#data science #curve fitting #arps Bulk Arps Curve Fitting on GPU This article proposes a method for significantly faster Arps decline curve fitting on large datasets using TensorFlow on GPUs. It tackles the limitations of traditional approaches, which rely on iterative curve fitting, like SciPy's curve_fit, applied to each data point one at a time. By leveraging TensorFlow's parallel processing
#data engineering #data quality #snowflake Text Similarity Analysis at Scale Think of your data as a giant puzzle, where the pieces often wear clever disguises. As a data engineer, you're often left trying to put the pieces back together, but there's a catch - you have thousands of pieces to sift through. Here's where text similarity can be a handy
#data science #llms #chatgpt Cost-Efficient Text-to-SQL Chatbot Using ChatGPT LLMs Overview Have you ever wondered how to make data more accessible to other employees in your organization without them having to deal with the headache of SQL queries? We would like to share with you our attempt at solving this problem by making a cost-efficient Text-to-SQL chatbot at Raisa. In
Recording Data History Between The Application Level and SQL Server Temporal Tables (part 1) Imagine logging into your application's database and finding a change in a data record that just doesn’t make any sense. You trace the application code, check for any bugs, sit with the users, try reproducing the change and all of this doesn't get you anywhere! Wouldn't it give you
Inductive Clustering For Lower Dimension Input Vectors IntroductionWe often need to expand or generalize the output of clustering algorithms for unseen data points in training without recomputing the clustering. Some clustering algorithms support this by nature, which is called Inductive Clustering. This article will discuss extending the non-inductive clustering algorithms to classify new instances and classify data
Lineage driven tasks orchestration IntroductionAccording to a new market pulse survey conducted by Matillion and International Data Group (IDG), which drew insight from 200 enterprise data professionals, companies are drawing from an average of over 400 different data sources. It doesn't stop there, but the survey found that, on average, enterprise data volumes are
#deep learning #data science #time series Time Series Forecasting using Transformers and Ordinal Regression Raisa's Domain and Time Series PredictionOne of the things we do at Raisa is forecasting oil and gas production for all wells in the United States. Forecasting are usually done for the next twelve months based on previous data, which is by definition a time series forecasting problem.With the
#machine learning #data science Introduction to Probabilistic Deep Learning Why Bayesian deep learning?Have you ever tried to learn about Bayesian neural networks but got hit in the face with a barrage of equations? Have you ever implemented a Bayesian neural network without actually understanding why this works? If the answer is yes to any of those questions, then
#design system #frontend #UX Design System at Raisa More Teams are changing the way they design digital products by incorporating their unique design systems. By utilizing a collection of repeatable components and a set of standards guiding the use of those components, the change has been proven to affect the pace of creation and innovation. In the last
#data engineering #data quality Data Quality Testing - The What, How, Why ... Tools and Practices This is a long read, so let me first tell you what exactly you’ll be getting out of reading this article. Understanding the importance of data quality and data testingExplore the main differences between two of the most prominent data testing tools (Great Expectations and DBT) with a data
