Raisa Tech

#design system #frontend #UI

Owning the Steering Wheel: How We Rebuilt Our Design System Dropdown and Multiselect

The dropdown and multiselect components are among the most fundamental building blocks in our design system. In a data-heavy application where users constantly filter, search, and select from large datasets, these components need to be performant, accessible, and flexible enough to handle diverse use cases across our product. Component HistoryOur

#llms #Data Pipelines #Document Parsing

Outperforming Industry-Standard Table Extraction: Building Our Own Data Pipeline

In the oil and gas industry, production data is everything. Daily production values form the foundation of well valuations and production forecasting. Get those numbers wrong or get them late, and your models fall apart. We have access to roughly 180,000 pages per year of the most accurate production

#design system #frontend #UI

Owning the Steering Wheel: How We Rebuilt Our Design System Table

The table is one of the core components in our product, especially in a data‑heavy application, where multiple user flows depend on it being both performant and predictable. Component historyThe table has always been an evolving component in our library, going through several phases and heading in multiple directions

#data engineering #llms

From Data to Insights: Building an Autonomous Analytics System with LangGraph

How I Built a Multi-Agent System That Automatically Transforms Raw Snowflake Data into Polished Business Reports. Across the organization, stakeholders regularly need answers: KPI summaries, financial comparisons, and model performance insights. These questions often come up ahead of meetings, reviews, or decision points, and they usually need answers now, not

#dev #dotnet #testing

From Chaos to Confidence: A Practical Guide to Unit Testing in Clean Architecture

The Problem: A Growing Codebase, Growing ConcernsLet's start by asking ourselves why do we need unit tests? Catch Bugs Early – Identify issues in individual components before integration.Improve Code Quality – Enforce better coding practices and maintainability as writing code for testing ensures adhering to the coding architecture and when a

Beyond the Browser: Integrating Financial Tools into Excel Workflows

In the oil and gas industry, data drives million-dollar decisions. As a software team working in asset management, we built web dashboards that helped our users evaluate, buy, and manage oil and gas fields across the US. Our dashboards were a mix of modern web applications and data dashboards built

#data engineering #dbt #pipelines

Lint, Test, Deploy: Building a dbt CI Pipeline with SQLFluff Style Checks

Enhancing dbt Development with Automated PR Validation in Azure PipelinesIn the fast-paced world of data engineering, maintaining high-quality, consistent code is crucial. As teams grow and projects scale, the need for thorough validation of pull requests (PRs) becomes even more important. This ensures minimal errors, enforces best practices, and guarantees

#data engineering #data analysis #data science

Snowflake Notebooks: What I Love and What I Hope to See Next

I recently explored Snowflake Notebooks, which have recently became generally available, and found it quite interesting. In this article, I’ll cover the following topics: What Snowflake Notebook is.4 key features which I love in notebooks.3 additional features I’d like to see.Ideas for integrating it into

#data engineering #evaluating data sources

Establishing a Framework for Evaluating Data Sources Quantitatively

Data has become one of the most critical assets in any business setting with significant influence on a business' success of failure. Ease of access to quality data enables business stakeholders to make accurate data-driven decisions and, depending on the timeliness of the data, gives an edge over competitors to

#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