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Integrations / Sales & Marketing / iTunes Connect
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Move iTunes Connect Data into a Data Warehouse in Minutes

Panoply makes it easy to store and explore iTunes Connect data. The iTunes Connect integration quickly moves all of your Connect data into Panoply’s own Smart Data Warehouse, where it is imported to the cloud automatically without needing to manage an ETL or ELT process.

iTunes Connect lets developers manage their media content that is sold and viewed on iTunes, and provides developers with data on downloads and purchases. With Panoply, that data can be instantly combined with other data sources, and your analysts and marketers can create reports and dashboards that make it possible to learn more about your iTunes audience.

Panoply’s end-to-end “smart” data management platform makes it easier than ever for SMBs to store data, identify opportunities, and optimize their apps and media, all in one central location.

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Panoply makes it easy to analyze iTunes Connect data

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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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