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 257 GitHub stars and 366 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
            • 5
              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 · 21.3K 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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              Punith Ganadinni
              Senior Product Engineer · | 2 upvotes · 14.7K views

              Hey all, I need some suggestions in creating a replica of our RDS DB for reporting and analytical purposes. Cost is a major factor. I was thinking of using AWS Glue to move data from Amazon RDS to Amazon S3 and use Amazon Athena to run queries on it. Any other suggestions would be appreciable.

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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
              • 14
                Visual Data Flows using Directed Acyclic Graphs (DAGs)
              • 7
                Free (Open Source)
              • 5
                Simple-to-use
              • 4
                Reactive with back-pressure
              • 4
                Scalable horizontally as well as vertically
              • 3
                Bi-directional channels
              • 3
                Fast prototyping
              • 2
                Data provenance
              • 2
                Built-in graphical user interface
              • 2
                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
                Memory-intensive
              • 1
                HA support is not full fledge

              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
              • 4
                Python
              • 2
                Custom operators
              • 2
                K
              • 2
                Dashboard
              • 1
                Good api
              • 1
                Apache project
              CONS OF AIRFLOW
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                related Airflow posts

                Shared insights
                on
                Jenkins
                Airflow

                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 Functions
                Airflow

                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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                  CONS OF DATABRICKS
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                    related Databricks posts

                    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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                      One platform for every big data problem
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                      Easy to install and to use
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                      Great for distributed SQL like applications
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                      Works well for most Datascience usecases
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                      Machine learning libratimery, Streaming in real
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                      In memory Computation
                    • 0
                      Interactive Query
                    CONS OF APACHE SPARK
                    • 3
                      Speed

                    related Apache Spark posts

                    Eric Colson
                    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M 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 · 937K 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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