Top 10 Google Cloud Managed Service for Apache Spark Alternatives & Competitors

Top 10 Alternatives to Google Cloud Managed Service for Apache Spark Recently Reviewed By G2 Community

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Databricks
By Databricks Inc.
4.6/5
(759)
Product Description
Making big data simple
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Reviewers say compared to Google Cloud Managed Service for Apache Spark, Databricks is:
Better at meeting requirements
Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution
ETL Tools

Cloudera Data Platform
By Cloudera
4.1/5
(136)
Product Description
Cloudera Enterprise Core provides a single Hadoop storage and management platform that natively combines storage, processing and exploration for the enterprise.
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Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution
ETL Tools

Azure Data Factory
By Microsoft
4.6/5
(99)
Product Description
Azure Data Factory (ADF) is a fully managed, serverless data integration service designed to simplify the process of ingesting, preparing, and transforming data from diverse sources. It enables organizations to construct and orchestrate Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows in a code-free environment, facilitating seamless data movement and transformation across on-premises and cloud-based systems. Key Features and Functionality: - Extensive Connectivity: ADF offers over 90 built-in connectors, allowing integration with a wide array of data sources, including relational databases, NoSQL systems, SaaS applications, APIs, and cloud storage services. - Code-Free Data Transformation: Utilizing mapping data flows powered by Apache Spark™, ADF enables users to perform complex data transformations without writing code, streamlining the data preparation process. - SSIS Package Rehosting: Organizations can easily migrate and extend their existing SQL Server Integration Services (SSIS) packages to the cloud, achieving significant cost savings and enhanced scalability. - Scalable and Cost-Effective: As a serverless service, ADF automatically scales to meet data integration demands, offering a pay-as-you-go pricing model that eliminates the need for upfront infrastructure investments. - Comprehensive Monitoring and Management: ADF provides robust monitoring tools, allowing users to track pipeline performance, set up alerts, and ensure efficient operation of data workflows. Primary Value and User Solutions: Azure Data Factory addresses the complexities of modern data integration by providing a unified platform that connects disparate data sources, automates data workflows, and facilitates advanced data transformations. This empowers organizations to derive actionable insights from their data, enhance decision-making processes, and accelerate digital transformation initiatives. By offering a scalable, cost-effective, and code-free environment, ADF reduces the operational burden on IT teams and enables data engineers and business analysts to focus on delivering value through data-driven strategies.
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Reviewers say compared to Google Cloud Managed Service for Apache Spark, Azure Data Factory is:
Better at meeting requirements
More expensive
Categories in common with Google Cloud Managed Service for Apache Spark:
ETL Tools

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Azure Data Lake Store
By Microsoft
4.5/5
(40)
Product Description
Azure Data Lake Storage is a cloud-based, enterprise-grade data lake solution designed to store and analyze massive amounts of data in its native format. It enables organizations to eliminate data silos by providing a single storage platform that supports structured, semi-structured, and unstructured data. This service is optimized for high-performance analytics workloads, allowing businesses to derive insights from their data efficiently. Key Features and Functionality: - Scalability: Offers virtually unlimited storage capacity, accommodating data of any size and type without the need for upfront capacity planning. - Security: Provides robust security mechanisms, including encryption at rest, advanced threat protection, and integration with Microsoft Entra ID (formerly Azure Active Directory) for role-based access control. - Integration: Seamlessly integrates with various Azure services such as Azure Databricks, Azure Synapse Analytics, and Azure HDInsight, facilitating comprehensive data processing and analytics. - Cost Optimization: Allows independent scaling of storage and compute resources, supports tiered storage options, and offers lifecycle management policies to optimize costs. - Performance: Supports high-throughput and low-latency data access, enabling efficient processing of large-scale analytics queries. Primary Value and Solutions Provided: Azure Data Lake Storage addresses the challenges of managing and analyzing vast amounts of diverse data by offering a scalable, secure, and cost-effective storage solution. It eliminates data silos, enabling organizations to store all their data in a single repository, regardless of format or size. This unified approach facilitates seamless data ingestion, processing, and visualization, empowering businesses to unlock valuable insights and drive informed decision-making. By integrating with popular analytics frameworks and Azure services, it streamlines the development of big data solutions, reducing time-to-insight and enhancing overall productivity.
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Reviewers say compared to Google Cloud Managed Service for Apache Spark, Azure Data Lake Store is:
More expensive
Better at meeting requirements
Better at support
Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution

