Google Cloud Datalab vs Sisense comparison

Read 46 Sisense reviews

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Google Cloud Datalab

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Executive SummaryUpdated on Jan 1, 2025

Sisense and Google Cloud Datalab compete in advanced data analytics. Sisense has the upper hand in cost-effectiveness and ease of use, while Google Cloud Datalab is preferred for its extensive feature set, reflecting its higher cost.

Features: Sisense includes robust analytics, intuitive drag-and-drop functions, and advanced visualizations. Google Cloud Datalab is known for its integration capabilities, in-depth machine learning options, and customizable architecture.

Room for Improvement: Sisense could enhance its customization options and simplify integration with more external platforms. It can also broaden its machine learning capabilities. Google Cloud Datalab might reduce its complexity for easier setup, clarify documentation on infrastructure configurations, and improve response times when handling large datasets.

Ease of Deployment and Customer Service: Sisense facilitates a straightforward deployment process with additional support services, benefiting businesses of various sizes. Google Cloud Datalab, while requiring more initial setup due to its complexity, offers excellent documentation and support for detailed customization.

Pricing and ROI: Sisense attracts budget-conscious clients with a lower setup cost and quicker ROI. Google Cloud Datalab's higher initial investment is justified by its comprehensive capabilities and extensive features.

To learn more, read our detailed Google Cloud Datalab vs. Sisense Report (Updated: September 2026).

Google Cloud Datalab vs. Sisense

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Helped 915,341 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Google Cloud Datalab

Ranking in Data Visualization

26th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Science Platforms (22nd)

Sisense

Ranking in Data Visualization

11th

Average Rating

8.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

BI (Business Intelligence) Tools (12th), Cloud Analytics (4th), Reporting (10th), Embedded BI (6th)

Mindshare comparison

As of October 2026, in the Data Visualization category, the mindshare of Google Cloud Datalab is 1.3%, up from 0.8% compared to the previous year. The mindshare of Sisense is 2.4%, down from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Visualization Mindshare Distribution
Product	Mindshare (%)
Sisense	2.4%
Google Cloud Datalab	1.3%
Other	96.3%

Data Visualization

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

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Vishal Halbe

System Integration Specialist at a non-profit with self employed

Has improved project turnaround time with customizable dashboards and faster data delivery to stakeholders

Sisense has some limitations, especially in rendering tables or graphs. For example, if you want to perform custom sorting or searching based on multiple columns, that becomes quite tricky. Providing more control over how the data is sorted or filtered would be quite useful. Additionally, it could provide more connectors to integrate with emerging different data sources to exponentially increase the amount of data it can handle. Sisense should provide more support for CI/CD, as we found the CI/CD approach quite limited, which is something that can be worked on. Some data scientists suggested more features can be offered by providing different models for data analytics. Sisense can improve by adding more features for handling complex schemas and making the pricing straightforward. The dashboarding features are good enough, but additional features could make it even more attractive. Also, CI/CD support or source control support would be really helpful.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"For me, it has been a stable product."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"All of the features of this product are quite good."

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"Sisense can do both in a much much faster way, which drives our reporting efficiency into another level."

"ElastiCube Manager is a very easy to use ETL tool, which includes the ability to easily transform raw data into reusable data."

"We are now able to increase our visibility into our business beyond the canned reports available in our ERP (NetSuite) and provide actionable, timely insight to our leadership."

"The Sisense data cube allows us to execute heavy, complex computations on the fly so customers can slice and dice the data based on their business needs."

"Using Sisense, we were able to get rid of those complex Excel sheets, improve the automation of the company and produce meaningful insights."

"We dropped Excel and saved hours of finance team work, with automated reports and dashboards."

"Reduced time to complete paper-based quarterly reports from one month to one day using Sisense to create dashboards."

"We monetize Sisense to our customers so they can transform it into their application, and it works as a native application."

More Sisense pros

Cons

"The interface should be more user-friendly."

"The product must be made more user-friendly."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"The application lacks a control of the exporting function."

"Sisense's scalability is tricky; I would like to see more infra-scaling functionality for effective injection capacity and DR functionality."

"Sisense has no native support for week as a data dimension, which is a major issue for us."

