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  1. Stackups
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  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs StreamSets

Airflow vs StreamSets

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
StreamSets
StreamSets
Stacks53
Followers133
Votes0

Airflow vs StreamSets: What are the differences?

Introduction Airflow and StreamSets are both popular data integration and orchestration tools used in the big data and data engineering domain. While both tools have similarities in terms of features and functionality, there are several key differences that set them apart.

1. Airflow: Task-based Workflow Orchestration Airflow follows a task-based workflow orchestration approach. Jobs or tasks are defined as individual units of work, and dependencies between tasks are explicitly defined. Airflow provides a flexible and powerful scheduling capability, allowing users to schedule tasks based on time, data availability, or event triggers. This task-based approach gives users fine-grained control over dependency management and enables complex workflow design.

2. StreamSets: Data Flow Orchestration StreamSets, on the other hand, follows a data flow orchestration approach. It focuses on the movement and transformation of data streams rather than individual tasks. Users define data pipelines using connectors and processors, where data flows from one stage to another, undergoing transformations along the way. StreamSets offers a visual interface for designing and monitoring data flows, making it easier for users to construct complex ETL pipelines.

3. Airflow: Python-centric Workflow Definition Airflow uses Python as its primary workflow definition language. Users define tasks and workflows using Python code, which allows for maximum flexibility and customization. Airflow provides a rich set of operators and hooks for interacting with various data sources and systems. Python code can be used to implement custom functionality and logic, making it a popular choice among developers and data engineers.

4. StreamSets: GUI-based Pipeline Design StreamSets takes a graphical approach to pipeline design. Users can visually assemble pipelines by connecting pre-built stages and configuring their properties. The graphical interface provides a user-friendly way to design, test, and monitor pipelines without requiring extensive coding or programming skills. StreamSets also supports scripting and expression language for more advanced use cases, but the emphasis is on visual design.

5. Airflow: Plug-and-play Integration with External Systems Airflow offers seamless integration with a wide range of external systems and tools. It provides a rich set of connectors and hooks for interacting with various databases, cloud services, message queues, and more. Airflow can easily pull data from and push data to different systems, enabling users to design complex workflows involving multiple data sources and destinations.

6. StreamSets: Built-in Data Quality and Data Governance StreamSets puts a strong emphasis on data quality and data governance. It provides built-in data drift and anomaly detection capabilities, allowing users to monitor data streams for any unexpected changes. StreamSets also includes data lineage and metadata management features, which help users track the origin of data and ensure its reliability and compliance. These built-in quality and governance features make StreamSets a great choice for organizations with strict data integrity requirements.

In Summary, Airflow and StreamSets differ in their workflow orchestration approach, with Airflow focusing on task-based workflows and StreamSets on data flow orchestration. Airflow utilizes Python for workflow definition, while StreamSets offers a visual interface for pipeline design. Airflow has extensive integration capabilities, while StreamSets emphasizes data quality and governance.

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Advice on Airflow, StreamSets

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Airflow
Airflow
StreamSets
StreamSets

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.

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

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks
1.7K
Stacks
53
Followers
2.8K
Followers
133
Votes
128
Votes
0
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
Cons
  • 2
    No user community
  • 1
    Crashes
Integrations
No integrations available
HBase
HBase
Databricks
Databricks
Amazon Redshift
Amazon Redshift
MySQL
MySQL
gRPC
gRPC
Google BigQuery
Google BigQuery
Amazon Kinesis
Amazon Kinesis
Cassandra
Cassandra
Hadoop
Hadoop
Redis
Redis

What are some alternatives to Airflow, StreamSets?

Kafka

Kafka

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

RabbitMQ

RabbitMQ

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

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.

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.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

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