Top 10 Spark SQL Alternatives & Competitors

(45)4.5 out of 5

Explore the best alternatives to Spark SQL for users who need new software features or want to try different solutions. Other important factors to consider when researching alternatives to Spark SQL include integration and features. The best overall Spark SQL alternative is Oracle Database. Other similar apps like Spark SQL are PostgreSQL, ClickHouse, Microsoft SQL Server, and SQLite. Spark SQL alternatives can be found in Relational Databases but may also be in Real-time Analytic Database Software or Data Warehouse Solutions.

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Best Paid & Free Alternatives to Spark SQL

Oracle Database
PostgreSQL
ClickHouse
Microsoft SQL Server
SQLite
Teradata Vantage
Amazon Aurora
Amazon Relational Database Service (RDS)

Top 10 Alternatives to Spark SQL Recently Reviewed By G2 Community

Browse options below. Based on reviewer data, you can see how Spark SQL stacks up to the competition, check reviews from current & previous users in industries like Information Technology and Services, Retail, and Hospital & Health Care, and find the best product for your business.

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By Oracle

Product Description

Oracle Database is a comprehensive, multi-model database management system developed by Oracle Corporation. It is designed to handle various data types and workloads, including online transaction processing (OLTP), data warehousing, and mixed database operations. With its robust architecture, Oracle Database supports deployment across on-premises environments, cloud platforms, and hybrid configurations, offering flexibility and scalability to meet diverse business needs. Key Features and Functionality: - Multi-Model Support: Oracle Database accommodates various data models, including relational, document, graph, and key-value, enabling developers to work with diverse data types within a single platform. - Advanced Analytics: The database integrates advanced analytics capabilities, such as in-database machine learning and AI Vector Search, allowing users to perform complex analyses directly within the database environment. - High Availability and Scalability: Designed for mission-critical applications, Oracle Database offers features like data replication, backup, server clustering, and automatic storage management to ensure high availability and seamless scalability. - Security: With comprehensive security measures, including encryption, SQL Firewall, and data masking, Oracle Database safeguards sensitive information and maintains data integrity. - Multicloud Deployment: Oracle Database supports deployment across various cloud platforms, including Oracle Cloud Infrastructure, AWS, Microsoft Azure, and Google Cloud, providing flexibility and compliance with data residency requirements. Primary Value and Solutions Provided: Oracle Database addresses the complex data management needs of modern enterprises by offering a unified platform that supports multiple data models and workloads. Its integration of AI and machine learning capabilities enables organizations to derive actionable insights directly from their data, enhancing decision-making processes. The database's high availability and scalability ensure that businesses can maintain continuous operations and adapt to growing data demands. Additionally, its robust security features protect against data breaches and ensure compliance with regulatory standards. By supporting multicloud deployments, Oracle Database provides the flexibility to operate in various cloud environments, facilitating seamless integration and innovation across different platforms.

Reviewers say compared to Spark SQL, Oracle Database is:

More expensive

Better at meeting requirements

Categories in common with Spark SQL:

By PostgreSQL

Product Description

PostgreSQL is a powerful, open-source object-relational database system renowned for its reliability, extensibility, and adherence to SQL standards. Originating from the POSTGRES project at the University of California at Berkeley in 1986, it has evolved over nearly four decades into a robust platform capable of handling complex data workloads across various operating systems. PostgreSQL's architecture emphasizes data integrity and scalability, making it a preferred choice for developers and organizations worldwide. Key Features and Functionality: - Comprehensive Data Types: Supports a wide range of data types, including primitives (Integer, Numeric, String, Boolean), structured (Date/Time, Array, Range), document (JSON/JSONB, XML), and geometric types. - Advanced Data Integrity: Ensures data accuracy through features like UNIQUE constraints, primary and foreign keys, exclusion constraints, and various locking mechanisms. - High Performance and Concurrency: Utilizes advanced indexing methods (B-tree, GiST, GIN, BRIN), a sophisticated query planner, multi-version concurrency control (MVCC), parallel query execution, and table partitioning to optimize performance. - Reliability and Disaster Recovery: Offers write-ahead logging (WAL), various replication methods (asynchronous, synchronous, logical), point-in-time recovery (PITR), and active standbys to ensure data durability and availability. - Robust Security Measures: Provides multiple authentication methods (GSSAPI, SSPI, LDAP, SCRAM-SHA-256, Certificate, OAuth 2.0), a comprehensive access-control system, and supports multi-factor authentication. - Extensibility: Allows the creation of custom data types, functions, and operators. Supports procedural languages like PL/pgSQL, Perl, Python, and Tcl, with additional languages available through extensions. Primary Value and User Solutions: PostgreSQL addresses the needs of developers and organizations by offering a highly extensible and standards-compliant database system that ensures data integrity, scalability, and robust performance. Its open-source nature allows for continuous innovation and adaptability, enabling users to tailor the database to their specific requirements. Whether managing small applications or large-scale enterprise systems, PostgreSQL provides a reliable foundation for storing and processing data efficiently.