"The administrative side of Sisense is a little cumbersome and confusing."

"I'd like to see branding features and more layout possibilities."

"At present there are additional costs involved if we wish to share our data queues within this solution, which we would like to see removed."

"Users with viewer access have less capabilities than similar users in Tableau."

"The initial version we purchased only ran on Windows servers, which was less than ideal for our DevOps team. I believe that has been remedied in the latest release."

More Sisense cons

Pricing and Cost Advice

"It is affordable for us because we have a limited number of users."

"The product is cheap."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"This solution is more expensive than Tableau, Qlik and Dundas. It is an expensive tool. They charge $75,000 while Tableau is $45,000."

"Compared to Power BI or Tableau, Sisense is much more expensive."

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Top Industries

By visitors reading reviews

Construction Company

18%

Comms Service Provider

13%

Financial Services Firm

11%

Outsourcing Company

10%

Construction Company

12%

Outsourcing Company

11%

Comms Service Provider

10%

Financial Services Firm

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	29
Midsize Enterprise	7
Large Enterprise	16

Questions from the Community

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What needs improvement with Sisense?

So far, Sisense is performing well, but some visualizations lack flexibility in design options. Sisense should improve on handling extremely large data sets, and they should improve on maintaining ...

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What is your primary use case for Sisense?

In our organization, we use Sisense for business intelligence where it helps us with complex data from multiple sources and provides excellent insights, which makes it easy for us to make data-driv...

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What is your experience regarding pricing and costs for Sisense?

My experience with pricing, setup cost, and licensing has been positive, as the price is relatively affordable compared to other solutions, and I have no challenges with the pricing. The setup is s...

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Comparisons

Tableau Enterprise vs Google Cloud Datalab

Compared 8% of the time

Databricks vs Google Cloud Datalab

Compared 7% of the time

Qlik Sense vs Google Cloud Datalab

Compared 7% of the time

Dataiku vs Google Cloud Datalab

Compared 5% of the time

Anaconda Platform vs Google Cloud Datalab

Compared 5% of the time

More Google Cloud Datalab Competitors

Microsoft Power BI vs Sisense

Compared 7% of the time

Tableau Enterprise vs Sisense

Compared 6% of the time

Omni vs Sisense

Compared 6% of the time

Embeddable vs Sisense

Compared 4% of the time

Redwood Reporting - Report2Web vs Sisense

Compared 3% of the time

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Product Reports

Google Cloud Datalab

Download Google Cloud Datalab product report

Download Sisense product report

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Sisense enables data visualization and analytics with ease of use and fast setup, effectively integrating different data sources for efficient decision-making.

Sisense provides an intuitive platform for companies to handle data visualization and advanced analytics without requiring extensive technical knowledge. Quick deployment, custom dashboard creation, and seamless embedding are supported by an API-first approach. It allows integration of data from multiple databases, enhancing speed through in-chip methodology and real-time updates. Users benefit from its ElastiCube Manager and reduced deployment time, contributing to data-driven decisions. Comprehensive documentation supports users in navigating potential challenges.

What are the key features of Sisense?

Ease of Use: Facilitates quick data visualizations without technical expertise.
ElastiCube Manager: Allows custom data management and dashboard creation.
API-First Approach: Enhances integration flexibility across databases.
Real-Time Updates: Ensures timely and accurate data insights.
In-Chip Methodology: Optimizes processing speed and performance.

What benefits should users expect from Sisense?

Reduced Deployment Time: Accelerates analytics implementation.
Data-Driven Decisions: Facilitates informed decisions with comprehensive insights.
Customer Support: Provides reliable assistance and documentation.

Sisense is commonly used across industries such as product development, sales, and marketing to deliver tailored analytics and dashboards for diverse departmental needs. Organizations leverage Sisense's capabilities for embedding analytics, optimizing financial models, and improving sales strategies. Its intuitive interface allows non-technical teams to efficiently handle and derive insights from large datasets.

Sample Customers

Information Not Available

Ebay, WIX, Wave Accounting, ESPN.com, Magellan Luxury Hotel, Paylogic, Sony, Merck, EDA, One Hour Translation, NASA, Plastic Jungle, Philips, Yahoo

Google Cloud Datalab vs. Sisense