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SQL Server 2017 VS Easy ML for Java

Compare SQL Server 2017 VS Easy ML for Java and see what are their differences

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Contents:
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  3. » Reviews
  4. » Alternatives

SQL Server 2017 logo SQL Server 2017

Jul 1, 2017 - Learn about tools and services for mobile and paginated Reporting Services reports and Power BI reports on premises.

Easy ML for Java logo Easy ML for Java

The easiest way to start with Machine Learning in Java
  • SQL Server 2017 Landing page
    Landing page //
    2021-09-20
Not present

SQL Server 2017 features and specs

  • Cross-Platform Support
    SQL Server 2017 offers cross-platform support, enabling it to run on Windows, Linux, and Docker containers, providing flexibility and integration into various environments.
  • Graph Database Capabilities
    Introduces graph database capabilities, allowing the modeling of complex data relationships easily and efficiently, expanding its use cases.
  • Advanced Analytics
    Integrates with Microsoft R and Python services, facilitating advanced analytics and machine learning directly within the database, which helps organizations to perform sophisticated data analysis.
  • Adaptive Query Processing
    Includes adaptive query processing features to optimize query performance automatically, improving application speed and efficiency.
  • Enhanced Security
    SQL Server 2017 continues to enhance security with features like Always Encrypted, Dynamic Data Masking, and Row-Level Security to protect sensitive data.

Possible disadvantages of SQL Server 2017

  • Cost
    Licensing and support costs for SQL Server can be relatively high, particularly for enterprise editions, which may not be cost-effective for smaller organizations.
  • Complexity
    SQL Server 2017 includes a vast array of features and configurations that can introduce complexity, requiring substantial expertise to manage and optimize.
  • Resource Intensive
    Requires significant system resources for optimal performance, which may necessitate additional investment in hardware to operate efficiently at scale.
  • Limited NoSQL Functionality
    While SQL Server 2017 introduces some NoSQL features through its support for JSON and graph databases, it still lags behind dedicated NoSQL databases in terms of flexibility and scalability for unstructured data.
  • Version-Specific Features
    Some advanced features are only available in the latest versions or specific editions, which may necessitate upgrades or specific licensing to access the full capabilities, leading to additional expenses.

Easy ML for Java features and specs

No features have been listed yet.

Analysis of Easy ML for Java

Overall verdict

  • Easy ML for Java appears to be a lightweight, approachable library aimed at bringing machine learning capabilities to Java developers without requiring deep ML expertise or switching to Python-centric ecosystems. It seems suitable for developers who want to integrate basic ML functionality into existing Java applications with minimal overhead, though it likely lacks the depth, community support, and cutting-edge features of major frameworks like TensorFlow, PyTorch, or scikit-learn.

Why this product is good

  • Native Java implementation avoids the need for language interop or JNI bridges to Python-based ML libraries
  • Simpler API design makes it more accessible for Java developers without extensive ML background
  • Documentation via GitBook suggests an organized, readable learning path for newcomers
  • Lightweight footprint can be beneficial for integrating into existing Java-based systems without heavy dependencies
  • Good fit for educational purposes or prototyping simple ML concepts within a Java codebase

Recommended for

  • Java developers who want to experiment with ML without learning Python
  • Small to medium projects requiring basic classification, regression, or clustering functionality
  • Students or educators teaching foundational ML concepts using Java
  • Teams with existing Java infrastructure who need lightweight ML integration without major architectural changes
  • Prototyping and proof-of-concept work rather than production-grade, large-scale ML systems

SQL Server 2017 videos

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SQL Server 2017 – Everything you need to know

More videos:

  • Review - SQL Server 2017 Features

Easy ML for Java videos

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Category Popularity

0-100% (relative to SQL Server 2017 and Easy ML for Java)

SQL Server 2017

Easy ML for Java

Data Dashboard
100 100%
Data Dashboard
0% 0
Artifical Intelligence
0 0%
Artifical Intelligence
100% 100
Data Visualization
100 100%
Data Visualization
0% 0
Machine Learning
0 0%
Machine Learning
100% 100

User comments

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What are some alternatives?

When comparing SQL Server 2017 and Easy ML for Java, you can also consider the following products

JasperReports - JasperReports Server is a stand-alone and embeddable reporting server.

Telerik Reporting - Deliver Reports to Any Application. Add reports to any business application. View reports on mobile devices and in web, desktop and cloud apps. Export reports to any format.

Pentaho - Pentaho is a Business Intelligence software company that offers Pentaho Business Analytics, a suite...

Crystal Reports - Save up to 25% when you buy or upgrade. Discover SAP Crystal Reports to take control of complex data and monitor business performance to achieve results.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

i-net Clear Reports - Java reporting engine and Crystal Reports alternative for embedding interactive reports, dashboards, and document exports into enterprise applications. Supports existing .rpt files, REST APIs, modern web reporting, and on-premises or cloud deployment

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