Ascend.io vs DBT
Bringing All Your ELT 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:
Ingestion, Transformation, Orchestration, Reverse-ETL, and Observability in 5 or more products.
Build experiences that are text based with limited context, rather than rich visual interfaces that provide immediate feedback.
Highly complex SQL that is difficult to understand, debug, and maintain, and as a result greatly slow the velocity of innovation.
Limited ability to leverage more powerful languages like Python.
There is an alternative: Ascend brings fun and excitement back to ELT 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’s Data Pipeline Automation Platform, you can stop simply watching your jobs run and see your data flow!
Capability Comparison by Category
INGEST CAPABILITIES
| ASCEND | dbt | |
|---|---|---|
|
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 |
TRANSFORM CAPABILITIES
| ASCEND | dbt | |
|---|---|---|
|
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 |
ORCHESTRATE CAPABILITIES
| ASCEND | dbt | |
|---|---|---|
|
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 |
DELIVER CAPABILITIES
| ASCEND | dbt | |
|---|---|---|
|
Reverse-ETL Move data into 100's of external systems with an extensive library of configuration-based connectors. |
Available | Not Available |
|
Destination Table Management Automatically handles changes to schema in external systems. |
Available | Not Available |
|
Data Sharing Publish/subscribe to data sets that are automatically linked and orchestrated with no additional code. |
Available | Not Available |
OBSERVE CAPABILITIES
| ASCEND | dbt | |
|---|---|---|
|
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 component 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 |
|
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 |