Trusted by data teams big & small
#1 in Customer Satisfaction on G2 Crowd

High Growth Startups

Kai Rikhye Lead Data Engineer
“Panoply's machine learning based data warehouse management saves a lot of time spent on maintenance and trial-and-error tuning, allowing data engineers to focus on helping deliver business insights.”

InVision used Panoply to automate its ingestion & data warehouse management — going from nothing to a fully functional warehouse in a single day.

One of the biggest challenges of running a data warehouse in a high-growth company is having to continuously manage capacity and performance as schemas and workloads rapidly evolve. With Panoply our team could focus more on insights rather than maintannce.

Enterprise at Scale

Kimberly-Clark expanded analytic capabilities without more staff

Panoply automated Kimberly-Clark's data wrangling and analytics needs, saving time and headcount. On just one regional retail report the team saved eight hours per week—equivalent to $250,000 every two years. This report is one example of Kimberly Clark’s “huge return on investment,” bringing new capabilities without adding another Business Analyst to the team or undergoing lengthy requirements-gathering or customer development processes.
Helena Carre EMEA Omnichannel Analytics
“Panoply gave me the things I needed - speed, automation, efficiency, flexibility - without blowing up my budget, increasing my headcount, or adding unneeded complexity. It continues to be a great complement to my existing resources.”

Simplifying Redshift

Abhi Sivasailam Director of Analytics
“We needed a tool that would take away the need to support countless ETL processes on one hand and manage all of the day to day data and storage optimizations on the other hand so that me and my analysts can focus on building our models and providing value to the organization.”

Honeybook was running into Redshift performance problems and spending too much time managing data and ETL processes.

Panoply centralized HoneyBook data, allowing them to easily query joined data sets in seconds. This helped HoneyBook to scale data without increasing engineering headcount and improved daily query times from hours to mins.
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.

From raw data to analysis in under 10 minutes

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