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Spark SQL

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Ratings

G2
4.5/5
(45)

Spark SQL description

Spark SQL is a tool that makes it easier to analyze large amounts of organized data within your company. It's like using a spreadsheet program but designed to handle much bigger datasets. You can work with data using familiar tools like SQL or through simple commands, making it easier for your teams to understand and analyze information. Spark SQL is versatile and can be used with different programming languages, giving your data professionals flexible options.

Who is Spark SQL best for

Spark SQL simplifies large dataset analysis with familiar tools like SQL. Users praise its efficiency and scalability for large datasets and seamless Spark integration. However, some find resource management and debugging complex queries challenging. Ideal for data professionals in small to large businesses.

  • Best for small, medium, and large businesses needing efficient data analysis.

  • Ideally suited for Finance, Banking, Insurance, and Software/IT/Telecom industries.

Spark SQL features

Type in the name of the feature or in your own words tell us what you need
Supported

Spark SQL supports standard SQL syntax, including ANSI compliance mode.
Supported

Spark SQL offers native SQL support for querying data.
Supported

Spark SQL supports data aggregation using aggregate functions and its DataFrame API.
Supported

Spark SQL offers robust data transformation capabilities using SQL queries, DataFrames, and Datasets.
Supported

Spark SQL can generate reports, especially for large datasets, but interactive dashboards may be slow.
Supported

Spark SQL offers query optimization with Catalyst optimizer and Adaptive Query Execution.
Supported

Spark SQL allows connecting to various sources, joining data and transforming it with custom SQL and Python code.
Qualities

We evaluate the sentiment that users express about non-functional aspects of the software

Ease of Use

Strongly positive
+1

Reliability and Performance

Rather positive
+0.33

Spark SQL reviews

We've summarised 45 Spark SQL reviews (Spark SQL G2 reviews) and summarised the main points below.

Pros of Spark SQL
  • Efficient and scalable for large datasets.
  • Familiar SQL syntax for easy querying.
  • Seamless integration with the Spark ecosystem.
  • Robust query optimization through Catalyst.
  • Unified data processing capabilities.
Cons of Spark SQL
  • Occasional challenges in resource management.
  • Absence of some conventional SQL functions.
  • Debugging complex queries can be challenging.
  • Performance tuning requires in-depth knowledge.
  • Limited compatibility with all SQL dialects.

Spark SQL alternatives

  • Logo of Hive
    Hive
    Better for batch processing and ETL processes needing Hadoop integration. Suitable for large companies, especially in e-commerce, retail, consumer goods, education, software, IT, telecommunications, marketing, agriculture, and automotive industries. Handles petabytes of data.
    Read more
  • Logo of Microsoft SQL Server
    Microsoft SQL Server
    Better for relational database management. A strong option for businesses needing robust security and integration with Microsoft products.
    Read more
  • Logo of Google Cloud BigQuery
    Google Cloud BigQuery
    Serverless architecture simplifies infrastructure management. Better for real-time analytics with streaming capabilities. More integrations with Google products. Wider industry applicability. More user reviews suggest greater adoption. However, users report higher and less predictable costs.
    Read more
  • Logo of Apache Arrow
    Apache Arrow
    Better for cross-platform and multi-language support. Apache Arrow is a Spark SQL alternative focused on speed and efficiency for large datasets. However, it is known to have a steeper learning curve and less mature enterprise analytics capabilities.
    Read more
  • Logo of mSQL
    mSQL
    Better for users with limited resources. A Spark SQL alternative known for speed and efficiency. More suitable for small to medium businesses.
    Read more
  • Logo of Studio 3T
    Studio 3T
    Better for users working with MongoDB. More visual interface focused. Slower with large datasets. High cost and licensing terms.
    Read more

Spark SQL FAQ

  • What is Spark SQL and what does Spark SQL do?

    Spark SQL is a module in Apache Spark that allows you to query structured data using SQL. It provides a familiar SQL interface for data manipulation and analysis, enabling efficient processing of large datasets within the Spark ecosystem. It supports various data sources and integrates seamlessly with other Spark components.

  • How does Spark SQL integrate with other tools?

    Spark SQL integrates seamlessly with other Apache Spark components, enabling unified data processing. It supports various data sources and programming languages like Python, Java, Scala, and R. This allows diverse data professionals to leverage its capabilities for efficient data analysis.

  • What the main competitors of Spark SQL?

    Top Spark SQL competitors include Hive, a data warehouse solution for vast datasets using SQL-like queries, and Google BigQuery, a fully managed, serverless data warehouse service for large-scale data analysis. Microsoft SQL Server and Apache Arrow are also alternatives.

  • Is Spark SQL legit?

    Yes, Spark SQL is a legitimate and widely used tool for large-scale data analysis. It offers efficient processing, familiar SQL syntax, and seamless integration with the Spark ecosystem, making it a safe and reliable choice for data professionals.

  • How much does Spark SQL cost?

    Spark SQL itself doesn't have a separate price. It's a component of Apache Spark, which is open-source and free to use. Costs will depend on the cloud platform or infrastructure where you run Spark. Therefore, consider if Spark SQL is worth the cost based on your specific infrastructure setup.

  • Is Spark SQL customer service good?

    There is no information available about Spark SQL's customer service. However, users appreciate its efficiency with large datasets, familiar SQL syntax, and seamless integration with the Spark ecosystem. Some find resource management, debugging, and performance tuning challenging.

Reviewed by

MK
Michal Kaczor
CEO at Gralio

Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.

TT
Tymon Terlikiewicz
CTO at Gralio

Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.