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Structured & semi-structured data sources + analytics tools.
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Resources
Check out our content library, articles and documentation to learn about Data Management, Data Science and Panoply’s latest technology.
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Redshift at Lightspeed
In this webinar we’ll show you how to use cost-effective, and infinitely scalable data pipelines and management platforms built on top of Redshift. [+]
The Evolution of the Data Warehouse
A new type of Data Warehouse is arising. Cloud computing, the great democratizer, has allowed to create new paradigms that align with other general IT trends like Big Data, Virtualization... [+]
The Gorilla Guide to The History of Data Engineering
In this 70-page book, you will go on a journey of discovery. You will travel back to the early days of electronic data and trek through the decades all the... [+]
Data Engineering - Introduction and Epochs
Perhaps the biggest challenge inherent in Big Data projects is ensuring that there are underlying tools and infrastructure components— whether on-premises, in the cloud, or even in the car—to store... [+]
Data Engineering - Inventions & Milestones
A number of really smart people have contributed a lot of knowledge and insight over the years toward the research that comprises modern data engineering. There have been numerous inventions—both... [+]
Panoply: Smart Data Warehouse Overview
Panoply is the world’s only ETL-less smart data warehouse. Built for the cloud, Panoply delivers the industry’s fastest time to insights by eliminating the development and coding typically associated with... [+]
Built for Speed: Comparing Panoply and Amazon Redshift Rendering Performance Utilizing Tableau Visualizations
See how Panoply improved Tableau dashboard load time by 10x across the following dashboards in this Tableau-managed performance study. [+]
Data Science and Engineering Today
Whereas access to data and powerful information technology systems used to be considered a competitive business differentiator, today, access to such services is a prerequisite for business success. Today, businesses... [+]
Moving Data to the Cloud: Data Warehouse or Data Lake?
With big data, data warehouses, data lakes, it is easy to get confused about the pros and cons and what to use where. In the past two years, for instance,... [+]
Data Warehouse Trends Report 2017
More than 800 respondents and attendants to AWS RE:Invent conference held in Las Vegas in December 2016 shared feedback about data warehouse trends. [+]
Redshift vs BigQuery - Closing the Gaps in Data Warehousing
In this report we analyze some of the latest benchmarks and comparative research for AWS Redshift and Google BigQuery that has been capturing the Media attention in the late months.... [+]
Leveraging AWS to Overcome Data Warehousing Complexity
Data warehouse systems have evolved over time as overall computer performance increased. This evolution has allowed businesses to collect more data from more [+]
HoneyBook Case Study
When Honeybook moved to Panoply, they replaced the Vanilla Redshift cluster with a Panoply Powered AWS stack and immediately transferred all of their ETL management to Panoply’s pipeline dashboard. [+]
Zeek Case Study
Zeek uses Panoply to collect and connect various data sources from Mixpanel to Couchbase to Mailchimp and Zendesk and string them up together with a push of a button. [+]
Conquering Inefficiency in Data Analytics
Big data and analytics are coming to the forefront of enterprise IT requirements today and for a good reason to become a significant contributor to the bottom line. However, in... [+]
Using the Cloud to Maximize Return on Investment
Moving into a PaaS solution from a traditional on-premises solution can certainly be a leap of faith, as it requires giving up some measure of control. However, a cloud platform... [+]