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Spark Streaming VS AWS DataOps Development Kit

Compare Spark Streaming VS AWS DataOps Development Kit and see what are their differences

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Contents:
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  2. » Videos
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Spark Streaming logo Spark Streaming

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

AWS DataOps Development Kit logo AWS DataOps Development Kit

An open source development framework to help you build data workflows and modern data architecture on AWS.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • AWS DataOps Development Kit Landing page
    Landing page //
    2023-07-25

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.

AWS DataOps Development Kit features and specs

No features have been listed yet.

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

AWS DataOps Development Kit videos

No AWS DataOps Development Kit videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Spark Streaming and AWS DataOps Development Kit)

Spark Streaming

AWS DataOps Development Kit

Stream Processing
100 100%
Stream Processing
0% 0
Data Stores
0 0%
Data Stores
100% 100
Data Management
100 100%
Data Management
0% 0
Big Data Tools
0 0%
Big Data Tools
100% 100

User comments

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Social recommendations and mentions

Based on our record, Spark Streaming seems to be more popular. It has been mentiond 5 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 / 18 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 / about 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

AWS DataOps Development Kit mentions (0)

We have not tracked any mentions of AWS DataOps Development Kit yet. Tracking of AWS DataOps Development Kit recommendations started around Jul 2022.

What are some alternatives?

When comparing Spark Streaming and AWS DataOps Development Kit, 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.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

Splunk - Splunk's operational intelligence platform helps unearth intelligent insights from machine data.

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

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.

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