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Dataiku vs BigQuery

Last Updated: November 18th, 2024

Our analysts compared Dataiku vs BigQuery 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

Dataiku is a powerful data analytics platform designed to empower organizations with data-driven insights and machine learning capabilities. It offers a comprehensive suite of features, including data integration, preparation, and advanced machine learning, all within a user-friendly interface. Dataiku facilitates collaboration among data professionals and business users, streamlining the data analytics process. Its AutoML capabilities simplify machine learning model development, making it accessible to users with varying levels of expertise. Real-time insights and scalability are key benefits, allowing organizations to make timely decisions and adapt to changing data requirements. Despite some learning curve challenges, Dataiku remains a favored choice for medium and large businesses seeking robust data analytics solutions.

BigQuery, a cloud-based data warehouse offered by Google, provides businesses with a scalable and cost-effective solution for analyzing massive datasets. It eliminates the need for infrastructure management, allowing users to focus on extracting valuable insights from their data using familiar SQL and built-in machine learning capabilities. BigQuery's serverless architecture enables efficient scaling, allowing you to query terabytes of data in seconds and petabytes in minutes.

BigQuery is particularly well-suited for organizations dealing with large and complex datasets that require rapid analysis. Its ability to integrate data from various sources, including Google Cloud Platform and other cloud providers, makes it a versatile tool for businesses with diverse data landscapes. Key benefits include scalability, ease of use, and cost-effectiveness. BigQuery offers a pay-as-you-go pricing model, allowing you to only pay for the resources you consume. You are billed based on the amount of data processed by your queries and the amount of data stored.

While BigQuery offers numerous advantages, it's important to consider factors such as your specific data analytics needs and budget when comparing it to similar products. User experiences with BigQuery have generally been positive, highlighting its speed, scalability, and ease of use. However, some users have noted that the pricing structure can become complex for highly demanding workloads.

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Product Insights

Advanced Data Preparation: Dataiku offers powerful data preparation tools, allowing users to clean, transform, and enrich data easily. This ensures that data is ready for analysis and modeling.
Collaborative Environment: Dataiku provides a collaborative workspace where data scientists, analysts, and business users can work together seamlessly, fostering cross-functional teamwork.
Scalable Machine Learning: With Dataiku, organizations can build, deploy, and monitor machine learning models at scale. This empowers data-driven decision-making across the enterprise.
AutoML Capabilities: Dataiku's AutoML functionality automates the model selection and hyperparameter tuning process, making it easier for users to create accurate machine learning models.
Visual Data Exploration: Users can visually explore data, gaining valuable insights through interactive charts and graphs, simplifying the process of uncovering trends and patterns.
Model Deployment: Dataiku streamlines the deployment of machine learning models into production, ensuring that insights generated from data are put into action efficiently.
Data Governance: Dataiku offers robust data governance features, including data lineage, access controls, and auditing, to maintain data quality and compliance.
Automated Reporting: Users can create automated reports and dashboards to share insights with stakeholders, saving time and ensuring data-driven decisions are accessible to all.
Flexible Integration: Dataiku seamlessly integrates with a variety of data sources and tools, making it adaptable to existing data ecosystems and technologies.
Interpretable AI: Dataiku places a strong emphasis on model explainability, helping users understand why AI models make specific predictions, enhancing trust and transparency.

Forecast and Plan Ahead: Ingest large amounts of data quickly to strengthen forecasting and boost decision-making processes.
Deliver Insights: Find discrepancies in data and act on them accordingly.
Focus on Analytics and Not Infrastructure: Handles large volumes of data without putting strain on an organization’s IT resources.
Provide a User-Friendly Environment: It’s user-friendly for both technical and non-technical users. High-level knowledge is not necessary to operate the software effectively.
Speed Up Processes: Utilizes fast SQL databases to quickly and efficiently analyze terabytes worth of data.

Data Integration: Dataiku offers seamless data integration capabilities, allowing users to connect to various data sources, import data, and prepare it for analysis.
Machine Learning Studio: Users can build, train, and deploy machine learning models in a user-friendly visual interface, streamlining the data science workflow.
Data Preparation: Dataiku provides robust data preparation tools, enabling users to clean, transform, and enrich data efficiently for analysis.
AutoML: The AutoML functionality automates model selection and hyperparameter tuning, simplifying the process of creating accurate machine learning models.
Collaboration Hub: Dataiku offers a collaborative environment where data scientists, analysts, and business users can work together on data projects.
Model Deployment: Users can easily deploy machine learning models into production, ensuring that insights generated from data are put into action.
Visual Data Exploration: Dataiku enables users to visually explore data through interactive charts and graphs, facilitating data discovery and insights.
Data Governance: The platform includes robust data governance features, such as data lineage, access controls, and auditing, ensuring data quality and compliance.
Automated Reporting: Users can create automated reports and dashboards to share insights with stakeholders, streamlining communication.
Scalability: Dataiku is designed to scale with an organization's data needs, making it suitable for both small teams and large enterprises.

Machine Learning: Comes with machine learning modules that can perform mass-segmentation and recommendations in seconds. These modules can be built and trained within minutes without ingesting data for training.
Cloud Hosted: Handles all the hardware provisioning, warehousing and hardware management from the cloud.
Real-Time Analytics: Large volumes of business data are quickly analyzed and presented to the user to ensure that insights and data discrepancies can be immediately uncovered.
Automated Backups: Data is automatically stored and backed up multiple times a day. Data histories can be easily restored to prevent loss and major changes.
Big Data Ecosystem Integrations: Integrate with other big data products such as Hadoop, Spark and Beam. Data can be directly written from the system into these products.
Data Governance: Features such as access management, filter views, encryption and more are included in the software. The product is compliant with data regulations such as the GDPR.

