Software Alternatives, Accelerators & Startups

Concurrent VS OpenText Analytics

Compare Concurrent VS OpenText Analytics and see what are their differences

Concurrent logo Concurrent

Concurrent is a technology solution providing real-time computing solutions for businesses and individuals.

OpenText Analytics logo OpenText Analytics

OpenText Analytics provides software and services to develop and deploy custom business intelligence and information applications.
  • Concurrent Landing page
    Landing page //
    2023-07-13
  • OpenText Analytics Landing page
    Landing page //
    2023-07-04

Concurrent features and specs

  • Scalable Data Processing
    Concurrent provides tools that enable scalable data processing on distributed systems, which can handle large datasets and complex pipelines efficiently.
  • Open Source Tools
    The company offers open-source tools, such as Cascading, which allows developers to build powerful data applications and workflows without being tied to proprietary solutions.
  • Integration with Hadoop
    Concurrent provides strong integration with Hadoop, allowing users to leverage the vast Hadoop ecosystem for advanced data processing capabilities.
  • Developer Productivity
    By using tools like Cascading, developers can focus more on business logic rather than the intricacies of distributed computing and low-level detail plumbing.
  • Community Support
    Being based on open-source projects, Concurrent benefits from a large community of users and contributors, providing robust support and continuous improvements.

Possible disadvantages of Concurrent

  • Steep Learning Curve
    Tools like Cascading can have a steep learning curve for developers who are not already familiar with Hadoop and the MapReduce paradigm.
  • Dependency on Hadoop
    Strong integration with Hadoop can be a downside for organizations looking to migrate away from Hadoop or use different big data processing frameworks.
  • Performance Overhead
    Abstracting away lower-level details and focusing on developer productivity can sometimes introduce performance overhead compared to writing optimized, low-level code.
  • Complex Setups
    Setting up Cascading and related tooling within an organization's infrastructure might require significant time and effort, especially for teams with less experience in the big data domain.
  • Limited Vendor-Specific Features
    As open-source tools need to remain general and widely applicable, they may lack some of the specific features and optimizations provided by proprietary, vendor-specific solutions suited for particular use cases.

OpenText Analytics features and specs

  • Comprehensive Reporting
    OpenText Analytics offers robust reporting tools that support a wide array of data visualization options, making it suitable for complex business intelligence requirements.
  • Scalability
    The platform is designed to scale from departmental to enterprise-level deployments, accommodating a growing amount of data and more complex analytics over time.
  • Data Integration
    Supports integration with various data sources, including databases, cloud services, and on-premise systems, ensuring a seamless flow of information.
  • User-Friendly Interface
    Features a drag-and-drop interface that makes it easier for users to create and manage their own dashboards and reports without needing deep technical expertise.
  • Customizability
    Offers extensive options for customization, allowing users to tailor solutions to fit specific business needs and preferences.

Possible disadvantages of OpenText Analytics

  • Cost
    The solution can be expensive for small to medium-sized businesses, particularly when considering the total cost of ownership, including licenses, implementation, and maintenance.
  • Complexity
    While powerful, the platform can be complex to set up and may require a steep learning curve, as well as skilled personnel for effective implementation and maintenance.
  • Performance Issues
    Some users have reported performance issues, particularly when dealing with extremely large datasets or complex queries.
  • Limited Community Support
    Compared to other analytics platforms, OpenText Analytics has a smaller community, which can make it difficult to find peer support or third-party resources.
  • Periodic Updates
    Updates and new features are not always released as frequently as some users might like, which can be a downside for organizations looking for the latest capabilities.

Concurrent videos

LOCADTR Concurrent Review Module Walk Through

More videos:

  • Review - Concurrent Review Instructions
  • Review - Documentation Requirements for Claim Submission and Concurrent Review

OpenText Analytics videos

OpenText Analytics Live Demo

Category Popularity

0-100% (relative to Concurrent and OpenText Analytics)
Data Dashboard
60 60%
40% 40
Big Data Analytics
62 62%
38% 38
Database Tools
59 59%
41% 41
Data Science And Machine Learning

User comments

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What are some alternatives?

When comparing Concurrent and OpenText Analytics, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Rakam - Custom analytics platform