Software Alternatives & Reviews

Elastic Cloud VS Spark Streaming

Compare Elastic Cloud VS Spark Streaming and see what are their differences

Elastic Cloud logo Elastic Cloud

The Oracle Exalogic Elastic Cloud Software is the unique set of software components, tools, and documentation required to make the Exalogic Elastic Cloud Hardware functional and usable as a platform for Oracle's Fusion Middleware and business applic…

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Elastic Cloud Landing page
    Landing page //
    2023-09-14
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Elastic Cloud videos

Elastic Cloud: Getting Started with Elasticsearch Service

More videos:

  • Review - Introduction to Amazon EC2 - Elastic Cloud Server & Hosting with AWS

Spark Streaming videos

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

Category Popularity

0-100% (relative to Elastic Cloud and Spark Streaming)
Custom Search Engine
100 100%
0% 0
Stream Processing
0 0%
100% 100
Custom Search
100 100%
0% 0
Data Management
0 0%
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 3 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.

Elastic Cloud mentions (0)

We have not tracked any mentions of Elastic Cloud yet. Tracking of Elastic Cloud recommendations started around Mar 2021.

Spark Streaming mentions (3)

  • 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 / 3 months 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 1 year 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 2 years ago

What are some alternatives?

When comparing Elastic Cloud and Spark Streaming, you can also consider the following products

Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.

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

Morpheus - Morpheus is integration software designed to help major cloud infrastructure work in harmony. For example, if a company has assets on both Google's and Amazon's cloud services, Morpheus helps bridge the gap to improve productivity.

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

Site Search 360 - Site Search 360 enhances and improves your built-in CMS or product search with autocompletion, semantic search, filters, facets, detailed analytics, and a whole lot of customization options.

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