Ascend.io vs Azure Data Factory
Flexible Coding Systems To Meet All Data Orchestration Needs
The future of data teams and systems relies on robust data orchestration tools. As the foundation of crucial data science and analytics workflows, enterprise-grade data pipelines need to be fast, simple to create, easy to manage, massively scalable, and seamless to integrate with other data systems.
To help achieve this, low-code and no-code technologies have risen to prominence in recent years. However, these lower-code technologies use visual interfaces to abstract away the majority of coding required to build business logic, which greatly limits the flexibility to meet more enterprise-grade, complex business logic needs with custom code when needed.
There is an alternative: flex code dataflow automation systems that are massively scalable and declarative in design. These systems address both needs, where data analysts benefit from low-code features while data engineers can add raw code to handle special situations, under one unified framework.
Simple
In minutes, build, deploy, and manage production-ready, declarative data pipelines using flexible coding options.
Scalable
Build massively scalable dataflows including complex business logic with requiring additional costly resources.
Powerful
Connect to any data lake, warehouse, database, stream, or API as well as BI tools, notebooks, and other big data systems.
Smart
Optimize data pipelines and infrastructure with dynamic resourcing, intelligent persistence, and advanced deduplication.
Capability Comparison by Category
INGEST CAPABILITIES
| ASCEND | ADF | |
|---|---|---|
|
Any Data, Anywhere, Any Format Connect to any lake, queue, warehouse, database or API. |
Native to the Platform | Limited |
|
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 | Limited |
|
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 |
BUILD CAPABILITIES
| ASCEND | ADF | |
|---|---|---|
|
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 | Not Available |
|
Flexible Coding Options Deliver a unified frame work with low code options for ease of use and high code options for special, customized scenarios. |
Fully Automated | Not Available |
|
Interactive Pipeline Builder Navigate live data pipelines, from source to sink, and everything in between. Trace column lineage, preview data, and prototype changes in minutes instead of days. |
Fully Automated | Limited |
|
Queryable Pipeline Query every component of the pipeline as a table to explore, validate, and manipulate data. |
Fully Automated | Not Available |
|
Git & CICD Integration Use any CI/CD solution such as Jenkins or CircleCI. |
Fully Automated | Fully Automated |
RUN CAPABILITIES
| ASCEND | ADF | |
|---|---|---|
|
Intelligent Persistence Persist data at every stage of the pipeline to minimize compute cost, pinpoint defects, and massively reduce debug/restart time. |
Fully Automated | Not Available |
|
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 | Limited |
|
Dynamic Partitioning Auto-partition data to optimize propagation of incremental changes in data. |
Fully Automated | Limited |
|
Automated Backfill Efficient management of back-fill and late arriving data. |
Supported | Supported |
|
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 | Supported |
INTEGRATE CAPABILITIES
| ASCEND | ADF | |
|---|---|---|
|
Notebook Connectors Connect Jupyter, Zeppelin, and more directly to data pipelines for access to data as it moves through your data pipelines. |
Native to the Platform | Limited |
|
BI & Data Visualization Feed data directly to your BI and Data Visualization tools. |
Native to the Platform | Limited |
|
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. |
Fully Automated | Limited |
|
Record APIs & SDKs Read records from any stage of any data pipeline via JDBC or high-speed records API. |
Available | Limited |
GOVERN CAPABILITIES
| ASCEND | ADF | |
|---|---|---|
|
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 | Available |
|
Activity Monitoring & Reporting Track of all user, data, and system events, with integration into external platforms such as Splunk and PagerDuty. |
Fully Automated | Not Available |
|
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 |