In Q1 2021, we’ve added another dimension to our strong relationship with AWS via new and innovative ways to leverage powerful machine learning and AI made for any advanced analyst or data scientist familiar with Python. Sisense and the AWS environment empowers you to unleash your inner analytics wiz to enrich your data and uncover new intelligence that will help you evolve your business.
Sisense Custom Code: Accelerating your advanced analytics
Sisense and AWS are better together and now there’s a brand new way to use Python from Jupyter Notebooks in Sisense to accelerate your advanced analytics or perform simple data prep and cleansing with ease.
The new Custom Code feature in Sisense enables you to run Python from within your Jupyter notebooks to transform, prepare, and cleanse data inside your ElastiCube models as part of the build process. Now, there are infinite ways that you can take robust and efficient Python code vs. long, messy SQL queries to augment your existing analysis and extract buried intelligence within hundreds of millions of bytes of data.
Using Amazon Comprehend, you can perform sentiment analysis to understand whether a customer review is positive, negative, or neutral, or apply named entity recognition to identify your data types as names, organizations, locations, and more. You can also translate text data into over 50 different languages to unite all your text data in a common language using both Sisense and AWS through a seamless experience — and that’s just the start.
Being able to draw out those key insights brings a deeper level of understanding to what you already know, and allows you to act upon your results faster to evolve your organization in ways you didn’t know were possible.
Start augmenting your data
To begin your data’s transformation, select “Add Custom Code” at the column, table, or data model level of your ElastiCube and then choose the notebook you’d like to layer additional analysis onto (we even have sample Jupyter notebooks for you to kickstart your efforts). Then comes the fun part:
The sky’s the limit when it comes to transforming your data using Python. Run your data through your custom Python code and even AWS Services like Amazon Comprehend, Amazon Translate or Amazon SageMaker to produce a new column of results in a new table. Say you have text data that includes your Amazon customer reviews. Run this data through sentiment analysis to add a column that specifies how customers feel about your products or company.
Take your analytics to new heights
We’re beyond excited to see what new questions you’ll answer using the new Custom Code capability in Sisense. It’s game changing in many ways, and helps to provide the flexible, integrated experience you need to get more out of your data stores.
To start leveraging this new feature in your workflows, download our latest release today! Delve into the technical details, including how to get set up, here.
Parke Hunter is a Product Marketing Manager at Sisense. She has over five years of experience in the software industry, including in customer success and product marketing. She is dedicated to empowering all Sisense customers to leverage analytics innovations to evolve their companies and disrupt their markets.