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.
Data Integration facilitates the combination of data from diverse sources into a unified view, crucial for businesses to make informed decisions and enhance operational efficiency. With comprehensive solutions available, organizations can streamline their data workflows. Data Integration solutions are vital for businesses aiming to handle large volumes of data efficiently. These solutions help in synchronizing data from multiple sources, ensuring consistent data across platforms, and...
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.