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What is Yandex Data Proc?

You decide on the cluster size, node specifications, and various services, while Yandex Data Proc takes care of the setup and configuration of Spark and Hadoop clusters, along with other necessary components. The use of Zeppelin notebooks alongside a user interface proxy enhances collaboration through different web applications. You retain full control of your cluster with root access granted to each virtual machine. Additionally, you can install custom software and libraries on active clusters without requiring a restart. Yandex Data Proc utilizes instance groups to dynamically scale the computing resources of compute subclusters based on CPU usage metrics. The platform also supports the creation of managed Hive clusters, which significantly reduces the risk of failures and data loss that may arise from metadata complications. This service simplifies the construction of ETL pipelines and the development of models, in addition to facilitating the management of various iterative tasks. Moreover, the Data Proc operator is seamlessly integrated into Apache Airflow, which enhances the orchestration of data workflows. Thus, users are empowered to utilize their data processing capabilities to the fullest, ensuring minimal overhead and maximum operational efficiency. Furthermore, the entire system is designed to adapt to the evolving needs of users, making it a versatile choice for data management.

What is Apache Spark?

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.

Media

Media

Integrations Supported

Apache HBase
Apache Hive
Apache Zeppelin
Hadoop
Yandex DataSphere

Integrations Supported

Apache HBase
Apache Hive
Apache Zeppelin
Hadoop
Databricks
Deequ
Flyte
Gemini Enterprise Agent Platform Notebooks
IBM SPSS Modeler
Kedro
Mage Static Data Masking
Oracle Cloud Infrastructure Data Flow
Pavilion HyperOS
Prophecy
Retina
ScaleOps
Tonic Ephemeral
Union Cloud
Yottamine

API Availability

Has API

API Availability

Pricing Information

$0.19 per hour
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Yandex

Date Founded

1997

Company Location

Russia

Company Website

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

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

spark.apache.org

Categories and Features

Data Pipeline

Not specified

Categories and Features

Big Data

Not specified

Data Analysis

Not specified

Data Modeling

Not specified

Query Engines

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation

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