Alternatives to CDAP logo

Alternatives to CDAP

Airflow, Apache Spark, Akutan, Apache NiFi, and StreamSets are the most popular alternatives and competitors to CDAP.
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What is CDAP and what are its top alternatives?

Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.
CDAP is a tool in the Big Data Tools category of a tech stack.
CDAP is an open source tool with 589 GitHub stars and 288 GitHub forks. Here’s a link to CDAP's open source repository on GitHub

Top Alternatives to CDAP

  • 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. ...

  • 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. ...

  • Akutan

    Akutan

    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world. ...

  • 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. ...

  • StreamSets

    StreamSets

    An end-to-end data integration platform to build, run, monitor and manage smart data pipelines to deliver continuous data for DataOps ...

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Apache Flink

    Apache Flink

    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala. ...

  • Amazon Athena

    Amazon Athena

    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. ...

CDAP alternatives & related posts

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
  • 45
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 11
    Cluster of workers
  • 10
    Extensibility
  • 5
    Open source
  • 4
    Python
  • 4
    Complex workflows
  • 3
    K
  • 2
    Custom operators
  • 2
    Apache project
  • 2
    Dashboard
  • 2
    Good api
CONS OF AIRFLOW
  • 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
  • 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

See more
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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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
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    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 · 2.1M 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 · 1M 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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Akutan logo

Akutan

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A Distributed Knowledge Graph Store
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PROS OF AKUTAN
    Be the first to leave a pro
    CONS OF AKUTAN
      Be the first to leave a con

      related Akutan posts

      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
      • 15
        Visual Data Flows using Directed Acyclic Graphs (DAGs)
      • 8
        Free (Open Source)
      • 7
        Simple-to-use
      • 5
        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
      • 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
        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?

      See more
      StreamSets logo

      StreamSets

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      An end-to-end platform for smart data pipelines
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      PROS OF STREAMSETS
        Be the first to leave a pro
        CONS OF STREAMSETS
        • 2
          No user community
        • 1
          Crashes

        related StreamSets posts

        Splunk logo

        Splunk

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        Search, monitor, analyze and visualize machine data
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        PROS OF SPLUNK
        • 2
          API for searching logs, running reports
        • 1
          Query engine supports joining, aggregation, stats, etc
        • 1
          Query any log as key-value pairs
        • 1
          Splunk language supports string, date manip, math, etc
        • 1
          Granular scheduling and time window support
        • 1
          Alert system based on custom query results
        • 1
          Custom log parsing as well as automatic parsing
        • 1
          Dashboarding on any log contents
        • 1
          Ability to style search results into reports
        • 1
          Rich GUI for searching live logs
        CONS OF SPLUNK
        • 1
          Splunk query language rich so lots to learn

        related Splunk posts

        Shared insights
        on
        KibanaKibanaSplunkSplunkGrafanaGrafana

        I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

        See more
        Apache Flink logo

        Apache Flink

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        Fast and reliable large-scale data processing engine
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        PROS OF APACHE FLINK
        • 15
          Unified batch and stream processing
        • 8
          Easy to use streaming apis
        • 8
          Out-of-the box connector to kinesis,s3,hdfs
        • 3
          Open Source
        • 1
          Low latency
        CONS OF APACHE FLINK
          Be the first to leave a con

          related Apache Flink posts

          Surabhi Bhawsar
          Technical Architect at Pepcus · | 7 upvotes · 548K views
          Shared insights
          on
          KafkaKafkaApache FlinkApache Flink

          I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

          See more

          I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

          See more
          Amazon Athena logo

          Amazon Athena

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          Query S3 Using SQL
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          PROS OF AMAZON ATHENA
          • 15
            Use SQL to analyze CSV files
          • 8
            Glue crawlers gives easy Data catalogue
          • 6
            Cheap
          • 5
            Query all my data without running servers 24x7
          • 4
            No data base servers yay
          • 3
            Easy integration with QuickSight
          • 2
            Query and analyse CSV,parquet,json files in sql
          • 2
            Also glue and athena use same data catalog
          • 1
            No configuration required
          • 0
            Ad hoc checks on data made easy
          CONS OF AMAZON ATHENA
            Be the first to leave a con

            related Amazon Athena posts

            I use Amazon Athena because similar to Google BigQuery , you can store and query data easily. Especially since you can define data schema in the Glue data catalog, there's a central way to define data models.

            However, I would not recommend for batch jobs. I typically use this to check intermediary datasets in data engineering workloads. It's good for getting a look and feel of the data along its ETL journey.

            See more

            Hi all,

            Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

            See more