Source https://unsplash.com/photos/sb7RUrRMaC4 Snowflake is a cloud data platform. To be more specific it’s the first cloud built data platform. Its architecture allows data specialists to not only create data warehouses but also cloud data lake-houses because it can manage both structured and unstructured data easily. Being that Snowflake is based in the cloud, this means that it…
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Photo by Jakub Skafiriak on Unsplash Born out of frustration with the only open source distributed programming implementation of the time, Apache Spark was created in the UC Berkeley AMPLab in 2014 to replace it’s predecessor Hadoop MapReduce. MapReduce was robust but burdened by excessive boiler plating, serialization and deserialization. MapReduce was created to anticipate node…
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Photo by CHUTTERSNAP on Unsplash In our last article, we outlined what to avoid when you modernize your data architecture. That was more of an introduction piece for people to know what to do when they start developing their data strategies. Now we are going to work to start developing a strategy to modernize your…
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Photo by CHUTTERSNAP on Unsplash When it comes to best practices and business alignment, most new data engineers learn as they go on the job. From building overly complex and unsustainable systems to putting too much faith in existing data structures, here are five of the most common mistakes and traps that even the most…
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Photo by Tim Mossholder on Unsplash By Travis Wolf The goal of completing a machine learning model lifecycle is to deploy and maintain a working pipeline of data. As more companies look to implement ML models to optimize data-driven functions, being able to deliver and maintain these models will require a web framework that can…
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“Futuristic research from International Data Corporation (IDC) indicates by 2022 organizations will devote approximately $1.97 trillion to enable Digital Transformation” Introduction Technology is continuously evolving, and with every new turn, results in the consumption of large amounts of data. Researches from firms such as IBM show we create approximately 2.5 quintillion bytes of data per…
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*Stationary, Auto-correlation, Differencing Accurately forecasting costs, sales, user growth, patient readmission, etc is an important step to providing directors actionable information. This can be difficult to model by hand or in Excel. In addition, using traditional methods like moving averages might not provide enough insight into the various trends and seasonality that occur in real…
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We hope you have enjoyed both previous parts(part 1, part 2) of this intro to data analysis and data science. In the next few sections we will be looking at different data sets, asking questions and posing possible stopping points where you can try to think about what you might do or conclude from the data…
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Data analysis is part of any data scientists daily work (along with data munging and cleansing). It is also very important for a good portion of everyone else in the modern workforce. This could be a systems analysts, business owners, financial teams, and project managers. However, most undergrad courses do not (or at the very…
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If you have worked with financial or accounting data, then you know that a $10 discrepancy today can become a $100,000 tomorrow. An error in a data storage system or business intelligence report might seem small at first. It can be tempting for analysts to ignore it, or fudge the numbers for the month. Maybe…
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