Move Stripe Data into a Data Warehouse in Minutes

Let Panoply integrate your Stripe data into your cloud data warehouse in moments, so that it is ready for immediate analysis. Stripe, known best for its online payment system, provides sales and customer data in the form of their Sigma product.

Panoply can extract that data directly from Stripe and instantly add it to your data warehouse without any additional ETL or ELT support. Stripe data can then be queried, compared, and analyzed from within the Panoply platform both by itself and with other data sources.

With Panoply, businesses can generate better customer analytics, determine market opportunities, reduce return rates, and compare across data channels all from within Panoply’s single, end to end data management platform. Panoply makes it easier for companies to use their Stripe data without an expensive and expansive data warehouse and analysis infrastructure.

Panoply makes it easy to analyze Stripe data

Why Panoply?

Use-Case Optimization

Analyzes queries and data – identifying the best configuration for each use case, adjusting it over time, and building indexes, sortkeys, diskeys, data types, vacuuming, and partitioning.

Query Optimization

Identifies queries that do not follow best practices – such as those that include nested loops or implicit casting – and rewrites them to an equivalent query requiring a fraction of the runtime or resources.

Server Optimization

Optimizes server configurations over time based on query patterns and by learning which server setup works best. The platform switches server types seamlessly and measures the resulting performance.

Updates, Upserts, and Deletions

Supports standard SQL update and upset operations out of the box – without worrying about vacuuming or rebuilding- unlike many analytical databases.

Semi-Structured Data Parsing

Supports semi-structured text values like nested JSON, user-agent strings, some standard log formats, CSV, and serialized Ruby objects – parsing these objects and normalizing them into a relational database design.

Nested Structures

Handles nested structures automatically, flattening them into several tables with a one-to- many relationship. The result is a ready-to- use relational database design for all current and future datasets.

Columnar Storage

Provides seamless data storage and management in a multitiered, columnar storage based on Amazon Redshift, Elasticsearch, and Hadoop or SS3.

Data Tracking and Alerts

Simplifies how you keep track of vast amounts of data – by identifying patterns, providing notification of anomalies, and generating alerts when the results of arbitrary SQL queries exceed predefined thresholds.

Backup and Recovery

Automatically backs up changes to data to a redundant S3 storage, optionally saved in two different availability zones across continents – enabling full recovery to any point in time.

From raw data to analysis in under 10 minutes

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