What is our primary use case?
My main use case for Ascend.io is that we have been working with an e-commerce client that was struggling to manage the complexity of their ETL pipelines. The team was spending 80% of their time writing boilerplate Spark code and managing job failures on Amazon EMR. We implemented Ascend.io, acquiring it via the AWS Marketplace to transform their data engineering approach. We used the platform to orchestrate the automated data flows powering the recommendation engine, managing a volume of approximately 15 terabytes of data per month from diverse sources such as S3, RDS, databases, and external APIs. Ascend.io allowed us to replace thousands of lines of manual code with a declarative platform that autonomously manages the underlying AWS infrastructure.
The specific scenario where Ascend.io made the biggest difference was during the management of unexpected schema changes and schema drift from external data sources during peak sales periods. Working with multiple third-party APIs and vendors, we frequently encountered a situation where new columns were added or data types were changed without prior notice. In a traditional Spark environment on EMR, this would have triggered total pipeline failures requiring hours of manual work to clean up partial data and reprocess everything. With Ascend.io, thanks to its Data Awareness Engine, the platform handled this scenario intelligently.
What is most valuable?
The standout feature is the Data Awareness Engine, in my opinion the intelligent control plane. Unlike traditional orchestrators that run tasks based on schedules or external events, Ascend.io understands the state of the data. If a source file changes or transformation logic is updated, the engine automatically identifies only the impacted data partitions and recalculates exclusively those. This eliminated the need to write complex logic for partial reloads and ensures that downstream data is always consistent with the latest version of the code.
Ascend.io impacted my organization positively because it helped me solve my problem by solving our operational maintenance crisis. Previously, every time a Spark job failed, we had to manually intervene to clean up partial data and restart the pipeline. With Ascend.io, infrastructure management and checkpointing are fully automated. It drastically reduced our technical debt, allowing our data engineers to focus on business logic rather than cluster management or writing boilerplate ingestion code. Code reduction eliminated 60% to 70% of custom Spark code. Operational cost saw a 30% reduction in man-hours dedicated to pipeline maintenance and incident management. The meantime to recovery reduced from hours to minutes due to automatic failure tracking. With Ascend.io, you write what you want, not how to do it. It is a declarative approach and reduces code by 80%. This is very important to me.
A good feature is the integrated lineage because an instant visualization of data flow across all components is very useful.
What needs improvement?
Ascend.io can be improved regarding the initial learning curve because for those used to writing pure Spark code, a mindset shift is required to trust the tool's automation. Another area for improvement is the customization limit because while flexible in extremely niche use cases, the tool's abstraction can make low-level fine-tuning more complex than native Spark, in my opinion.
For how long have I used the solution?
I have been using Ascend.io since 2023.
What do I think about the stability of the solution?
Ascend.io is stable for me.
What do I think about the scalability of the solution?
The scalability regarding Ascend.io is excellent and truly cloud native. Ascend.io scales dynamically based on the workload. We started by processing small batches and scaled up to 15 terabytes per month without changing a single line of infrastructure code. It manages the underlying compute resources on AWS elastically, scaling up during processing windows like retail sales events and scaling down immediately after. This allowed us to scale the number of managed pipelines without any issues.
How are customer service and support?
I did not speak with customer support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I did not evaluate any other option.
How was the initial setup?
The customer was in charge of the setup, not me.
What about the implementation team?
Regarding pricing and licensing, our experience has been very positive due to the AWS Marketplace integration. The customer shared this feedback with us. Regarding setup cost, they were remarkably low because Ascend.io is a SaaS platform and we avoided the massive upfront investment usually required for building and configuring custom Spark clusters. This was perfect for us.
What was our ROI?
We obtained several metrics related to cost reduction. We eliminated 70% to 80% of custom Spark boilerplate code. Regarding operational efficiency, we saw a 30% reduction in man-hours dedicated to pipeline maintenance and bug fixing. Related to time to market, the average time to develop and deploy a new pipeline dropped from three weeks to just five days, representing a significant increase in release velocity. Another positive metric is data integrity because monitoring coverage increased from 30% to 80% of critical pipelines, and we plan to move to 100% of critical pipelines.
What's my experience with pricing, setup cost, and licensing?
Our experience has been very positive due to the AWS Marketplace integration. The customer shared this feedback with us. Regarding setup cost, they were remarkably low because Ascend.io is a SaaS platform and we avoided the massive upfront investment usually required for building and configuring custom Spark clusters.
What other advice do I have?
I recommend adopting Ascend.io, especially if you have lean teams and need to manage complex pipelines. Leverage the AWS Marketplace to integrate costs into your existing cloud budget. However, I suggest not approaching it as a simple ingestion tool, but rather investing time in correctly mapping your data services to fully exploit the platform's declarative logic. Ensure the team is trained on the shift from writing jobs to defining data flows to maximize release velocity. Ascend.io was the first solution for me. My company does not have a business relationship with this vendor other than being a customer.
I give this product a rating of 8.5 out of 10.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
