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 414 GitHub stars and 509 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 Spark
    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

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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          related Talend posts

          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
          • 1
            Easy to create DAG and execute it
          CONS OF AWS DATA PIPELINE
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            related AWS Data Pipeline posts

            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
            • 9
              Managed Hive Metastore
            CONS OF AWS GLUE
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              related AWS Glue posts

              Pardha Saradhi
              Technical Lead at Incred Financial Solutions · | 6 upvotes · 69.5K 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.

              See more

              Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

              1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
              2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
              3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
              4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
              5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
              6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
              7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
              8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!
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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)
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                Free (Open Source)
              • 7
                Simple-to-use
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                Reactive with back-pressure
              • 5
                Scalable horizontally as well as vertically
              • 4
                Fast prototyping
              • 3
                Bi-directional channels
              • 2
                Data provenance
              • 2
                Built-in graphical user interface
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                End-to-end security between all nodes
              • 2
                Can handle messages up to gigabytes in size
              • 1
                Hbase support
              • 1
                Kudu support
              • 1
                Hive support
              • 1
                Slack integration
              • 1
                Support for custom Processor in Java
              • 1
                Lot of articles
              • 1
                Lots of documentation
              CONS OF APACHE NIFI
              • 2
                HA support is not full fledge
              • 2
                Memory-intensive

              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
              • 50
                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
              • 5
                Complex workflows
              • 5
                Python
              • 3
                Custom operators
              • 3
                Apache project
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                Good api
              • 2
                Dashboard
              CONS OF AIRFLOW
              • 2
                Running it on kubernetes cluster relatively complex
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                Open source - provides minimum or no support
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                Logical separation of DAGs is not straight forward
              • 1
                Observability is not great when the DAGs exceed 250

              related Airflow posts

              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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              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?

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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
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                Best Performances on large datasets
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                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
              • 1
                Data stays in your cloud account
              • 1
                Multicloud
              CONS OF DATABRICKS
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                related Databricks posts

                Jan Vlnas
                Developer Advocate at Superface · | 5 upvotes · 30.3K 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.

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                Apache Spark logo

                Apache Spark

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                Fast and general engine for large-scale data processing
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                PROS OF APACHE SPARK
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                  Open-source
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                  Fast and Flexible
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                  Great for distributed SQL like applications
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                  One platform for every big data problem
                • 6
                  Easy to install and to use
                • 3
                  Works well for most Datascience usecases
                • 2
                  In memory Computation
                • 2
                  Interactive Query
                • 2
                  Machine learning libratimery, Streaming in real
                CONS OF APACHE SPARK
                • 3
                  Speed

                related Apache Spark posts

                Eric Colson
                Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.6M views

                The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

                Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

                At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

                For more info:

                #DataScience #DataStack #Data

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                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.3M views

                Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

                Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

                https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

                (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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