Alternatives to Apache Camel logo

Alternatives to Apache Camel

Kafka, ActiveMQ, Apache NiFi, Spring Batch, and RabbitMQ are the most popular alternatives and competitors to Apache Camel.
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What is Apache Camel and what are its top alternatives?

Apache Camel is an open-source integration framework that allows developers to easily integrate different systems using enterprise integration patterns. It provides a wide range of connectors and components for seamless integration with various technologies. However, setting up and configuring Apache Camel can be complex and challenging for beginners, and it may require a steep learning curve for some users.

  1. MuleSoft Anypoint Platform: MuleSoft Anypoint Platform is a comprehensive integration platform that offers graphical design tools, connectivity to over 120 applications and services, and unified API management. It provides a user-friendly interface for creating integrations and supports both cloud and on-premise deployments. Pros: Easy to use graphical interface, robust API management capabilities. Cons: Expensive pricing for enterprise features.
  2. Spring Integration: Spring Integration is part of the larger Spring ecosystem and provides a lightweight messaging framework for building enterprise integration solutions. It leverages Spring's dependency injection and configuration model, making it easy to integrate with other Spring projects. Pros: Seamless integration with other Spring projects, extensive documentation. Cons: Limited out-of-the-box connectors compared to Apache Camel.
  3. Talend: Talend is a data integration platform that offers a wide range of tools for connecting, accessing, and transforming data. It provides a drag-and-drop interface for building data pipelines and supports real-time data processing. Pros: Rich set of tools for data integration, visual design interface. Cons: Steeper learning curve compared to Apache Camel.
  4. Kafka Connect: Kafka Connect is a framework for building connectors between Kafka and other data systems. It provides a scalable and fault-tolerant way to stream data in and out of Apache Kafka. Pros: Built-in fault tolerance, seamless integration with Apache Kafka. Cons: Limited support for non-Kafka integrations.
  5. Zapier: Zapier is a no-code integration platform that allows users to connect hundreds of popular apps without any coding. It provides a simple interface for creating automated workflows and supports a wide range of triggers and actions. Pros: No coding required, extensive app integrations. Cons: Limited customization options compared to Apache Camel.
  6. Boomi: Boomi is a cloud-based integration platform that offers a visual interface for creating integrations between cloud and on-premise systems. It provides pre-built connectors for popular applications and services and supports real-time data synchronization. Pros: Intuitive visual interface, extensive library of connectors. Cons: Limited support for complex data transformations.
  7. AWS Step Functions: AWS Step Functions is a serverless orchestration service that allows users to coordinate multiple AWS services into serverless workflows. It provides a visual workflow editor and supports error handling and retries for reliable execution. Pros: Serverless architecture, seamless integration with AWS services. Cons: Limited support for non-AWS integrations.
  8. TIBCO BusinessWorks: TIBCO BusinessWorks is an integration platform that offers a visual development environment for building integrations and APIs. It supports a wide range of protocols and data formats and provides built-in support for monitoring and analytics. Pros: Visual development environment, extensive protocol support. Cons: Higher cost compared to open-source alternatives like Apache Camel.
  9. Microsoft Azure Logic Apps: Azure Logic Apps is a cloud-based integration platform that allows users to automate workflows and integrate systems using a visual designer. It provides hundreds of pre-built connectors for popular services and supports custom code and API integrations. Pros: Built-in connectors, seamless integration with Azure services. Cons: Limited customization options for complex workflows.
  10. Pipes: Pipes is a modern data integration platform that enables users to build data pipelines and workflows without any coding. It offers a simple visual interface for designing data transformations and supports real-time data processing. Pros: No-code platform, real-time data processing capabilities. Cons: Limited connectors compared to Apache Camel.

Top Alternatives to Apache Camel

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • ActiveMQ
    ActiveMQ

    Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License. ...

  • 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. ...

  • Spring Batch
    Spring Batch

    It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management. ...

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • 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. ...

  • 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. ...

  • Fuse
    Fuse

    It is a set of user experience development tools that unify design, prototyping and implementation of high quality, native apps for iOS and Android. ...