Reviewers say compared to Spark SQL, PostgreSQL is:

Better at meeting requirements

Categories in common with Spark SQL:

By ClickHouse

Product Description

Reviewers say compared to Spark SQL, ClickHouse is:

Easier to set up

Better at support

Better at meeting requirements

Categories in common with Spark SQL:

Relational Databases

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SQLite

By SQLite

4.3/5

(260)

Product Description

SQLite is a C-language library that provides a self-contained, serverless, and zero-configuration SQL database engine. Renowned for its small footprint and high reliability, SQLite is the most widely deployed database engine globally, embedded in billions of devices and applications. Its cross-platform database file format ensures seamless data transfer across different systems, making it a preferred choice for developers seeking an efficient and dependable database solution. Key Features and Functionality: - Self-Contained and Serverless: Operates without a separate server process, reading and writing directly to disk files. - Zero-Configuration: Requires no setup or administration, facilitating easy integration into applications. - Full-Featured SQL Implementation: Supports advanced capabilities like partial indexes, indexes on expressions, JSON, common table expressions, and window functions. - Single-File Database: Stores the entire database, including multiple tables, indices, triggers, and views, in a single cross-platform disk file. - Scalability: Handles databases up to 281 terabytes and supports row sizes up to 1 gigabyte. - Compact Size: With all features enabled, the library size can be less than 900KiB, depending on the target platform and compiler optimization settings. - High Performance: In some cases, SQLite is faster than direct filesystem I/O. - Cross-Platform Compatibility: Supports various operating systems, including Android, iOS, Linux, Mac, and Windows. - Public Domain Source Code: The source code is in the public domain, allowing free use for any purpose. Primary Value and User Solutions: SQLite offers a lightweight, reliable, and easy-to-use database solution that eliminates the complexities associated with traditional database management systems. Its serverless architecture and zero-configuration setup make it ideal for applications where simplicity and efficiency are paramount. By storing data in a single file, SQLite simplifies data management and portability, addressing the needs of developers and organizations seeking a robust database engine without the overhead of a full-fledged RDBMS.

Reviewers say compared to Spark SQL, SQLite is:

Easier to admin

Easier to set up

Categories in common with Spark SQL:

By Teradata

Product Description

Reviewers say compared to Spark SQL, Teradata Vantage is:

More expensive

Categories in common with Spark SQL:

Relational Databases

Amazon Aurora

By Amazon Web Services (AWS)

4.5/5

(454)

Product Description

Amazon Aurora is a fully managed relational database service that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Compatible with MySQL and PostgreSQL, Aurora delivers up to five times the throughput of standard MySQL databases and up to three times that of standard PostgreSQL databases. It is designed for high availability, offering up to 99.99% availability within a single region and up to 99.999% across multiple regions. Aurora's architecture includes a distributed, fault-tolerant storage system that automatically scales up to 128 tebibytes, ensuring continuous data access and durability. Additionally, Aurora provides serverless configurations, allowing automatic scaling based on application needs, and integrates seamlessly with other AWS services for machine learning and analytics. Key Features and Functionality: - High Performance: Delivers up to five times the throughput of MySQL and three times that of PostgreSQL, enabling efficient handling of demanding workloads. - High Availability: Designed for up to 99.99% availability within a single region and up to 99.999% across multiple regions, ensuring continuous data access. - Scalability: Automatically scales storage up to 128 tebibytes and supports up to 15 read replicas for read-intensive applications. - Serverless Configuration: Offers Aurora Serverless, which automatically adjusts capacity based on application demand, eliminating the need for manual provisioning. - Machine Learning Integration: Integrates with Amazon SageMaker and Amazon Comprehend, allowing for in-database machine learning capabilities without data movement. - Security: Provides multiple layers of security, including network isolation, encryption at rest and in transit, and compliance with various industry standards. Primary Value and Solutions Provided: Amazon Aurora addresses the need for a high-performance, highly available, and scalable relational database service that is cost-effective and easy to manage. By offering compatibility with MySQL and PostgreSQL, it allows organizations to migrate existing applications without significant code changes. Aurora's automatic scaling and serverless options cater to applications with variable workloads, reducing operational overhead and costs. Its integration with AWS machine learning services enables real-time analytics and predictive capabilities directly within the database, enhancing application functionality. Overall, Aurora simplifies database management while delivering enterprise-grade performance and reliability.

Reviewers say compared to Spark SQL, Amazon Aurora is:

Easier to admin

More expensive

Better at support

Categories in common with Spark SQL:

Relational Databases