Photo by Windows on Unsplash It seems like it might be finally happening. Data engineering is finally getting a little bit of the data lime light. Which makes sense. The data being stored and analyzed is not only becoming more voluminous but its speed, complexity and variety are also increasing. Making it difficult to wrangle. With […]
Photo by Samuel Sianipar on Unsplash In the business world, cloud technology has become more and more dominant in recent years. Right now, research shows that about 50% of all business data is stored in the cloud, which just demonstrates the importance of external data sources and their place in the modern business environment. In […]
Photo by Karo Kujanpaa on Unsplash By Travis Wolf Introduction — “What is MLOps? — DevOps for ML” — Kaz Sato Challenges arise as the production of machine learning models scale up to an enterprise level. MLOps plays a role in mitigating some of the challenges like handling scalability, automation, reducing dependencies, and streamlining decision making. Simply […]
Background In 2014 a group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. This paper pointed out a growing problem that many companies might have been ignoring. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the […]
Photo by SELİM ARDA ERYILMAZ on Unsplash The rise in self-service analytics is a significant selling point for data warehousing, automatic data integrations, and drag and drop dashboards. In fact, in 2020, the largest software IPO this year was a data warehousing company called Snowflake. The question is how do you get your data from external […]
Data warehousing was introduced in 1988 by IBM researchers Barry Devlin and Paul Murphy. Since then the concept has evolved and taken on a life of its own. Increasing challenges and complexities of business have forced data warehousing to become a distinct discipline. Over the years this has led to best business practices, improved technologies, and hundreds […]
There is a lot of talk about how data, data science and machine learning can all be applied to help make critical businesses decisions. As our team works with various companies across the US and in various industries, we have had a lot of opportunities to do more than just talk. We have helped many […]
Photo by Markus Spiske on Unsplash Python really is everywhere at this point. Although many gatekeepers argue whether a person is really a software developer if they don’t code in a language more difficult than Python, it still is everywhere. It’s used to automate, manage websites, analyze data, and wrangle big data. As data grows, the way we manage it […]
Photo by Karl Bewick on Unsplash In the 20th century we were reliant on companies relied on servers and computers to be on premise. This meant when new servers had to be spun up, it could take weeks or even months to get everything set up. From getting budget, putting out orders, having servers shipped and then installed […]
Photo by Martin Adams on Unsplash Introduction Now that organizations are dealing with big data on a day-to-day basis to generate useful insights, we require more efficient software/data development lifecycles. The era of big data calls for some powerful data operation tools which can automate processes and reduce the cycle time of the data analytics for enormous datasets. […]