vs DBT

Transformation System Capabilities Comparison vs dbt

Bringing All Your ETLT Needs Under One Roof

The diversity of data combined with an overburdened IT-team is pushing more and more of the ingest and data transformation work to the analyst. The current toolsets to help support these more complex activities can result in 4 common challenges:

  1. Ingestion and Transformation in 2 or more products.
  2. Build experiences that are text based with limited context, rather than rich visual interfaces that provide immediate feedback.
  3. Highly complex SQL that is difficult to understand, debug, and maintain, and as a result greatly slow the velocity of innovation.   
  4. No ability to leverage more powerful languages like Python.

There is an alternative: Ascend brings fun and excitement back to ETLT by solving all of these challenges in an extremely elegant way by combining ingest from anywhere with SQL and Python transformations, and an innovative GUI that enables analysts to break apart their complex SQL into a series of connected, visual components that are self-documenting, easy to understand, and wickedly fast to iterate and innovate.

With Ascend Data Engineering Platform, you can stop simply watching your jobs run and see your data flow.

Capability Comparison by Category


Any Data, Anywhere, Any Format
Connect to any lake, queue, warehouse, database or API.
Natively Embedded Not Available
Change Detection
Detect and ingest new, updated, and deleted data automatically.
Track where your data is located, how often it changes, what has already been ingested.
Fully Automated Not Available
Data Profiling
Auto-profile every piece of data being ingested.
Fully Automated Not Available
Automated Data Reformatting
Aggregate small files into single partitions for processing efficiency, and automatically convert any incoming format to Snappy compressed Parquet files.
Fully Automated Not Available


Declarative Data Pipelines
Enable developers to focus code solely on WHAT they want done to the data. Zero code needed to orchestrate the underlying work on HOW to achieve the desired state.
Native to the Platform, combine SQL, Java, Scala, Python code Native to the Platform, only SQL
Interactive Pipeline Builder
Navigate live data pipelines, from source to sink, and everything in between. Trace data lineage, preview data, and prototype changes in minutes instead of days.
Fully Automated No DAG builder, basic lineage tracking
Queryable Pipeline
Query every component of the pipeline as a table to explore, validate, and manipulate data.
Fully Automated Fully Automated
Git & CICD Integration
Use any CI/CD solution such as Jenkins or CircleCI.
Supported Supported


Intelligent Persistence
Persist data at every stage of the pipeline to minimize compute cost, pinpoint defects,
and massively reduce debug/restart time.
Fully Automated Rudimentary incremental processing, no mid-pipeline restart
Data & Job Deduplication
Safely deduplicate work across all pipelines, ensuring your pipelines run fast, efficiently, and cost effectively, while making branching and merging as easy as it is with code.
Fully Automated Not Available
Dynamic Partitioning
Auto-partition data to optimize propagation of incremental changes in data.
Fully Automated Not Available
Automated Backfill
Efficient management of back-fill and late arriving data.
Supported Not Available
Automated Spark Management
Optimize Spark parameters for every job, based on data and code profiles, and manage all aspects of jobs being sent for processing on the Spark engine.
Supported Not Available


Notebook Connectors
Connect Jupyter, Zeppelin, and more directly to data pipelines for access to data as it moves through your data pipelines.
Supported Supported
BI & Data Visualization
Feed data directly to your BI and Data Visualization tools.
Available Available
File-Based Access
Get direct access to data at every stage of the pipeline (.snappy.parquet) files for efficient processing by other big data systems.
Available Available
Record APIs & SDKs
Read records from any stage of any data pipeline via JDBC or high-speed records API.
Available Available


Automated Cataloging
Provide an organized and searchable access to all code and data under the platform’s management, with automated registration of new data sets and code.
Fully Automated Not Available
Data Lineage Tracking
Instantly visualize the lineage of any column of data from sink to source, including all operations performed on it.
Fully Automated Not Available
Resource & Cost Reporting
For every piece of data in the system, report the resources required, historically and at present, to produce and maintain it.
Fully Automated Not Available
Activity Monitoring & Reporting
Track of all user, data, and system events, with integration into external platforms such as Splunk and PagerDuty.
Fully Automated Partial Functionality
Secure Data Feeds
Reuse end result data sets in other pipelines through a subscribe workflow. Provide external access to end result data sets via API.
Fully Automated Not Available
Data Garbage Crawl
Ability to crawl data storage systems, automatically deleting data that has been abandoned and is no longer associated with active data pipelines.
Fully Automated Not Available

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