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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Apache Spark and StreamSets
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 526.5K views

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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Replies (2)
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ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 369.3K views
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Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of Apache Spark
Pros of StreamSets
  • 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
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
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    Cons of Apache Spark
    Cons of StreamSets
    • 4
      Speed
    • 2
      No user community
    • 1
      Crashes

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    What is 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.

    What is StreamSets?

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

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    What companies use Apache Spark?
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    What are some alternatives to Apache Spark and StreamSets?
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Apache Beam
    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
    Apache Flume
    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
    See all alternatives