Logo vs Azure Data Factory

Orchestration System Capabilities Comparison 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.


In minutes, build, deploy, and manage production-ready, declarative data pipelines using flexible coding options.


Build massively scalable dataflows including complex business logic with requiring additional costly resources.


Connect to any data lake, warehouse, database, stream, or API as well as BI tools, notebooks, and other big data systems.


Optimize data pipelines and infrastructure with dynamic resourcing, intelligent persistence, and advanced deduplication.

Capability Comparison by Category


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


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 data 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


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


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


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

Ready for intelligent, declarative data orchestration?