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Factiva VS Spark Streaming

Compare Factiva VS Spark Streaming and see what are their differences

Cyclr
Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Factiva logo Factiva

Built by Dow Jones, Factiva is a news aggregate service with a focus on helping business owners and investors stay ahead of the market curve.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Factiva Landing page
    Landing page //
    2023-03-17
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Factiva videos

+ Add

Review & Repeat Searches -- Factiva demo

More videos:

  • Review - Factiva Mobile - Get the News You Need Overview
  • Review - Finding the Australian Financial Review on Factiva

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

Category Popularity

0-100% (relative to Factiva and Spark Streaming)

Factiva

Spark Streaming

Sales Tools
100 100%
Sales Tools
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Data Integration
100 100%
Data Integration
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

Share your experience with using Factiva and Spark Streaming. For example, how are they different and which one is better?
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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.

Factiva mentions (0)

We have not tracked any mentions of Factiva yet. Tracking of Factiva 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 / 5 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 Factiva and Spark Streaming, you can also consider the following products

Software AG webMethods - Software AG’s webMethods enables you to quickly integrate systems, partners, data, devices and SaaS applications

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

IBM MQ - IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.

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

Oracle BAM - BAM Main OTN Page

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

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