Apache Camel alternatives & related posts

Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
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    High-throughput
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    Distributed
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    Scalable
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    High-Performance
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    Durable
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    Publish-Subscribe
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    Simple-to-use
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    Open source
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    Written in Scala and java. Runs on JVM
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    Message broker + Streaming system
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    KSQL
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    Avro schema integration
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    Robust
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    Suport Multiple clients
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    Extremely good parallelism constructs
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    Partioned, replayable log
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    Simple publisher / multi-subscriber model
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    Fun
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    Flexible
CONS OF KAFKA
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    Non-Java clients are second-class citizens
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    Needs Zookeeper
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    Operational difficulties
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    Terrible Packaging

related Kafka posts

Nick Rockwell
SVP, Engineering at Fastly · | 46 upvotes · 3.5M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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ActiveMQ logo

ActiveMQ

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A message broker written in Java together with a full JMS client
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PROS OF ACTIVEMQ
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    Easy to use
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    Open source
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    Efficient
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    JMS compliant
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    High Availability
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    Scalable
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    Distributed Network of brokers
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    Persistence
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    Support XA (distributed transactions)
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    Docker delievery
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    Highly configurable
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    RabbitMQ
CONS OF ACTIVEMQ
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    ONLY Vertically Scalable
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    Support
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    Low resilience to exceptions and interruptions
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    Difficult to scale

related ActiveMQ posts

I want to choose Message Queue with the following features - Highly Available, Distributed, Scalable, Monitoring. I have RabbitMQ, ActiveMQ, Kafka and Apache RocketMQ in mind. But I am confused which one to choose.

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Naushad Warsi
software developer at klingelnberg · | 1 upvote · 779.5K views
Shared insights
on
ActiveMQActiveMQRabbitMQRabbitMQ

I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

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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)
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    Simple-to-use
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    Scalable horizontally as well as vertically
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    Reactive with back-pressure
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    Fast prototyping
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    Bi-directional channels
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    End-to-end security between all nodes
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    Built-in graphical user interface
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    Can handle messages up to gigabytes in size
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    Data provenance
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    Lots of documentation
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    Hbase support
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    Support for custom Processor in Java
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    Hive support
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    Kudu support
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    Slack integration
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    Lot of articles
CONS OF APACHE NIFI
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    HA support is not full fledge
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    Memory-intensive
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    Kkk

related Apache NiFi posts

John Calandra
Data Manager at The Garrett Group · | 8 upvotes · 359.1K views

There is a question coming... I am using Oracle VirtualBox to spawn 3 Ubuntu Linux virtual machines (VM). VM1 is being used as a data lake - just a place to store flat files. VM2 hosts Apache NiFi. VM3 hosts PostgreSQL. I have built a NiFi pipeline that reads flat files on VM1 and then pipes the data over to and inserts it into the Postgresql database. I left this setup alone for a while, and then something hiccupped on VM3, and I had to rebuild it. Now I cannot make a remote connection to Postgresql on VM3. I was using pgAdmin3 on VM3, but it kept throwing errors - I found out it went end-of-life in 2018 and uninstalled it. pgAdmin4 is out, but for some reason, I cannot get the APT utility to find/install it. I am trying to figure out the pgAdmin4 install problem and looking for a good alternative for pgAdmin4 that I can use to diagnose the remote database connection problem. Does anyone have any suggestions? Thanks in advance.

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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?

See more
Spring Batch logo

Spring Batch

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PROS OF SPRING BATCH
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      related Spring Batch posts

      RabbitMQ logo

      RabbitMQ

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      Open source multiprotocol messaging broker
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      PROS OF RABBITMQ
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        It's fast and it works with good metrics/monitoring
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        Ease of configuration
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        I like the admin interface
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        Easy to set-up and start with
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        Durable
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        Intuitive work through python
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        Standard protocols
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        Written primarily in Erlang
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        Simply superb
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        Completeness of messaging patterns
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        Scales to 1 million messages per second
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        Reliable
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        Distributed
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        Supports MQTT
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        Better than most traditional queue based message broker
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        Supports AMQP
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        Clusterable
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        Clear documentation with different scripting language
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        Great ui
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        Inubit Integration
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        Better routing system
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        High performance
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        Runs on Open Telecom Platform
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        Delayed messages
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        Reliability
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        Open-source
      CONS OF RABBITMQ
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        Too complicated cluster/HA config and management
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        Needs Erlang runtime. Need ops good with Erlang runtime
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        Configuration must be done first, not by your code
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        Slow

      related RabbitMQ posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 1.7M views
      Shared insights
      on
      CeleryCeleryRabbitMQRabbitMQ
      at

      As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

      Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

      #MessageQueue

      See more

      Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

      See more
      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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          related Talend posts

          Shared insights
          on
          TalendTalendSnapLogicSnapLogic

          SnapLogic Vs Talend: Which one to choose when you have a lot of transformation logic to be used huge volume of data load on everyday basis.

          . better monitor & support . better performance . easy coding

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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
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            Dashboard
          CONS OF AIRFLOW
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            Observability is not great when the DAGs exceed 250
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            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

          related Airflow posts

          Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

          Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

          There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

          Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

          Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

          Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

          See more

          We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

          See more
          Fuse logo

          Fuse

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          Mobile interfaces for your IT systems
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