Anybody who hangs out in IT or data science circles will invariably become familiar with the acronym GIGO. GIGO stands for “garbage in, garbage out” (or “good in, good out,” depending on who you ask). But semantics aside, it’s a…
Continue reading Less GIGO, more KNOW: Do’s and Don’t’s in Data Collection and Distribution
As a company formed by data engineers and analysts, we at Panoply love data in all its forms! But when you’re working with different types of data storage and processing tools, it’s important to understand the constraints and performance abilities…
Continue reading How To Tell The Difference Between A Database And A Data Warehouse
Data preparation, part of the data management process, involves collecting raw data from multiple sources and consolidating it into a file or database for analysis. Data preparation is an initial step in data warehousing, data mining, and machine learning projects.…
Continue reading Top Data Preparation Tools for Your Business
When we say “big data”, many think of the Hadoop technology stack. Is this the big data stack?Well, not anymore. Big data concepts are changing. The data community has diversified, with big data initiatives based on other technologies:The common denominator…
Continue reading Big Data Stack in Big Data Analytics | Panoply Blog
Just ten years ago, even the most advanced analytics professionals only had to manage a handful of data sources. But data volume, velocity, and variety are now increasing exponentially.In 2016, IBM estimated that 90% of the world’s data had been…
Continue reading Data Warehousing Concepts: Everything You Need to Know in 4 Minutes
Humans are visually motivated. When we see a movie, we’re drawn to the most beautiful and exotic settings, the most striking and recognizable actors, the biggest explosions. What we don’t spend a lot of time thinking about is the work…
Continue reading Business Intelligence and Data Warehousing Meet: Magic Ensues
Both business and tech analysts agree that a capacity for advanced analytics is becoming mission-critical to the basic viability for all companies. While some believe that we are only three to five years to this point, others estimate that there…
Continue reading Becoming an analytics driven organization: challenge accepted
Build vs. Buy—tale as old as time, song as old as…”I need that analysis yesterday!”Historically, a build vs. buy analysis was focused on on-premises vs. cloud. As cloud-based software services and data analytics tools have increased in popularity, the value…
Continue reading Cloud Data Warehouse Costs: A Build Vs. Buy Analysis
This series of posts analyzes how specific databases stack up against each other. We’ve covered the following comparisons so far:In this post, we’ll see how PostgreSQL compares to MongoDB. Who Uses These Databases?The following companies use either PostgreSQL or MongoDB.PostgreSQL: Apple,…
ETL. It should be fairly straightforward, right? We extract data, transform it as needed, and load it into the target application. But like the recipe for the perfect Bloody Mary or the ideal hamburger, someone else’s way of doing things…
Continue reading The Worst Advice We’ve Ever Heard About ETL
Nearly everyone in the IT world has heard the quote from Facebook’s Mark Zuckerberg about the need for speed and performance in technology—“Move fast, break things.” While that might apply to social media platforms, there is less forgiveness for breaking…
Continue reading 10 Minutes To A High-Performance Big Data Stack. Really.
2017 has come to an end, and as you’re gearing up for a successful 2018, you might find a cheat sheet of essential ETL tools and data storage comparisions handy. This list provides commentary to help you make the best decisions…
Continue reading Learn About ETL And Data Storage Tools You Need For 2018
