Data Pipeline Automation and Snowflake ETL

Data Pipeline Automation and Snowflake ETL

Ascend increases data engineering and analytics engineering productivity by 10X with the first fully integrated ETL platform for Snowflake

“The scale and power of Snowflake combined with the acceleration and automation of Ascend enable us to not only take on new data challenges and workloads, but deliver results faster than ever before.”

Laurent Bride

Chief Technology Officer at Komodo Health

Ascend unifies the core capabilities data engineers and analytics engineers need—data ingestion, transformation, delivery, orchestration, and observability—into a seamless experience on Snowflake​.
Data Ingestion

Connect to popular data sources with pre-built connectors, or create your own with Ascend’s flex-code interface.

Data Transformation

Normalize, enrich, filter, aggregate, and otherwise transform data in SnowflakeSQL, Python, Scala or Java. Drive Ascend programmatically with our Python SDK or build interactively through our visual interface.

Data Delivery

Reverse ETL any data set to any destination including popular databases, blob stores, and warehouses whether on-premises or cross-cloud.

Data Orchestration

Ascend’s DataAware automation is an industry first to incrementally propagate data across data pipelines, checkpoint data at each transform to guarantee the data integrity, enable point of failure restarts, and more.

Data Observability

Visualize activity across your pipelines, understand costs, trace lineage, monitor events, and send customized notifications.

Optimize Snowflake Workloads and Increase Productivity With
The Ascend Data Automation Cloud

Build Data Pipelines & Workloads Faster

Data teams can build critical data pipelines far faster with Ascend automation, reducing the amount of code and increasing the time spent running workloads and distributing results throughout the organization with Snowflake scale and speed.

Combine Ingest with Transformation

Ascend connects directly to source systems to ingest incremental data into Snowflake and seamlessly continues the incremental orchestration across all transformations to the final tables used by data analysts and scientists.

Increase Logic Reuse (and Productivity)

The Ascend platform increases the adoption and reuse of data products built by many individual contributors with features that foster transparency and observability of logic and data. This enables multiple data teams to produce single versions of truth by consolidating, coordinating, sharing and access controlling their Snowflake data pipelines and logic across the business.

“By leveraging Snowflake’s scale in combination with Ascend’s unified data and analytics engineering capabilities, data teams can accelerate productivity and time to value for data workloads and more simply achieve true data engineering success.”

Tarik Dwiek

Head of Technology Alliances at Snowflake

Learn More About Ascend's Unified Data Engineering and
Analytics Engineering Capabilities

Ready To Start With the Power and Scale of
Ascend + Snowflake?

Additional Resources

The New Data Scale Challenge
From struggling with data volume and infrastructures to scaling data team capacity—what is the answer to increasing bandwidth?
Whitepaper
DataAware Podcast
With a variety of guests from all facets of data engineering and associated teams, episodes look in-depth at the role of data engineering and data teams, trends, best (and worst) practices, real world use cases, and more.
PODCAST
A Deep Dive Into Data Orchestration at Harry's
Learn how the Harry's data science team expedited ingesting, transforming, and delivering retail data feeds into a new, robust shared data model.
Video