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Amazon EMR vs Apache Spark: What are the differences?

Amazon EMR is a cloud-based big data platform that allows users to process and analyze large datasets using popular frameworks like Apache Spark and Apache Hadoop. Apache Spark, on the other hand, is an open-source distributed computing system that provides fast and versatile data processing capabilities, supporting a wide range of applications for big data analytics. Let's explore the key differences between the two:

  1. Data Processing Framework: Amazon EMR is a managed service that allows users to process large amounts of data using frameworks such as Apache Spark, Apache Hadoop, and more. On the other hand, Apache Spark is a distributed computing system that specifically focuses on data processing and analytics. While both Amazon EMR and Apache Spark are capable of processing big data, Apache Spark is known for its speed and in-memory computing capabilities, making it a popular choice for real-time analytics and machine learning tasks.

  2. Ease of Use: Amazon EMR provides a managed environment for running big data frameworks, making it easier for users to set up and manage their data processing workflows. It offers pre-configured environments and automated scaling, allowing users to focus on their analysis rather than infrastructure management. In contrast, Apache Spark requires users to set up their own cluster infrastructure and manage various aspects of the system, which can be more complex and time-consuming.

  3. Supported Services: Amazon EMR is an umbrella service that supports various big data frameworks, including Apache Spark. It integrates with other AWS services such as Amazon S3, Amazon Redshift, and Amazon DynamoDB, making it easier to move, transform, and analyze data across different services. On the other hand, Apache Spark can be run on various platforms and cloud providers, offering more flexibility in terms of deployment options.

  4. Ecosystem and Libraries: Amazon EMR provides a wide range of pre-installed libraries and tools that users can leverage for their data processing tasks. It offers support for popular big data tools like Apache Hive, Apache Pig, and Apache Zeppelin. In contrast, Apache Spark has its own ecosystem of libraries and tools that can be used for data processing and analytics. It offers libraries like Spark SQL, MLlib, and GraphX, enabling users to perform various data-related tasks within the Spark environment.

  5. Pricing Model: Amazon EMR follows a pay-as-you-go pricing model, where users are billed based on the resources consumed during their data processing tasks. The pricing includes costs for EC2 instances, storage, and data transfer. In contrast, Apache Spark itself is an open-source project, and the costs associated with running Spark depend on the infrastructure and resources used by the user. Users can choose to deploy Spark on their own hardware or on cloud providers, allowing for more control over the cost aspect.

  6. Scalability and Fault Tolerance: Amazon EMR offers automatic scaling capabilities, allowing users to add or remove compute nodes based on the workload. It also provides built-in fault tolerance mechanisms, ensuring that data processing jobs are not affected by node failures. Apache Spark, being a distributed computing system, also offers scalability and fault tolerance features, allowing users to handle large datasets and handle failures gracefully. However, the level of scalability and fault tolerance can vary depending on the deployment environment and infrastructure setup.

In summary, Amazon EMR is a managed service that supports various big data frameworks, including Apache Spark, offering ease of use, integration with AWS services, and a pre-installed ecosystem of libraries. Apache Spark, on the other hand, is a distributed computing system specifically designed for data processing and analytics, known for its speed, flexibility in deployment options, and its own ecosystem of libraries and tools.

Advice on Amazon EMR and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 530.2K 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 · 372.6K 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 Amazon EMR
Pros of Apache Spark
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Massive data handling
  • 1
    Great support
  • 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 Amazon EMR
Cons of Apache Spark
    Be the first to leave a con
    • 4
      Speed

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    - No public GitHub repository available -

    What is Amazon EMR?

    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

    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.

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    What are some alternatives to Amazon EMR and Apache Spark?
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    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.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Azure HDInsight
    It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
    See all alternatives