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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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.

Description

You determine the cluster size, node specifications, and a range of services, while Yandex Data Proc effortlessly sets up and configures Spark, Hadoop clusters, and additional components. Collaboration is enhanced through the use of Zeppelin notebooks and various web applications via a user interface proxy. You maintain complete control over your cluster with root access for every virtual machine. Moreover, you can install your own software and libraries on active clusters without needing to restart them. Yandex Data Proc employs instance groups to automatically adjust computing resources of compute subclusters in response to CPU usage metrics. Additionally, Data Proc facilitates the creation of managed Hive clusters, which helps minimize the risk of failures and data loss due to metadata issues. This service streamlines the process of constructing ETL pipelines and developing models, as well as managing other iterative operations. Furthermore, the Data Proc operator is natively integrated into Apache Airflow, allowing for seamless orchestration of data workflows. This means that users can leverage the full potential of their data processing capabilities with minimal overhead and maximum efficiency.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
Apache Airflow No 
Apache Flume No 
Apache HBase No 
Apache Hive No 
Apache Zeppelin No 
Hadoop No 
Matplotlib No 
NumPy No 
PubSub+ Platform Yes 
Python No 
TensorFlow No 
Yandex Cloud No 
Yandex DataSphere No 
pandas No 
scikit-image No 

Integrations

Apache Spark Yes 
Apache Airflow Yes 
Apache Flume Yes 
Apache HBase Yes 
Apache Hive Yes 
Apache Zeppelin Yes 
Hadoop Yes 
Matplotlib Yes 
NumPy Yes 
PubSub+ Platform No 
Python Yes 
TensorFlow Yes 
Yandex Cloud Yes 
Yandex DataSphere Yes 
pandas Yes 
scikit-image Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0.19 per hour
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

spark.apache.org/streaming/

Vendor Details

Company Name

Yandex

Founded

1997

Country

Russia

Website

cloud.yandex.com/en/services/data-proc

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