Product Ranking

#26

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#10

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Analyst Rating Summary

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User Sentiment Summary

91%

of users recommend this product

Dataiku has a 'excellent' User Satisfaction Rating of 91% when considering 7 user reviews from 1 recognized software review sites.

90%

of users recommend this product

BigQuery has a 'excellent' User Satisfaction Rating of 90% when considering 724 user reviews from 3 recognized software review sites.

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4.4 (292)

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Awards

Synopsis of User Ratings and Reviews

Comprehensive Feature Set: Users appreciate Dataiku's wide range of features, from data preparation to advanced machine learning, enabling end-to-end data analytics.

Intuitive Interface: Dataiku's user-friendly interface receives praise for its ease of use, making it accessible to both data professionals and business users.

Effective Collaboration: Many users find Dataiku's collaborative environment conducive to teamwork, facilitating cross-functional collaboration on data projects.

Scalability: Dataiku's scalability is highly regarded, making it suitable for small teams and large enterprises, adapting to evolving data requirements.

AutoML Capabilities: Users value Dataiku's AutoML functionality, which simplifies machine learning, making it accessible to users with varying levels of expertise.

Real-Time Insights: Dataiku's ability to provide real-time insights is a significant benefit, enabling timely decision-making based on up-to-date data.

Data Governance: Dataiku's robust data governance features are highly regarded, helping maintain data quality and ensuring compliance with regulations.

Community and Support: Users appreciate the Dataiku community and support resources, which provide valuable assistance and guidance.

Integration Capabilities: Many users highlight Dataiku's seamless integration with other tools and systems, enhancing their data workflows.

Transparency and Explainability: Dataiku's focus on model transparency and explainability is praised, enhancing trust in machine learning models.

Performance: The system can execute queries on massive amounts of data with agility, as specified by about 89% of users who mentioned performance.

Functionality: About 68% of users who reviewed functionality talked about its robust inbuilt features.

Ease of Use: The UI is simple and easy to navigate, according to about 72% of users who talked about user-friendliness.

Integration: Approximately 75% of reviewers who talked about integration said that it connects to numerous other tools seamlessly.

Scalability: All users who reviewed scalability said that the platform scales to thousands of servers.

Steep Learning Curve: Some users find Dataiku's learning curve to be relatively steep, particularly for those new to data science and machine learning.

Resource Intensive: Running complex operations and large-scale data processing in Dataiku can be resource-intensive, potentially requiring substantial computing power.

Costly Licensing: The cost of Dataiku's licensing can be a concern for small organizations or startups with limited budgets.

Limited Free Version: The free community edition of Dataiku has limitations in terms of features and scalability, which may not meet the needs of larger enterprises.

Integration Challenges: Some users encounter challenges when integrating Dataiku with certain legacy systems or non-standard data sources, requiring additional effort and customization.

Dependency on Data Quality: The effectiveness of Dataiku's analysis and modeling heavily relies on the quality of input data, which can be a challenge if data is not well-maintained.

Customization Complexity: Highly customized data workflows may require a deeper understanding of the platform, potentially making customization more complex.

Real-Time Processing: Dataiku may not be the ideal choice for applications requiring real-time data processing, as it primarily focuses on batch processing.

Competitive Market: Dataiku operates in a competitive market with various alternatives, making it essential for users to evaluate if it aligns with their specific needs and budget.

Security Concerns: While Dataiku offers security features, organizations handling highly sensitive data may need additional security measures to meet compliance standards.

Cost: Approximately 76% of users who mentioned cost complained that it’s expensive, and charges can rack up quickly if queries aren’t properly constructed.

Learning Curve: About 82% of users mentioned that the software has a steep learning curve.

Resources: About 89% of users who spoke about resources said that documentation and video tutorials are lacking and need improvement.

Visualization: Data visualization capabilities aren’t up to the mark, according to all users who talked about visualization.

User reviews for Dataiku reveal a mixed sentiment, with notable strengths and weaknesses. Users appreciate Dataiku's comprehensive feature set, user-friendly interface, and its effectiveness in facilitating collaboration among diverse teams. Scalability is another advantage, making it suitable for various organizational sizes. AutoML capabilities and real-time insights are well-received for their accessibility and timeliness. However, several users express concerns about a steep learning curve, especially for newcomers to data science. The platform's resource-intensive nature can be challenging, and the cost of licensing may be a barrier for smaller organizations. Some users find limitations in the free community edition and face integration challenges with legacy systems or non-standard data sources. Data quality dependency and customization complexity are other reported cons. Dataiku is often compared to similar products in a competitive market, and users stress the importance of evaluating it against specific needs and budgets. Security-conscious organizations may need additional measures when handling sensitive data. Despite its limitations, Dataiku maintains a strong user base due to its robust feature set and collaborative capabilities, enabling data-driven decision-making in various industries.

Bigquery is a scalable big data warehouse solution. It enables users to pull correlated data streams using SQL like queries. Queries are executed fast regardless of the size of the datasets. It manages the dynamic distribution of workloads across computational clusters. The easy-to-navigate UI is robust and allows the user to create and execute machine learning models seamlessly. Users liked that it can connect to a variety of data analytics and visualization tools. However, users complained that query optimization is an additional hassle they have to deal with, as the solution is expensive and poorly constructed queries can quickly accumulate charges. It can be overwhelming for the non-technical user, and SQL coding knowledge is required to leverage its data analysis capabilities. Data visualization features are lacking and in need of improvement.

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