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SageMaker vs QlikView

Last Updated: November 18th, 2024

Our analysts compared SageMaker vs QlikView based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

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Product Basics

Amazon SageMaker is a comprehensive machine learning platform by Amazon Web Services (AWS) designed to simplify the entire machine learning lifecycle. It empowers businesses to build, train, deploy, and manage machine learning models efficiently. Key features include robust data preprocessing tools, a wide selection of machine learning algorithms, and automated hyperparameter tuning. SageMaker's scalability ensures it's suitable for both small experiments and large-scale production deployments. It offers cost-efficiency with a pay-as-you-go pricing model and facilitates model management and monitoring. The platform integrates seamlessly with the AWS ecosystem, providing security and compliance features. SageMaker's AutoML capabilities make machine learning accessible to users of varying expertise. Overall, it streamlines the machine learning process, enabling organizations to harness the power of AI for improved decision-making and innovation.

QlikView is a data discovery and customer insight platform from Qlik, a leader in the insight and intelligence space. However, it is not available for purchase any longer. Qlik Sense, Qlik’s next-generation offering, is available for new customers. It offers self-service data that can help drive decisions and generate significant ROI for technical skill level users.

It’s built from the ground up to be affordable, scalable and adaptable. It can ingest data from diverse sources like big data streams, file-based data, and on-premise or cloud data. It is well-known for its data associations and relationship functionality, keeping data in context automatically. It delivers results quickly via its patented in-memory data processing module, processing data down to as little as 10% of its original size.

Pros

Intuitive interface
Fast data visualization
Easy data exploration
User-friendly for non-technical users
Strong community and support

Cons

Limited data modeling capabilities
Licensing costs can be high
Customization can be challenging
Version control can be a concern
Performance can slow with large datasets

$0.51 Hourly

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$2,500/User, Annual

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Product Insights

Accelerated Machine Learning: Amazon SageMaker offers a robust environment for building, training, and deploying machine learning models quickly and efficiently. It streamlines the ML workflow, reducing time-to-market.
Scalability: With SageMaker, you can effortlessly scale your machine learning projects. It can handle both small-scale experiments and large-scale production deployments, ensuring flexibility as your needs evolve.
Cost Efficiency: SageMaker's pay-as-you-go pricing model and built-in cost optimization tools help you manage expenses effectively. It optimizes resource allocation, preventing unnecessary spending.
Managed Infrastructure: The service abstracts the complexities of infrastructure management. This allows data scientists and developers to focus on model development rather than worrying about provisioning and maintaining infrastructure.
AutoML Capabilities: SageMaker provides AutoML features that automate aspects of model selection, hyperparameter tuning, and deployment, making it accessible to users with varying levels of expertise.
Robust Data Labeling: SageMaker includes data labeling tools and integration with Amazon Mechanical Turk, making it easier to annotate and prepare data for training, a critical step in machine learning workflows.
Secure and Compliant: Amazon SageMaker adheres to industry-leading security and compliance standards. It encrypts data, monitors access, and offers tools for compliance with regulations like GDPR and HIPAA.
Customizable Workflows: SageMaker's flexibility allows you to customize your machine learning workflows to suit your specific requirements. You can integrate your own algorithms, libraries, and tools seamlessly.
Model Management: It simplifies model management, versioning, and deployment, making it easy to keep track of different iterations of your models and roll out updates effortlessly.
Real-time Inference: SageMaker supports real-time model inference, enabling you to integrate machine learning predictions into your applications and services in real-time, enhancing user experiences.

Optimize Connections: Spot connections in data even when working with large data sets. Take a granular look at data through drill-down and view high-level trends in customer/employee behavior.
Increase Data Security and Control: Ensure total control over sensitive data. Prevent unauthorized access to crucial data sets through user-control, filtered views and permissions. AES-256 GCM provides data integrity and keeps information secured. Grant access to view data using user-specific access.
Enhance Functionality: Build the product in alignment with user needs and specifications. Extend functionality with QlikView Workbench to customize the app with custom scripting.
Increase Insight: Gain unparalleled insights into business operations, as well as the market at large. Track KPIs like sales, budgets, employee performance, and hundreds of other pre-built or custom data points.
Flexible Deployment: Choose from a cloud analytics solution, an on-premise installation, or a combined solution to get desired scalability and flexibility.
Intuitive Interface: Obtain data results via the self-service and code-free visual tool. Its accessible interface provides updated information instantly.
Empower Company Culture: Foster a data-driven culture by empowering employees to share data and insights directly or outside the platform.
Increase Competition: Monitor and track employee performance using KPI dashboards across organizations. Analyze changes in empirical data and make decisions to capitalize the profits.
Automated Recommendation: Receive and recommend automated context-aware suggestions using machine learning (ML). Prioritize recommendations on visualization type for easy access.
Accelerate Business Growth: Scale the businesses by deploying self-service and predictable BI. Test business performance using free tools available on the webpage.
Enhance Accessibility: Access all features from anywhere and on any device using an HTML5 client. Provides mobile push notifications to ensure unrestricted and consistent access.
Worldwide Support: Support fifteen languages and native currency conversions while detecting which language to use according to the browser or operating system.
Optimize Deployment and Configuration: Deploy quickly and allow for even quicker configuration because data is not required to be stored in any silos or cubes.

