Sisense Notebooks: Advanced analysis into your dashboards and embedded pages

Notebooks are a way of performing ad-hoc analysis on disparate datasets, developing powerful charts which can be used for data storytelling while providing data security and integrity. It enables analysts to draw more from the data through advanced analysis using SQL, Python, and R. Data can be queried, customized, and visualized in the form of graphs and charts using Notebooks. 

Notebooks allow users to control the flow of information with custom queries to draw more complex insights from their datasets with higher accuracy and precision. They help in boosting data analysts’ productivity with integrated workflows, source control, advanced security, and more. Reusability of data is also something that can be achieved through Sisense notebooks. This can be done by recreating a view of the dataset to be more readable while acquiring a cleaner and enhanced version of the dataset which will aid in further analysis. data experts know that there are hundreds of different chart types, including Sankey, box plots, and others that are popular in the market. Notebooks support custom JavaScript chart types as well as Python and R chart types available through Matplotlib, Seaborn, GGPlot, and many others. They also allow charts developed by notebooks to be saved to dashboards or embedded into webpages. 

Notebooks also make it easy for analysts to access cloud resources through libraries such as Boto/AWS, GCP, and others. Sisense Fusion Analytics is a self-service capability as well as tools for data analysis. One of the tools provided here is Notebooks which play an important role as a self-servicing tool since it offers various functionalities through python and its libraries. Some of the key features used in Notebooks was the use of SQL and python by using packages like Boto, Seaborn, scikit, TensorFlow, ow, etc., to create custom code. The Sisense platform additionally offers a rich set of features including caching, modeling, SSO, APIs, security and control, and plug-ins. 

 Notebooks offer an integrated workflow, so that the analyst goes from model design to advanced analysis to visualization to source control, all in one location, and with the highest degree of data security. It gives teams a better approach to working on specialized projects and activities or advanced querying. This is done by providing high access control to teams keeping productivity and collaboration high. Sisense Fusion Analytics has an integration with Git which is a version-controlled cloud storage tool for codes of different programming languages. It delivers an integrated, end-to-end decision-making platform to help not just succeed, but thrive in this decision economy. Fusion Analytics offers speed, flexibility, and the ability to acquire deep insights quickly and easily for all stakeholders in the enterprise. 

Capabilities of Sisense Notebooks:  

Folders. Separate folders and subfolders can be created in notebooks to better suit an analyst’s preferences and workflow. 

Sharing. For easy collaboration, notebooks can be shared with or without the ability of the receiver to view the SQL and Python code. Analysts can share their work with other analysts for a second perspective by including SQL and/or Python code. Sharing Notebooks without the code, on the other hand, allows analysts to share data with business users. 

Cloud Integration. Notebooks make it possible to use Python to access cloud services. AWS Lambda functions, for example, could be called, or documents on S3 might be read and processed immediately. The ability to use libraries like boto3 expands Python’s capabilities significantly. 

Machine Learning and AI. Python makes it possible to create, train, and deploy machine learning models. Libraries like scikit-learn, Keras, and Theano, among others, are supported by notebooks. Additionally, using the pip install commands, extra libraries can be added per session, allowing for more extensive analytics beyond the baseline packages. Models can be trained and stored in cloud storage services like S3 for later usage and application with new data. 

Notebooks combine code-first analytics with model-based business intelligence, allowing you to expand analytics delivery and interact more effectively with other analysts and business users. Without requiring the analyst to generate dimensional models, code-first can be used as a standalone development environment. Notebooks open the door to advanced analytical applications like Machine Learning and Artificial Intelligence by extending your SQL analysis using Python and R. 

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