The Advanced Analytics Platform
For Data Teams

Robust, code-driven tools for analyzing complex data. Utilize machine learning and predictive analytics with SQL, R and Python all in the same environment.

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Get Deeper Insights with Powerful Tools
for Advanced Analytics

With increasing volumes and complexity of data, the process of transforming raw data into actionable insights requires more powerful tools. Sisense for Cloud Data Teams (previously Periscope Data) provides data teams with the ability to build cloud data pipelines and quickly perform advanced analysis using the languages they prefer, all while utilizing software development best practice workflows.

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Write Queries, Analyze Results

Avoid the pitfalls of fragmented open-source toolchains and single-user workbenches with the integrated suite of capabilities in Sisense for Cloud Data Teams

Explore Data with Code

Write queries in SQL, analyze results using Python and R. Create reusable analysis frameworks for a more iterative experience through code views and snippets.

Visualize Results

Visualize query results or convert them to models written back to AWS Redshift or Snowflake. Visualize massive quantities of data with server-side matplotlib and ggplot.

Deliver Custom Insights

Deliver interactive visualizations with powerful charting libraries. Allow users to slice and dice, pivot, filter, and download data with ease.


Build Cloud Pipelines

Extract and transform data without the fragility of production ELT infrastructure with Sisense’s Cloud Data Pipelines.

Launch Your Cloud Data Warehouse

Utilize Managed Cloud Data Pipelines to transition to your own AWS Redshift or Snowflake deployments with no downtime.
The built-in Summary Statistics functionality helps you quickly understand data at a glance to identify patterns, relationships, and unexpected values.

Integrate New Data

Ingest new sources directly into your warehouse and respond to requests without the overhead of semantic modeling by working with both unmodeled and modeled data.visualizations using Python libraries or R packages such as plot.ly.
Create advanced, highly customizable visualizations using Python libraries or R packages such as plot.ly.

Improve Query Performance

Prototype new transformations before introducing them into nightly ELT processes. Reduce query times and computing costs by materializing views and reporting tables.
Automate and manage changes by syncing all user-generated content through Git, using GitHub, GitLab, BitBucket, or your own Git server.


Employ Development Best Practices

Level up your data team by developing your analytics like you develop software.

Unlock Advanced Analytics

Augment your data with predictive and statistical libraries or custom scripts and write results back to your warehouse to add crucial insights and generate advanced analytics.

Centralize Code

Collaborate with your team and save time on new projects by centralizing frequently used in-house code and logic with snippets and views.

Use Git Version Control

Control, track and manage changes to your queries and dashboards with a bi-drectional sync into the Git repository of your choosing.

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