Apache NiFi
By The Apache Software Foundation
4.2/5
(26)
Product Description
Apache NiFi is an open-source data integration platform designed to automate the flow of information between systems. It enables users to design, manage, and monitor data flows through an intuitive, web-based interface, facilitating real-time data ingestion, transformation, and routing without extensive coding. Originally developed by the National Security Agency (NSA) as "NiagaraFiles," NiFi was released to the open-source community in 2014 and has since become a top-level project under the Apache Software Foundation. Key Features and Functionality: - Intuitive Graphical Interface: NiFi offers a drag-and-drop web interface that simplifies the creation and management of data flows, allowing users to configure processors and monitor data streams visually. - Real-Time Processing: Supports both streaming and batch data processing, enabling the handling of diverse data sources and formats in real-time. - Extensive Processor Library: Provides over 300 built-in processors for tasks such as data ingestion, transformation, routing, and delivery, facilitating integration with various systems and protocols. - Data Provenance Tracking: Maintains detailed lineage information for every piece of data, allowing users to track its origin, transformations, and routing decisions, which is essential for auditing and compliance. - Scalability and Clustering: Supports clustering for high availability and scalability, enabling distributed data processing across multiple nodes. - Security Features: Incorporates robust security measures, including SSL/TLS encryption, authentication, and fine-grained access control, ensuring secure data transmission and access. Primary Value and Problem Solving: Apache NiFi addresses the complexities of data flow automation by providing a user-friendly platform that reduces the need for custom coding, thereby accelerating development cycles. Its real-time processing capabilities and extensive processor library allow organizations to integrate disparate systems efficiently, ensuring seamless data movement and transformation. The comprehensive data provenance tracking enhances transparency and compliance, while its scalability and security features make it suitable for enterprise-level deployments. By simplifying data flow management, NiFi enables organizations to focus on deriving insights and value from their data rather than dealing with the intricacies of data integration.
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Reviewers say compared to Google Cloud Managed Service for Apache Spark, Apache NiFi is:
Better at meeting requirements
Categories in common with Google Cloud Managed Service for Apache Spark:
ETL Tools

Azure HDInsight
By Microsoft
3.9/5
(17)
Product Description
HDInsight is a fully-managed cloud Hadoop offering that provides optimized open source analytic clusters for Spark, Hive, MapReduce, HBase, Storm, Kafka, and R Server backed by a 99.9% SLA.
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Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution

Snowflake
By Snowflake, Inc.
4.6/5
(729)
Product Description
Snowflake’s platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything “just works”. To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python, or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.
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Reviewers say compared to Google Cloud Managed Service for Apache Spark, Snowflake is:
Better at meeting requirements
More usable
Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution

Hadoop HDFS
By The Apache Software Foundation
4.4/5
(141)
Product Description
The Hadoop Distributed File System (HDFS) is a scalable and fault-tolerant file system designed to manage large datasets across clusters of commodity hardware. As a core component of the Apache Hadoop ecosystem, HDFS enables efficient storage and retrieval of vast amounts of data, making it ideal for big data applications. Key Features and Functionality: - Fault Tolerance: HDFS replicates data blocks across multiple nodes, ensuring data availability and resilience against hardware failures. - High Throughput: Optimized for streaming data access, HDFS provides high aggregate data bandwidth, facilitating rapid data processing. - Scalability: Capable of scaling horizontally by adding more nodes, HDFS can accommodate petabytes of data, supporting the growth of data-intensive applications. - Data Locality: By processing data on the nodes where it is stored, HDFS minimizes network congestion and enhances processing speed. - Portability: Designed to be compatible across various hardware and operating systems, HDFS offers flexibility in deployment environments. Primary Value and Problem Solved: HDFS addresses the challenges of storing and processing massive datasets by providing a reliable, scalable, and cost-effective solution. Its architecture ensures data integrity and availability, even in the face of hardware failures, while its design allows for efficient data processing by leveraging data locality. This makes HDFS particularly valuable for organizations dealing with big data, enabling them to derive insights and value from their data assets effectively.
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Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution

Qubole
By Qubole
4/5
(259)
Product Description
Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon, Microsoft and Google Clouds
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Categories in common with Google Cloud Managed Service for Apache Spark:
Big Data Processing and Distribution