Data Preprocessing Tools: SageMaker offers a range of data preprocessing capabilities, including data cleaning, transformation, and feature engineering, enabling users to prepare data efficiently for machine learning.
Wide Model Selection: Users have access to a diverse library of machine learning algorithms, from linear regression to deep learning frameworks like TensorFlow, making it suitable for a variety of use cases.
Hyperparameter Tuning: SageMaker automates hyperparameter optimization, helping users find the best configurations for their models, which can significantly improve model performance.
Model Training at Scale: It supports distributed training across multiple instances, reducing training times and enabling the handling of large datasets with ease.
Model Deployment: Users can deploy models as RESTful APIs, facilitating real-time inference in applications and services, and manage multiple model versions seamlessly.
AutoML Capabilities: SageMaker Autopilot streamlines model creation for users without deep machine learning expertise, automating tasks like feature engineering and model selection.
Monitoring and Debugging: It offers tools for model monitoring and debugging, helping users detect and address issues in deployed models, ensuring reliability in production.
Explainability and Bias Detection: SageMaker provides features for model explainability and bias detection, essential for understanding model predictions and addressing ethical considerations.
Integration with AWS Ecosystem: Seamlessly integrates with other AWS services, such as S3, Lambda, and Step Functions, facilitating end-to-end machine learning workflows within the AWS environment.
Security and Compliance: Offers comprehensive security features, including data encryption, access control, and compliance with industry standards, making it suitable for sensitive industries like healthcare and finance.
Cost Optimization: SageMaker includes cost optimization tools like automatic model scaling, enabling users to manage and optimize machine learning expenses efficiently.

Direct Data Source Connection: Connect to almost any data source, including cloud, big data, file-based and on-premise data. Pull information from many services (Salesforce, Hive, Teradata) and combine intel seamlessly into unique and intuitive dashboards.
Intelligent Visualization: Offer interactive displays and represent data in multiple ways for better data analysis. Flexible visualizations allow users to change and adjust graphics according to screen size.
Enterprise Collaboration: Facilitate collaboration for users to share the same dashboard, look at the same view or track one another as they navigate the application.
Strong Associations: Leverage the strength of the platform’s built-in association engine to conduct direct and indirect searches across data or within a single field. Identify data that is related and not associated.
Self-Service App Building: Build apps and files via the drag-and-drop function. Create individual lists with their visualization while managing and sharing across organizations.
Associative Indexing: Combine, transform and ingest data from multiple sources. Gathers data and indexes it to find logical associations. Explore and search big data repositories freely while keeping data intact.
Interactive Dashboards: Provide visualization capabilities and improve interaction using tooltip, lasso selection, filtering and drill-down functions. Encourage viewers to explore data by creating smart dashboards and distributing them using interactive elements.
In-Memory Application: House the software in memory, so conversions, queries and searches happen quicker and more efficiently. Eliminate problems that traditionally plague slow, on-disk applications. Locate all data in RAM.
Web Connectors: Extract data from multiple social networking sites and web-based sources using web APIs. Built-in connectors easily connect to any URL and fetch data.
Robust Data Controls: Enable meaningful data manipulation within the application by leveraging unique dashboards, reports and filter views.
Data Alerts: Spot anomalies and outliers by requesting context-aware alerts. Monitor and manage data without limitations.

Product Ranking

#28

among all
Big Data Analytics Tools

#32

among all
Big Data Analytics Tools

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Analyst Rating Summary

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User Sentiment Summary

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82%

of users recommend this product

QlikView has a 'great' User Satisfaction Rating of 82% when considering 1859 user reviews from 4 recognized software review sites.

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4.1 (239)

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4.3 (163)

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3.9 (732)

Synopsis of User Ratings and Reviews

Robust Feature Set: Users appreciate SageMaker's comprehensive feature set, which covers data preprocessing, model training, deployment, and monitoring, all in one platform.

Scalability: Many users highlight SageMaker's ability to scale seamlessly, accommodating both small-scale experiments and large-scale production workloads.

Cost-Efficiency: The pay-as-you-go pricing model and cost optimization tools receive positive reviews for helping users manage machine learning expenses effectively.

Integration with AWS: Users value SageMaker's integration with the broader AWS ecosystem, simplifying workflows and enhancing compatibility with other AWS services.

AutoML Capabilities: SageMaker's AutoML features, such as Autopilot, receive praise for automating complex machine learning tasks, making it accessible to a broader range of users.

Model Management: Users find the platform's model versioning and management tools useful for keeping track of models and deploying updates efficiently.

Security and Compliance: The robust security features, including data encryption and compliance with industry standards, are seen as a critical advantage for users with stringent data security requirements.

Real-time Inference: Users appreciate the capability to deploy models as RESTful APIs, enabling real-time predictions in applications and services, enhancing user experiences.

Community Support: Some users highlight the active SageMaker community, which provides valuable resources, tutorials, and support for users at all skill levels.

