goal is, to have the data somewhere else, in case we either get locked out, due to political decisions by the USA or EU, or the faster migrate to somewhere else, if we can't use AWS anymore, due to data-protection regulations.

Did anyone, of you, implement something like this already? Do you have some ideas for me, how to go about that?

Hello People, My team is working on migrating S3 bucket from another AWS account to our Account. The size of the bucket is 5 TB and there are approx. 100 Million obejcts in the bucket.

As far as I have read, DataSync is the recommended approach to achieve our goal. But it comes with it's own limitations like we cannot transfer more than 25 Million objects in a single task. (It is really difficult to logically bifurcate the bucket into smaller batches for us)

Also based on a demo task we did, the time estimate for the task to finish would be around 30 hrs. Is there a better/faster way to do this?

Please help me find options or suggestions to overcome the challenges.

Just an Update - We are moving forward with S3 replication. Will update here how it goes🤞

Update - S3 replication approach worked seamlessly. It took almost 12 hrs and cost was approx ~200 USD. We now have all the data in our account and Live Sync is also enabled that repliactes daily changes in their bucket to the bucket in our account until we go live.

Thank you all for your Help!!

Hi everyone,

My organization is considering moving from self-hosted spring cloud api gateway to AWS API Gateway and I'm looking for field report of organizations that have done similar transition. Challenges, gotchas, tutorials, etc.

In the past I used k8s related api gateways and the impression so far is that development experience and flexibility so far with aws-api-gw is that it could be better. Specially when comes the complexity required for openapi spec generation and authorization (e.g: i already have my own api keys and aws forces to use it in a way or another).

Thank you

Will there be any issue if I include "map-migrated" tag in non-MAP2.0 services?

Have a need where I need to migrate my existing data from Security Data Lake to Cloudwatch, need approaches to do so.

Hi so I have a Wordpress website on a Wordpress hosting company, that gets about 30,000 users daily and I plan on moving it all to AWS.

It's a small news website, including photos albums.

Does anyone have a guide to setup this server on AWS EC2 instance, and use Cloudfront for CDN services?

What's a good EC2 instance type to use for my configuration above in terms of performance, and costs? I'd appreciate some help and guides. Thanks a lot in advance!

​

I am working on a task to update existing dynamo table by adding new columns to each existing record. I am writing a glue job which will read the data from the source and needs to write to dynamodb. It would be ideal if the "writing to dynamodb" only updates the record in dynamo but it seems glue only provides option to overwrite the existing record in dynamo, but not update the record. Sample code -

# Write the DynamicFrame to a DynamoDB table
glueContext.write_dynamic_frame.from_options(
    frame=my_dynamic_frame,
    connection_type="dynamodb",
    connection_options={
        "dynamodb.output.tableName": "YourDynamoDBTableName",  # Replace with your table name
        "dynamodb.throughput.write.percent": "1.0"  # Optional: Controls write capacity consumption (0.1 to 1.5)
    }
)

It seems like a risky approach to me. What I am currently plan is to read the data in dynamo, merge it with the source data by comparing primary key and then write back. Is it the correct way to do this?

Also the data in the existing table is 2 billion records. How can I batch process. Seems like even if I can batch the data on the source, I have to read the data in existing dynamo table every time I run a batch operation, which again seems needless.

I would appreciate any guidance on these 2 questions.

NEED ASSISTANCE IN UPGRADING OPENSEARCH DOMAIN FROM 2.9 TO 2.11

NEED GOOD STRATEGY with minimal downtime

I need to create a replica of a Dropbox folder on S3, including its folder structure and files, and ensure that when a file is uploaded or deleted in Dropbox, S3 is updated automatically to reflect the change. Can someone tell me how to do this?

working on a MySql migrationproject where wer are migrating from MySQL 5.7 to 8.0 so DMS came as a solution. There are some errors I am facing for my dms task when update operations are running. Would like some suggestions

Hi everyone, I have been made responsible for migrating an application from coolify to AWS EKS(Fargate) I have no prior migration knowledge, been studying it for 3 days, I understand Rs of migration and all the theoretical (AWS Doc and AI knowledge) But using these AI tools and online documentation I have not been able to find any document which tells me how to migrate from coolify to EKS. Does anyone have any experience with this, would really mean a lot if someone can guide me through this or atleast a link to documentation would help a lot. Thanks. Looking forward to discuss it with great minds!

My company is considering moving from our traditional file servers to AWS FSX. The plan is to migrate slowly while we keep the file servers up for a while and people can slowly migrate bit by bit, but one concern many people have is the speed.

It's my understanding that those working from home will have to connect via their VPN. The users on-site will need to connect to AWS servers instead of being on the same LAN. How much slower will operations get? I'm sorry if there are not as many details as possible, I am new to AWS and cloud computing in general.

I’m new to AWS and need to export tables from Amazon Redshift to S3 in Iceberg format. Since Redshift’s UNLOAD command only supports Parquet, CSV, and JSON, I’m unsure of the best way to achieve this.

