Alternatives to Azure Data Factory logo

Alternatives to Azure Data Factory

Azure Databricks, Talend, AWS Data Pipeline, AWS Glue, and Apache NiFi are the most popular alternatives and competitors to Azure Data Factory.
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What is Azure Data Factory and what are its top alternatives?

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
Azure Data Factory is a tool in the Big Data Tools category of a tech stack.
Azure Data Factory is an open source tool with 463 GitHub stars and 563 GitHub forks. Here’s a link to Azure Data Factory's open source repository on GitHub

Top Alternatives to Azure Data Factory

  • Azure Databricks
    Azure Databricks

    Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • AWS Data Pipeline
    AWS Data Pipeline

    AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email. ...

  • AWS Glue
    AWS Glue

    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. ...

  • Apache NiFi
    Apache NiFi

    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. ...

  • Airflow
    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Databricks
    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • Apache Camel
    Apache Camel

    An open source Java framework that focuses on making integration easier and more accessible to developers. ...

Azure Data Factory alternatives & related posts

Azure Databricks logo

Azure Databricks

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Fast, easy, and collaborative Apache Spark–based analytics service
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PROS OF AZURE DATABRICKS
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    CONS OF AZURE DATABRICKS
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      related Azure Databricks posts

      Talend logo

      Talend

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      A single, unified suite for all integration needs
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      PROS OF TALEND
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        CONS OF TALEND
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          AWS Data Pipeline logo

          AWS Data Pipeline

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          Process and move data between different AWS compute and storage services
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          PROS OF AWS DATA PIPELINE
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            Easy to create DAG and execute it
          CONS OF AWS DATA PIPELINE
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            AWS Glue logo

            AWS Glue

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            Fully managed extract, transform, and load (ETL) service
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            PROS OF AWS GLUE
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              Managed Hive Metastore
            CONS OF AWS GLUE
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              related AWS Glue posts

              Will Dataflow be the right replacement for AWS Glue? Are there any unforeseen exceptions like certain proprietary transformations not supported in Google Cloud Dataflow, connectors ecosystem, Data Quality & Date cleansing not supported in DataFlow. etc?

              Also, how about Google Cloud Data Fusion as a replacement? In terms of No Code/Low code .. (Since basic use cases in Glue support UI, in that case, CDF may be the right choice ).

              What would be the best choice?

              See more
              Pardha Saradhi
              Technical Lead at Incred Financial Solutions · | 6 upvotes · 100K views

              Hi,

              We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.

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              Apache NiFi logo

              Apache NiFi

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              A reliable system to process and distribute data
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              PROS OF APACHE NIFI
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                Visual Data Flows using Directed Acyclic Graphs (DAGs)
              • 8
                Free (Open Source)
              • 7
                Simple-to-use
              • 5
                Scalable horizontally as well as vertically
              • 5
                Reactive with back-pressure
              • 4
                Fast prototyping
              • 3
                Bi-directional channels
              • 3
                End-to-end security between all nodes
              • 2
                Built-in graphical user interface
              • 2
                Can handle messages up to gigabytes in size
              • 2
                Data provenance
              • 1
                Lots of documentation
              • 1
                Hbase support
              • 1
                Support for custom Processor in Java
              • 1
                Hive support
              • 1
                Kudu support
              • 1
                Slack integration
              • 1
                Lot of articles
              CONS OF APACHE NIFI
              • 2
                HA support is not full fledge
              • 2
                Memory-intensive
              • 1
                Kkk

              related Apache NiFi posts

              I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

              For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

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              Airflow logo

              Airflow

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              A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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              PROS OF AIRFLOW
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                Features
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                Task Dependency Management
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                Beautiful UI
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                Cluster of workers
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                Extensibility
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                Open source
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                Complex workflows
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                Python
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                Good api
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                Apache project
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                Custom operators
              • 2
                Dashboard
              CONS OF AIRFLOW
              • 2
                Observability is not great when the DAGs exceed 250
              • 2
                Running it on kubernetes cluster relatively complex
              • 2
                Open source - provides minimum or no support
              • 1
                Logical separation of DAGs is not straight forward

              related Airflow posts

              Shared insights
              on
              AWS Step FunctionsAWS Step FunctionsAirflowAirflow

              I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.

              I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.

              I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?

              See more
              Shared insights
              on
              JenkinsJenkinsAirflowAirflow

              I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

              1. Trigger Matillion ETL loads
              2. Trigger Attunity Replication tasks that have downstream ETL loads
              3. Trigger Golden gate Replication Tasks
              4. Shell scripts, wrappers, file watchers
              5. Event-driven schedules

              I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

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              Databricks logo

              Databricks

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              A unified analytics platform, powered by Apache Spark
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              PROS OF DATABRICKS
              • 1
                Best Performances on large datasets
              • 1
                True lakehouse architecture
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                Scalability
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                Databricks doesn't get access to your data
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                Usage Based Billing
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                Security
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                Data stays in your cloud account
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                Multicloud
              CONS OF DATABRICKS
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                Jan Vlnas
                Developer Advocate at Superface · | 5 upvotes · 327.2K views

                From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

                I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

                Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

                See more
                Apache Camel logo

                Apache Camel

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                A versatile open source integration framework
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                PROS OF APACHE CAMEL
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                  Based on Enterprise Integration Patterns
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                  Has over 250 components
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                  Free (open source)
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                  Highly configurable
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                  Open Source
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                  Has great community
                CONS OF APACHE CAMEL
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                  related Apache Camel posts