Google Cloud DataLab - Features and Benefits

Google Cloud DataLab is a tool for exploratory data analysis, data visualization, and building machine learning models. It is a powerful BI Engine tool for structured and unstructured data to analyse the patterns in every dataset.

Features of Cloud DataLab 

• AI integration: Cloud DataLab integrates with Data processing, BigQuery, Cloud Storage, for faster processing of the data and the data is sent for further analysis through a custom pipeline.
• Machine learning: It supports the TensorFlow based model and scikit.It can also be integrated with Jupyter Notebook for running python.
• Supported by python: Google DataLab is also functionable on Jupyter for python packages. It can be used for statistical analysis, exploratory data analysis, advanced data science research analysis, pattern recognition, etc. 
• Wider range of language support: Cloud Data Lab supports Javascript, SQL, and Big Query.
• Data visualization: Using python packages like matplotlib, the data can be visualized in a simple way to find the patterns and trends among the data. For data cleaning, it imports the libraries.

Benefits of Google Cloud DataLab

• Highly scalable: It is a highly scalable data visualization tool based on python. After querying the data in BigQuery the data is then transformed into DataLab to run the analysis job.
• Data management: Using Cloud Datalab to gain insight from data. It explores, transforms, analyzes, and visualizes data using BigQuery, Cloud Storage, and Python.
• Open source: It can access the data from  BigQuery, AI Platform, Compute Engine, and Cloud Storage.
• Python support: It has the main advantage of python packages. As Datalab is based on Jupyter, it can access many other packages like the statistics packages.

Technicalities in Data Lab

Cloud Datalab uses templates instead of text files containing code. Notebooks integrate code, documentation written in markdown, and the results of code execution can be as text, image, or HTML or JavaScript. Like a code editor or IDE, notebooks allow to execute code in an interactive and iterative manner. While sharing the details with team members, it includes code, markdown documentation, and results that include interactive and informative charts, to provide them with context with Python or SQL query files.