Extensive Documentation: Users find the platform's extensive documentation and tutorials helpful for onboarding and troubleshooting, contributing to a smoother user experience.

Data Visualization: Approximately 80% of users who review its data visualization capabilities are satisfied with its intuitive drag-and-drop feature, rich libraries and its range of aesthetically appealing data representation options.

Data Preparation: Of users who mention data processing, 83% appreciate the platform’s seemingly limitless data transformation capabilities that help them deep-dive into all possible data relationships to glean actionable insights.

Functionality: Among users who share their views on this platform, around 68% say that they are satisfied with the power of its associative query engine that enables faster on-the-fly calculations and analytics aggregation at the speed of thought.

Sharing and Collaboration: About 83% of users who comment on sharing capabilities appreciate its multi-tier permissions capabilities and easy sharing of reports with clients via external sharing options.

Setup: Around 66% of users who mention ease of setup say that QlikView has a fast implementation cycle.

Complex Learning Curve: Users often find SageMaker challenging for beginners due to its extensive feature set, requiring significant time and effort to master.

Cost Management: Some users report difficulty in managing costs effectively, especially during large-scale model training, which can lead to unexpected expenses.

Limited Customization: Advanced users may encounter limitations when attempting to customize certain aspects of the SageMaker environment and algorithms.

Data Privacy Concerns: The cloud-based data storage raises concerns for users with strict data locality requirements or those subject to stringent data privacy regulations.

Dependency on AWS: To maximize SageMaker's capabilities, users often need to rely on the broader AWS ecosystem, potentially resulting in vendor lock-in.

Offline Processing Challenges: While designed for real-time inference, SageMaker may not be optimized for batch processing or offline use cases, limiting its versatility.

Resource Constraints: The platform's performance can be constrained by the chosen instance types, affecting the speed of model training and inference.

Complexity for Small Projects: Some users find SageMaker's robust features excessive for small-scale projects, leading to a steeper learning curve without commensurate benefits.

AutoML Limitations: While AutoML is a strength, it may not cover all use cases, and users may need to resort to manual interventions for specific scenarios.

Documentation Gaps: A few users have reported occasional gaps or ambiguities in the platform's documentation, which can be frustrating for troubleshooting and implementation.

Cost: Pricing plans are inflexible and can be cost-prohibitive for small organizations and startups, though large organizations may find that it offers high value, as stated by 93% of users who mention its cost.

Performance: Approximately 42% of users say that performance-wise, this platform is resource-hungry and liable to slow down when crunching large amounts of data on local machines.

User Interface and Graphics: Of users who mention user interface, around 44% say that it needs improvement in deep-dive capabilities, as well as its quality of graphics.

Reporting: Of users who mention reporting, approximately 46% say that it lacks ad-hoc reporting and built-in reporting capabilities, requiring paid plugins to enhance the graphics quality of reports.

User reviews of Amazon SageMaker reveal a platform appreciated for its robust feature set, scalability, and cost-efficiency. Many users find its comprehensive tools for data preprocessing, model training, deployment, and monitoring to be a significant strength. Scalability is another key advantage, with SageMaker accommodating both small-scale experiments and large-scale production workloads effectively. However, some users point out that SageMaker has a steep learning curve, particularly for beginners, and cost management can be challenging, especially during extensive model training. The platform's dependency on the broader AWS ecosystem can lead to vendor lock-in, which may not be ideal for organizations seeking flexibility. SageMaker's AutoML capabilities, such as Autopilot, are praised for automating complex tasks, but some advanced users note limitations in customization. Additionally, while designed for real-time inference, it may not be optimized for batch processing or offline use cases. In comparison to similar products, SageMaker stands out for its deep integration with AWS services, making it a preferred choice for those already within the AWS ecosystem. However, the learning curve and potential cost challenges are factors that users weigh against its benefits. The platform's active community support and extensive documentation receive positive mentions, contributing to a smoother user experience. Overall, Amazon SageMaker is a powerful tool for machine learning but requires careful consideration of its complexities and potential cost implications.

QlikView is one of the foremost BI solutions in the market today, mainly due to the power of its associative query engine to link data from multiple sources that drives its visually impressive dashboards. With its strong data visualization capabilities, users can perform search and filter through data on-the-fly and conduct deep-dives to glean insights that matter to them. With a fast setup, users can have their first data model up and running in very little time. The software resides in-memory and houses data in RAM for quicker retrieval. With multi-tier access permissions for in-organization users, it enables users to view executive summaries at a glance, while allowing them to drill-down into data to find out more. Sadly, Qlik is now scaling back on improvements and updates for QlikView and focusing on promoting QlikSense instead, a possible reason why its filter and search functions, ad-hoc reporting and graphics are lagging in terms of quality, as mentioned in many user reviews. Also, this platform can prove to be resource-heavy for databases housed on local machines, especially when performing batch update jobs. In addition to inflexible pricing plans and the cost of licensing, quite a few necessary add-ons are paid. In summary, QlikView is one of the leading in-memory BI tools available in the market today and rates excellently with users in terms of data aggregation and visualization capabilities; however, buyers should factor in its pricing plans and other limitations when searching for the perfect BI solution for their enterprise.

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