Complete end-to-end data management service.

Integrations

Structured & semi-structured data sources + analytics tools.

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Learn to use Panoply's data management platform.

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Integrations / Files & Services / Amazon S3

Move Amazon S3 Data into a Data Warehouse in Minutes

Panoply provides end to end data automation, from extraction to analysis, all with an ETL-free pipeline that is capable of connecting to both structured and semi-structured data sources. One of its most popular integrations is with Amazon Simple Storage Service, known as Amazon S3, a cloud based scalable storage solution.

It takes only a few minutes to setup Panoply’s integration with Amazon S3, allowing businesses of all sizes to import their historical data to the Panoply platform with only a few clicks. With Panoply, your Amazon S3 data will quickly become a part of your data warehouse and can be analyzed along with any additional data sources you may collect. With Panoply, you can start turning your Amazon S3 storage into useable data, all without any additional custom scripts.

Try Panoply

Panoply makes it easy to analyze Amazon S3 data

CHOOSING US

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.