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Description
BeakerX is an extensive suite of kernels and enhancements designed for the Jupyter interactive computing platform. It offers support for the JVM, Spark clusters, and polyglot programming, alongside features like interactive visualizations, tables, forms, and publishing capabilities. Each of BeakerX's supported JVM languages, in addition to Python and JavaScript, is equipped with APIs for generating interactive time-series, scatter plots, histograms, heatmaps, and treemaps. The interactive widgets retain their functionality in both saved notebooks and those shared online, featuring specialized tools for managing large datasets, nanosecond precision, zooming capabilities, and export options. Additionally, BeakerX's table widget seamlessly integrates with pandas data frames, enabling users to easily search, sort, drag, filter, format, select, graph, hide, pin, and export data to CSV or clipboard, facilitating quick connections to spreadsheets. Furthermore, BeakerX includes a Spark magic interface, complete with graphical user interfaces for managing configuration, monitoring status and progress, and interrupting Spark jobs, allowing users the flexibility to either utilize the GUI or programmatically create their own SparkSession. In this way, it significantly enhances the efficiency and usability of data processing and analysis tasks within the Jupyter environment.
Description
Spark Streaming extends the capabilities of Apache Spark by integrating its language-based API for stream processing, allowing you to create streaming applications in the same manner as batch applications. This powerful tool is compatible with Java, Scala, and Python. One of its key features is the automatic recovery of lost work and operator state, such as sliding windows, without requiring additional code from the user. By leveraging the Spark framework, Spark Streaming enables the reuse of the same code for batch processes, facilitates the joining of streams with historical data, and supports ad-hoc queries on the stream's state. This makes it possible to develop robust interactive applications rather than merely focusing on analytics. Spark Streaming is an integral component of Apache Spark, benefiting from regular testing and updates with each new release of Spark. Users can deploy Spark Streaming in various environments, including Spark's standalone cluster mode and other compatible cluster resource managers, and it even offers a local mode for development purposes. For production environments, Spark Streaming ensures high availability by utilizing ZooKeeper and HDFS, providing a reliable framework for real-time data processing. This combination of features makes Spark Streaming an essential tool for developers looking to harness the power of real-time analytics efficiently.
API Access
Has API
API Access
Has API
Integrations
Apache Spark
PubSub+ Platform
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Two Sigma Open Source
Website
BeakerNotebook.com
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
United States
Website
spark.apache.org/streaming/
Product Features
Application Development
Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development
