Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

StreamSets

Stacks53

Followers133

Votes0

Apache Flink vs StreamSets: What are the differences?

Introduction:

Apache Flink and StreamSets are both popular tools in the data processing realm, each with its unique features and capabilities. Understanding the key differences between these two platforms is crucial for making an informed choice when embarking on a data processing project.

Processing Model: Apache Flink is a powerful open-source stream processing framework that focuses on low-latency and high-throughput data processing. It enables event-driven, fault-tolerant processing with support for both batch and streaming data. On the other hand, StreamSets is an open-source data operations platform that focuses on simplifying the process of creating, deploying, and operating data pipelines. It primarily caters to the needs of data engineers and pipeline developers, providing a user-friendly interface for designing complex data workflows.
Scalability: Apache Flink is known for its excellent scalability, allowing users to scale out their processing tasks horizontally to handle massive amounts of data. It inherently supports distributed computing and can seamlessly scale up to thousands of nodes. In contrast, StreamSets also offers scalability options but is more suitable for medium-sized data pipelines rather than ultra-large scale deployments. It provides a balance between performance and ease of use for smaller to mid-sized data processing tasks.
Integration Ecosystem: Apache Flink has a robust ecosystem of integrations with various data sources and sinks, including Kafka, Hadoop, Elasticsearch, and more. It provides a wide range of connectors and libraries for interacting with different data systems, making it versatile for diverse use cases. On the other hand, StreamSets also offers a good set of connectors for popular data platforms, enabling users to easily ingest and process data from multiple sources. However, its integration ecosystem may not be as extensive as that of Apache Flink.
Real-time Processing Capabilities: Apache Flink is highly optimized for real-time data processing, with sub-millisecond latency for stream processing tasks. It excels in scenarios where real-time analytics and low-latency responses are crucial, making it a preferred choice for applications requiring immediate data insights. StreamSets, while capable of real-time data processing, may not achieve the same level of performance as Apache Flink in terms of real-time responsiveness and low latency.
Community Support and Development: Apache Flink has a vibrant open-source community with active contributors continually enhancing the platform's features and performance. It benefits from a large user base and ongoing development efforts, ensuring updates and improvements are regularly released. StreamSets also has a supportive community, but it may not be as extensive as that of Apache Flink, leading to potentially slower adoption of new features and limited community-driven contributions.
Use Cases: The primary use case for Apache Flink is real-time stream processing, where low latency and high throughput are critical requirements. It is well-suited for applications like fraud detection, IoT data processing, and real-time analytics. In contrast, StreamSets excels in data integration and pipeline orchestration tasks, making it ideal for data migration, ETL processes, and data cleaning workflows.

In Summary, Apache Flink is renowned for its real-time processing capabilities and scalability in handling massive data volumes, while StreamSets excels in simplifying data pipeline creation and integration tasks for medium-scale processing jobs. Both platforms cater to different aspects of data processing, focusing on distinct use cases and user requirements.

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Advice on Apache Flink, StreamSets

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Comments

Detailed Comparison

Apache Flink	StreamSets
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.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	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
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 53
Followers 879	Followers 133
Votes 38	Votes 0
Pros & Cons
Pros 16 Unified batch and stream processing 8 Easy to use streaming apis 8 Out-of-the box connector to kinesis,s3,hdfs 4 Open Source 2 Low latency	Cons 2 No user community 1 Crashes
Integrations
YARN Hadoop Hadoop HBase Kafka	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to Apache Flink, 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.

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

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.