Google Cloud Datalab Reviews & Product Details

Preinstalled libraries and packages Datalab comes with a rich set of preinstalled libraries and packages commonly used in data analysis, such as TensorFlow, pandas, NumPy, and matplotlib. This saves time and effort in setting up the environment and installing dependencies. Collaboration and sharing: Datalab supports collaborative work, allowing multiple users to work on the same notebook simultaneously. Review collected by and hosted on G2.com.

Limited programming language support Datalab primarily supports Python and SQL for data analysis. If you prefer working with other programming languages, such as R or Julia, you may find the language options limited. Review collected by and hosted on G2.com.

Seamless integration with Google Cloud Platform (GCP): Datalab integrates with other GCP services like BigQuery, Cloud Storage, and Dataflow, making it easy to work with data stored in the cloud. Datalab's Jupyter notebook interface is easy to use and allows users to collaborate on data analysis projects. Review collected by and hosted on G2.com.

Since Datalab is designed to work with Google Cloud Platform, it may not be the best option for users who are not already using GCP or who want to work with data stored on other platforms. While Datalab itself is free, users will need to pay for other GCP services like BigQuery or Cloud Storage to use it effectively. Depending on the size of your data and the complexity of your analysis, costs can add up quickly. Review collected by and hosted on G2.com.

Integrated Environment: One of the key advantages of Google Cloud Datalab is its seamless integration with the Google Cloud ecosystem. As a cloud-based solution, Datalab provides a unified environment for data exploration, combining tools like Jupyter Notebooks, BigQuery, and Cloud Storage. This integration simplifies the process of accessing, analyzing, and visualizing data, making it easier to derive insights from large datasets. Review collected by and hosted on G2.com.

Limited Scalability: While Google Cloud Datalab is suitable for exploratory data analysis and small-scale projects, it may face limitations when dealing with large datasets or computationally intensive tasks. Since Datalab runs on virtual machines, there may be performance bottlenecks when working with extensive data processing or complex machine learning algorithms. In such cases, leveraging other Google Cloud services like BigQuery or Dataflow might be necessary for improved scalability and performance. Review collected by and hosted on G2.com.

It is easy to use and the data lab contains more authentication it cloud be more secure to access Review collected by and hosted on G2.com.

It is having dashboard which is hard to access . Need to be user friendly Review collected by and hosted on G2.com.

I am a newbie to GCD as I just started learning data science and would love a platform that works better for a beginner. It supports tremendous volume of data for data visualization without any hustle and thereby I can do the work without any stress. The main thing which I like about GCD is that it supports various programming languages. Review collected by and hosted on G2.com.

The pricing would have little lesser than present one as it is not far affordable for a small startup. When compared to looker with Gcd, I would recommend looker as it have lot of latest features for a data scientist. Review collected by and hosted on G2.com.

It provides interfaces with all major frameworks of python like tensorflow, pytorch. You can create your notebooks and interact with the data and performs all sorts of machine learning tasks on it. One more powerful thing is that it integrates well with google VMs. Review collected by and hosted on G2.com.

One of the major drawback I felt with google datalab is that, it is sometimes very unstable with VPN setting. This makes the notebook kernel disconnect and one have to connect to kernel all over again. Review collected by and hosted on G2.com.

GCD is a powerful, relatively intuitive data lab solution. Google is consistently updating and adding more features to it. Using it you will always be kept on the cutting edge. Review collected by and hosted on G2.com.

Some data lab power-user tools can be challenging to find on occasion. Buried in menus or extra add-on feature libraries, causing a bit of a learning curve to how Google provides access to less used features. Review collected by and hosted on G2.com.

Google data lab has integration with popular languages like JS, BigQuery and it can be integrated with Jupyter notebook and python which can be used for statistical analysis. It is the best tool for data processing and Cloud storage. Review collected by and hosted on G2.com.

It requires more effort to learn google datalab and its features, it can be complex for beginners who have little to no experience in ML and Cloud. And sometimes not all the softwares in google datalab is updated. Review collected by and hosted on G2.com.

its flexible , simple and very useful cloud platform for safe and effective data storage.

Google Cloud Storage is one of the simplest and easiest to use options for finding files, documents, and photos Review collected by and hosted on G2.com.

thing to dislike in google cloud storage it is love one and safe cloud storage for data Review collected by and hosted on G2.com.

The on cloud jupyter notebook helps me run various code without thinking about the scalability and data volume . It supports large volumes of data without any glitch . Review collected by and hosted on G2.com.

Doesn't support R language. Though most of my pipelines are in python , support for R is restricted . This restricting various problems that can be solved using R. Review collected by and hosted on G2.com.