Apache Spark vs StreamSets

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

StreamSets

Stacks53

Followers133

Votes0

Apache Spark vs StreamSets: What are the differences?

Introduction

Apache Spark and StreamSets are two widely used technologies in the field of big data processing. While both are designed to handle large volumes of data, they have some key differences that distinguish them from each other. In this article, we will explore these differences in depth.

Integration with Hadoop Ecosystem: One of the major differences between Apache Spark and StreamSets is their integration with the Hadoop ecosystem. Apache Spark is primarily designed to work with the Hadoop ecosystem and seamlessly integrates with Hadoop Distributed File System (HDFS), Apache HBase, Apache Hive, and other components. On the other hand, StreamSets is a data integration platform that can work with various systems, including Hadoop, but does not have the same level of deep integration as Apache Spark.
Real-time Processing vs Batch Processing: Another key difference between Apache Spark and StreamSets is their primary focus on real-time processing and batch processing, respectively. Apache Spark is known for its real-time processing capabilities, allowing users to process and analyze data in near real-time, making it suitable for applications that require fast data processing. StreamSets, on the other hand, is focused on batch processing, where data is processed in batches rather than in real-time.
Programming Languages and APIs: Apache Spark and StreamSets also differ in terms of the programming languages and APIs they support. Apache Spark provides APIs in multiple languages, including Scala, Java, Python, and R, allowing developers to choose the language they are most comfortable with. StreamSets, on the other hand, provides a visual interface for designing data pipelines, making it more accessible for non-programmers and those who prefer a visual approach.
Data Transformation and Processing: When it comes to data transformation and processing, Apache Spark and StreamSets have different approaches. Apache Spark provides a rich set of transformations and processing operations, allowing users to manipulate and analyze data in various ways. StreamSets, on the other hand, focuses more on data integration and movement, providing tools for extracting, transforming, and loading data from various sources.
Scalability and Resource Management: Both Apache Spark and StreamSets are designed to handle large volumes of data, but they differ in terms of scalability and resource management. Apache Spark is known for its ability to scale horizontally, allowing users to add more nodes to the cluster to handle increasing workloads. StreamSets, on the other hand, is designed to be lightweight and can be easily deployed on smaller systems, making it suitable for use cases where scalability is not a primary concern.
Use Cases and Industry Adoption: Lastly, Apache Spark and StreamSets have different use cases and industry adoption. Apache Spark is widely used in industries such as finance, healthcare, and e-commerce, where real-time data processing and analytics are crucial. StreamSets, on the other hand, is popular in industries such as data integration, data engineering, and data governance, where the focus is more on data movement and transformation.

In summary, Apache Spark and StreamSets differ in terms of their integration with the Hadoop ecosystem, focus on real-time processing or batch processing, programming languages and APIs supported, approach to data transformation and processing, scalability and resource management capabilities, as well as their use cases and industry adoption.

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Advice on Apache Spark, 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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Detailed Comparison

Apache Spark	StreamSets
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.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 53
Followers 3.5K	Followers 133
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	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 Spark, 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.

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.

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.

Related Comparisons

Apache Spark vs StreamSets: What are the differences?

Introduction

Integration with Hadoop Ecosystem: One of the major differences between Apache Spark and StreamSets is their integration with the Hadoop ecosystem. Apache Spark is primarily designed to work with the Hadoop ecosystem and seamlessly integrates with Hadoop Distributed File System (HDFS), Apache HBase, Apache Hive, and other components. On the other hand, StreamSets is a data integration platform that can work with various systems, including Hadoop, but does not have the same level of deep integration as Apache Spark.
Real-time Processing vs Batch Processing: Another key difference between Apache Spark and StreamSets is their primary focus on real-time processing and batch processing, respectively. Apache Spark is known for its real-time processing capabilities, allowing users to process and analyze data in near real-time, making it suitable for applications that require fast data processing. StreamSets, on the other hand, is focused on batch processing, where data is processed in batches rather than in real-time.
Programming Languages and APIs: Apache Spark and StreamSets also differ in terms of the programming languages and APIs they support. Apache Spark provides APIs in multiple languages, including Scala, Java, Python, and R, allowing developers to choose the language they are most comfortable with. StreamSets, on the other hand, provides a visual interface for designing data pipelines, making it more accessible for non-programmers and those who prefer a visual approach.
Data Transformation and Processing: When it comes to data transformation and processing, Apache Spark and StreamSets have different approaches. Apache Spark provides a rich set of transformations and processing operations, allowing users to manipulate and analyze data in various ways. StreamSets, on the other hand, focuses more on data integration and movement, providing tools for extracting, transforming, and loading data from various sources.
Scalability and Resource Management: Both Apache Spark and StreamSets are designed to handle large volumes of data, but they differ in terms of scalability and resource management. Apache Spark is known for its ability to scale horizontally, allowing users to add more nodes to the cluster to handle increasing workloads. StreamSets, on the other hand, is designed to be lightweight and can be easily deployed on smaller systems, making it suitable for use cases where scalability is not a primary concern.
Use Cases and Industry Adoption: Lastly, Apache Spark and StreamSets have different use cases and industry adoption. Apache Spark is widely used in industries such as finance, healthcare, and e-commerce, where real-time data processing and analytics are crucial. StreamSets, on the other hand, is popular in industries such as data integration, data engineering, and data governance, where the focus is more on data movement and transformation.

Apache Spark vs StreamSets

Overview

Apache Spark vs StreamSets: What are the differences?