Apache Beam vs StreamSets

Overview

Apache Beam

Stacks183

Followers361

Votes14

StreamSets

Stacks53

Followers133

Votes0

Apache Beam vs StreamSets: What are the differences?

Introduction: In the world of data processing, Apache Beam and StreamSets are two popular tools that play a crucial role. Here, we will highlight key differences between Apache Beam and StreamSets.

Programming Paradigm: Apache Beam follows a unified batch and stream processing model, providing a consistent API for both types of data processing tasks. On the other hand, StreamSets focuses more on data ingestion and ETL processes, offering a visual drag-and-drop interface for quick pipeline development.
Scalability: Apache Beam is designed to run on various distributed processing backends, enabling scalability and fault-tolerance across different environments. StreamSets, however, is primarily focused on data movement within an organization and may not offer the same level of scalability as Apache Beam in distributed computing scenarios.
Community Support: Apache Beam has a strong open-source community backing it, leading to frequent updates, bug fixes, and additional features. StreamSets also has an active community, but it may not be as robust or extensive as Apache Beam's community support.
Flexibility: Apache Beam provides a high degree of flexibility by allowing developers to write their data processing logic in multiple languages such as Java, Python, and Go. StreamSets, on the other hand, relies more on the visual interface for designing data pipelines, which may limit the flexibility for advanced customizations.
Use Cases: Apache Beam is well-suited for complex data processing tasks that require advanced stream and batch processing capabilities, making it ideal for real-time analytics, machine learning pipelines, and large-scale data transformations. StreamSets, on the other hand, is more suitable for simpler data movement and ETL processes, making it a popular choice for data integration and data warehouse loading tasks.

In Summary, Apache Beam and StreamSets differ in programming paradigm, scalability, community support, flexibility, and use cases.

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CLI (Node.js)

Manual

Detailed Comparison

Apache Beam	StreamSets
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
-	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 183	Stacks 53
Followers 361	Followers 133
Votes 14	Votes 0
Pros & Cons
Pros 5 Cross-platform 5 Open-source 2 Unified batch and stream processing 2 Portable	Cons 2 No user community 1 Crashes
Integrations
No integrations available	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to Apache Beam, StreamSets?

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

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

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 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

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.

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.

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

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

Distributed SQL Query Engine for Big Data

Related Comparisons

Apache Beam vs StreamSets: What are the differences?

Programming Paradigm: Apache Beam follows a unified batch and stream processing model, providing a consistent API for both types of data processing tasks. On the other hand, StreamSets focuses more on data ingestion and ETL processes, offering a visual drag-and-drop interface for quick pipeline development.
Scalability: Apache Beam is designed to run on various distributed processing backends, enabling scalability and fault-tolerance across different environments. StreamSets, however, is primarily focused on data movement within an organization and may not offer the same level of scalability as Apache Beam in distributed computing scenarios.
Community Support: Apache Beam has a strong open-source community backing it, leading to frequent updates, bug fixes, and additional features. StreamSets also has an active community, but it may not be as robust or extensive as Apache Beam's community support.
Flexibility: Apache Beam provides a high degree of flexibility by allowing developers to write their data processing logic in multiple languages such as Java, Python, and Go. StreamSets, on the other hand, relies more on the visual interface for designing data pipelines, which may limit the flexibility for advanced customizations.
Use Cases: Apache Beam is well-suited for complex data processing tasks that require advanced stream and batch processing capabilities, making it ideal for real-time analytics, machine learning pipelines, and large-scale data transformations. StreamSets, on the other hand, is more suitable for simpler data movement and ETL processes, making it a popular choice for data integration and data warehouse loading tasks.

In Summary, Apache Beam and StreamSets differ in programming paradigm, scalability, community support, flexibility, and use cases.

Apache Beam vs StreamSets

Overview