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What is Spark Streaming?
Spark Streaming enhances Apache Spark's functionality by incorporating a language-driven API for processing streams, enabling the creation of streaming applications similarly to how one would develop batch applications. This versatile framework supports languages such as Java, Scala, and Python, making it accessible to a wide range of developers. A significant advantage of Spark Streaming is its ability to automatically recover lost work and maintain operator states, including features like sliding windows, without necessitating extra programming efforts from users. By utilizing the Spark ecosystem, it allows for the reuse of existing code in batch jobs, facilitates the merging of streams with historical datasets, and accommodates ad-hoc queries on the current state of the stream. This capability empowers developers to create dynamic interactive applications rather than simply focusing on data analytics. As a vital part of Apache Spark, Spark Streaming benefits from ongoing testing and improvements with each new Spark release, ensuring it stays up to date with the latest advancements. Deployment options for Spark Streaming are flexible, supporting environments such as standalone cluster mode, various compatible cluster resource managers, and even offering a local mode for development and testing. For production settings, it guarantees high availability through integration with ZooKeeper and HDFS, establishing a dependable framework for processing real-time data. Consequently, this collection of features makes Spark Streaming an invaluable resource for developers aiming to effectively leverage the capabilities of real-time analytics while ensuring reliability and performance. Additionally, its ease of integration into existing data workflows further enhances its appeal, allowing teams to streamline their data processing tasks efficiently.
What is Quickmetrics?
Start by accessing a simple link or leverage our client libraries that come with batching features. Keep track of signups, response times, monthly recurring revenue, and other important metrics, then present your insights on an attractive dashboard. Organize your key performance indicators into customized dashboards that provide a visually appealing view suitable for TV mode. You also have the option to send additional data to analyze differences across various categories. Easily integrate with NodeJS and Go through our effective and intuitive libraries. All your data is securely stored and accessible at a one-minute resolution, which we maintain throughout your subscription period. Motivate your team to work together and enjoy the visually stunning dashboards as a group. We have carefully crafted the system to ensure fast data loading. Furthermore, we’re committed to making data visualization not only informative but also engaging and beautiful, which is why we prioritize aesthetics in our designs. The aim is to create an enjoyable experience while you gain valuable insights from your data.
Integrations Supported
Apache Spark
PubSub+ Platform
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
$19 per month
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Apache Software Foundation
Date Founded
1999
Company Location
United States
Company Website
spark.apache.org/streaming/
Company Facts
Organization Name
Quickmetrics
Company Website
quickmetrics.io
Categories and Features
Categories and Features
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery