Move MongoDB Data into a Data Warehouse in Minutes

Panoply integrates both SQL and NoSQL data from any existing DBMS. Those capabilities would not be complete without MongoDB, the most widely used NoSQL DBMS. MongoDB is known for its automatic, easily scaled data clusters.

With Panoply, importing MongoDB data is as simple as a few clicks, and once there it is easily accessible from within the cloud database. It can be combined with other SQL and NoSQL data sources, and your data scientists and analysts can immediately query the data and drive actionable conclusions.

The Panoply platform makes it easy to consolidate all of your data, including MongoDB, and begin drawing conclusions in minutes without any custom scripts or programming. Secure, scalable, and powerful, Panoply is an invaluable tool for making use of your stored MongoDB data and replaces the need for a large data infrastructure. Create your own MongoDB data warehouse with Panoply.

Panoply makes it easy to analyze MongoDB 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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