+ AWS

Combining the power of the world’s largest cloud with the world’s most advanced data orchestration platform. + AWS For Intelligent Data Orchestration

Streamlined Data Engineering

With its unprecedented level of automation, unlocks the value of all the major native AWS data services, including Redshift, Sagemaker, Kinesis, Aurora, and all the variants of RDS. 

Seamless Integration with the Full Data Stack also seamlessly integrates with third-party ISV platforms that are now available on AWS, such as Qubole, Databricks, Snowflake, Looker, Tableau, and many others. + AWS unlocks this ecosystem with an ease of transaction through the marketplace, and as a result, the enterprise has a broad array of technology choices to meet its business needs. 

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Faster Build Velocity
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Why Use on AWS?

SELF-SERVICE DATA PIPELINES on AWS raises team productivity of data engineers, data scientists, and data analysts with self-service data pipelines by replacing the complexity of data engineering with low-code, declarative configurations, and a choice of compute engines including Databricks, Qubole, Spark, Snowflake, and more.  This allows more of the data team to participate in the development of their data pipelines and eliminates data engineering bottlenecks. 


Designed to ingest data specifically for data pipelines, the extensive connector framework is capable of ingesting data from all AWS native databases and streams, warehouses, and APIs, as well as a vast range of third-party databases, applications and platforms. This often eliminates complex ingestion and staging machinery, reducing cost and streamlining data operations.


Based on a completely new, data-centric paradigm,’s DataAware intelligence governs data lineage for all pipelines with full history linking data and code, and allocates costs to pipeline operations so data teams know exactly where to tune their code. As a result, data engineers are relieved of writing 95% of the code that is typical for data pipelines built from scratch or with simple orchestration tools.

The Ascend Unified Data Engineering Platform on AWS

Data Lake Ingest

The Ascend platform enables the data team to manage the ingestion workflow starting at the source, with connectors that include blob stores, file systems, APIs, applications, databases and CDC, streams, custom python code, and much more.

The platform ingests data from anywhere into the customer’s data lake, and immediately enriches, transforms, and stages the data as defined with declarative Scala, SQL, Java, and Python instructions. continuously syncs with new arriving data in the sources, and DataAware Intelligence tracks all data lineage and user activity end-to-end, with governance controls for the entire data lake.

Cloud Migration

The Ascend Platform is built from the ground up to be cloud-native and requires zero infrastructure migration or adaptation from the on-prem infrastructure. implements the functions of legacy data pipelines with 95% less code. Customers extract the 5% that actually represents the business logic and drop it into the platform in a matter of hours or days.

This greatly simplifies data ops, and your data team spends 90% less time on maintenance and so they have the time to tackle new business cases and bring innovations to life.