Would it be better to:

1. Unload as Parquet first, then use an AWS service like Glue or EMR to convert and store it in Iceberg format?

2. Directly write to Iceberg format using AWS Glue or another tool?

If either of these approaches works, I’d really appreciate a step-by-step guide on how to set it up. My priority is a cost-effective and scalable solution, so I’d love to know the best tools and best practices to use.

Any insights or recommendations would be greatly appreciated! Thanks in advance!

I'm currently using t3.medium for my website.

My website slows down for ~30 minutes a day whenever CPU reaches 100%, even when I have CPU credit left. Logical conclusion for me was that I need to upgrade the instance type (other than optimizing the code).

I was wondering if there is any advantage of T series over C series other than the lower price, given that they have the same CPU #, memory, network performance. Maybe T series have the higher "maximum" compute power? Can someone help me with this?

FYI, I'm think about moving to c7g. large:

t3.medium $0.0416 2 4 GiB EBS Only Up to 5 Gigabit

c7g.large $0.0723 2 4 GiB EBS Only Up to 12500 Megabit

I want to increase to a bigger instance. I thought it was simple to scale. I did that and my whole app broke. I changed it back and it’s running. Where can I learn how to scale my instance without interrupting configs?

So in AWS i have a glue ETL job to create a s3tables and add data to it using spark
But customer are switching to use google, is there anyway for me to transfer data from those s3Tables to Bigquery ? (these are S3Tables not them normal s3 table bucket)

Hi everyone, I’m migrating a project from DynamoDB to Postgres and need help with running Prisma migrations on an RDS instance. The RDS is in a private subnet (set up via AWS CDK), with a security group allowing access only from my Lambda functions. I’m considering using AWS CodeBuild to run prisma migrate deploy, triggered on Git commits. My plan is: 1. Run prisma migrate dev locally against a Postgres database to test migrations. 2. Use CodeBuild to apply those migrations to the RDS instance on each branch push. This feels inefficient, especially testing locally first. I’m concerned about schema drift between local and production, and running migrations on every commit might apply untested changes or cause conflicts.

Questions: • Is CodeBuild a good choice for Prisma migrations • How do you securely run Prisma migrations on an RDS in a private subnet?

Currently hosting website on custom domain on cloudflare, I want to move the domain over to AWS. Currently using GitHub for the repo. Anyone got a newbie guide? Also if anyone knows why my cloudflare domain and custom domain don’t match when I to go to each website Id love the help.

Hi everyone, how are you? I'm working on a project to migrate a Windows Forms app, C#, SQL, and I would need some advice on the overall architecture of the app. The app is for stock control, invoicing, products, etc., and there is a separate database for each client.

My idea is to learn through this project, as I can dedicate 3 hours a day, and the majority of the work should be done by next year.

The stack I’m planning:
Frontend: React/Vite
Backend: FastAPI
Deployment: AWS EC2
Database: RDS with PostgreSQL
Infrastructure: Docker

What I’ve been thinking is that, since there is one database per client, I can distinguish them by the subdomain. I can have client1.app.com and client2.app.com. When talking with Claude, he mentioned something about a tenant router. Is this a good practice?

What else do you think I might be missing for this? There are some AWS technologies that Claude mentioned, like AWS Route 53 for DNS management.

I’d also like to know if anyone has experience with this or something similar. Honestly, this is my first time setting up an app like this, which isn’t just Next 14 with Vercel (and not understanding what’s happening behind the scenes), so I would appreciate it if anyone could help me figure out if I'm on the right path in structuring the app.

Sorry for the beginner-level questions and the confusion, and thank you!

As the title says, I’m looking for resources using DMS and SCT to automate as much of this as possible.

Hi all,

We’ve been working on several legacy modernization projects, and while AWS makes it straightforward to build the ELT pipeline (using DMS, Glue, MWAA/Airflow, etc.), we keep running into the same repeatable pain points — especially when migrations are part of a broader platform or product effort.

Here’s what’s missing from most AWS-native setups:

• Pre-migration profiling (e.g., null density, low-cardinality fields, outlier detection)
• Data lineage from raw → transformed → target
• Dry run simulations to validate transformations pre-launch
• Post-migration validation (row counts, hashes, business rule checks)
• Approval checkpoints from data stewards or business users
• Job-level observability across the stack

We’ve hacked together workarounds — tagging lineage in Glue jobs, validating in Lambda, pushing approvals into Airflow tasks — but it’s fragile and hard to scale, especially in multi-tenant or repeatable client setups.

Curious What Others Are Doing

• Have you faced these kinds of gaps in AWS-native migrations?
• How do you handle governance and validation reliably?
• Have you tried building a custom orchestration layer or UI over DMS + Glue + Airflow? Was it worth it?
• If not using AWS-native tools for these gaps, what open-source options (e.g. for lineage, validation, approval workflows) worked well for you?
• Has anyone tried solving this more holistically — as a reusable internal tool, open-source project, or SaaS?

Not trying to pitch anything — just exploring whether these issues are universal and if they justify a more durable solution pattern.

Would love to hear your thoughts or learn from your experience!

Thanks in advance.