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Spark Streaming VS Amazon Elasticsearch Service

Compare Spark Streaming VS Amazon Elasticsearch Service and see what are their differences

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Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

Amazon Elasticsearch Service logo Amazon Elasticsearch Service

Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Amazon Elasticsearch Service Landing page
    Landing page //
    2023-03-13

Spark Streaming features and specs

  • Scalability
    Spark Streaming is highly scalable and can handle large volumes of data by distributing the workload across a cluster of machines. It leverages Apache Spark's capabilities to scale out easily and efficiently.
  • Integration
    It integrates seamlessly with other components of the Spark ecosystem, such as Spark SQL, MLlib, and GraphX, allowing for comprehensive data processing pipelines.
  • Fault Tolerance
    Spark Streaming provides fault tolerance by using Spark's micro-batching approach, which allows the system to recover data in case of a failure.
  • Ease of Use
    Spark Streaming provides high-level APIs in Java, Scala, and Python, making it relatively easy to develop and deploy streaming applications quickly.
  • Unified Platform
    It provides a unified platform for both batch and streaming data processing, allowing reuse of code and resources across different types of workloads.

Possible disadvantages of Spark Streaming

  • Latency
    Spark Streaming operates on a micro-batch processing model, which introduces latency compared to real-time processing. This may not be suitable for applications requiring immediate responses.
  • Complexity
    While it integrates well with other Spark components, building complex streaming applications can still be challenging and may require expertise in distributed systems and stream processing concepts.
  • Resource Management
    Efficiently managing cluster resources and tuning the system can be difficult, especially when dealing with variable workload and ensuring optimal performance.
  • Backpressure Handling
    Handling backpressure effectively can be a challenge in Spark Streaming, requiring careful management to prevent resource saturation or data loss.
  • Limited Windowing Support
    Compared to some stream processing frameworks, Spark Streaming has more limited options for complex windowing operations, which can restrict some advanced use cases.

Amazon Elasticsearch Service features and specs

  • Scalability
    Amazon OpenSearch Service allows for easy scalability of clusters based on demand, without the need to manually manage infrastructure.
  • Managed Service
    The service is fully managed by AWS, including automatic backups, monitoring, and updates, reducing operational overhead for users.
  • Integration
    Seamless integration with other AWS services such as Amazon VPC, AWS Lambda, and Amazon S3 for streamlined workflows and enhanced data analysis capabilities.
  • Security
    Built-in security features such as VPC support, IAM policies, and data encryption at rest and in transit ensure data is well-protected.
  • High Availability
    The service offers multiple availability zones for high availability and durability of data.
  • Cost Efficiency
    Comes with a pay-as-you-go pricing model which allows users to efficiently manage costs and scale resources according to budget and usage.

Possible disadvantages of Amazon Elasticsearch Service

  • Vendor Lock-in
    Relying on Amazon for Elasticsearch service can lead to vendor lock-in, making it hard to transition to other services or platforms without significant effort.
  • Cost
    While the pricing model is flexible, the costs can accumulate quickly with large-scale deployments, potentially leading to high expenses.
  • Limited Customization
    Being a managed service, it offers less flexibility and customization options compared to self-managed solutions.
  • Version Lag
    The service may not always be in sync with the latest releases and may lag behind the open-source Elasticsearch in terms of new features.
  • Complexity
    Setting up and optimizing Amazon OpenSearch Service can be complex, requiring a good understanding of both the service and underlying Elasticsearch technology.

Spark Streaming videos

+ Add

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

Amazon Elasticsearch Service videos

+ Add

Amazon Elasticsearch Service Deep Dive - AWS Online Tech Talks

More videos:

  • Review - Moving From Self-Managed Elasticsearch to Amazon Elasticsearch Service - AWS Online Tech Talks

Category Popularity

0-100% (relative to Spark Streaming and Amazon Elasticsearch Service)

Spark Streaming

Amazon Elasticsearch Service

Stream Processing
67 67%
Stream Processing
33% 33
Custom Search Engine
0 0%
Custom Search Engine
100% 100
Data Management
100 100%
Data Management
0% 0
Custom Search
0 0%
Custom Search
100% 100

User comments

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Social recommendations and mentions

Based on our record, Amazon Elasticsearch Service should be more popular than Spark Streaming. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Spark Streaming mentions (5)

  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / 28 days ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Spark Streaming: Offers micro-batch processing, suitable for high-throughput scenarios that can tolerate slightly higher latency. https://spark.apache.org/streaming/. - Source: dev.to / 9 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / over 1 year ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 2 years ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 3 years ago

Amazon Elasticsearch Service mentions (11)

  • OpenSearch for humans
    This change triggered a response from Amazon Web Services, which offered OpenSearch (data store and search engine) and OpenSearch Dashboards (visualization and user interface) as Apache2.0 licensed open-source projects. - Source: dev.to / about 1 year ago
  • OpenSearch as Vector DB: Supercharge Your LLM
    Amazon OpenSearch Service allows you to deploy a secured OpenSearch cluster in minutes. - Source: dev.to / almost 2 years ago
  • Building Serverless Applications with AWS - Data
    If yes to these, then OpenSearch is where you are looking. I rarely ever use OpenSearch on its own but usually pair it with DynamoDB. The performance of DDB and the power of searching with OpenSearch make a nice combination. And as with most things with Serverless, pick the right tool for the job. And when it comes to Data, there are so many choices because each one of these is specific to the problem it solves. - Source: dev.to / almost 2 years ago
  • Advice on a simple database architecture
    Have you looked into Amazon OpenSearch Service (https://aws.amazon.com/opensearch-service/)? You should be able to load the log files into that service and then query it there. Should simplify things a lot. Source: about 2 years ago
  • AWS Beginner's Key Terminologies
    Elasticsearch (analytics) An open-source, real-time distributed search and analytics engine used for full-text search, structured search, and analytics. OpenSearch was developed by the Elastic company. Amazon OpenSearch Service (OpenSearch Service) is an AWS-managed service for deploying, operating and scaling OpenSearch in the AWS Cloud. Https://aws.amazon.com/opensearch-service/. - Source: dev.to / over 2 years ago
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What are some alternatives?

When comparing Spark Streaming and Amazon Elasticsearch Service, you can also consider the following products

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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