End-to-end cloud data warehouse & management service.

Integrations

Structured & semi-structured data sources + analytics tools.

Customers

Trusted by data teams big & small. #1 in satisfaction on G2 Crowd.

Blog

Insights on data management, infrastructure, & analysis.

Pricing

Simple & transparent. 21-day free trial, no credit card required.

Demo

Schedule a test drive.

Integrations / Databases / Hadoop

Move Hadoop Data into a Data Warehouse in Minutes

Panoply makes it easy to combine your Hadoop HDFS data into your Panoply Smart Data Warehouse, giving you instant cloud access to your HDFS data without any ETL or ELT process. HDFS, or the Hadoop Distributed File System, is an open source data storage software framework. Panoply’s end to end data management solution is able to load Hadoop Data into your Panoply Smart Data Warehouse with only a few clicks, giving your analysts and scientists instant access.

Those that have had to choose Hadoop vs. a data warehouse are now given a solution with Panoply. Within minutes, your data is ready to be combined and analyzed with all other data sources. Your data team can immediately identify meaningful business solutions by generating custom reports and dashboards from within Panoply, all without any custom scripts.

Try Panoply

Panoply makes it easy to analyze Hadoop 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.