Simor Consulting
Category: Data Architecture
Data pipelines for AI are not the same as data pipelines for traditional software systems. The outputs are different. The failure modes are different. The tolerance for data quality issues is differen
The orchestration market has a clear incumbent and two serious challengers. Apache Airflow has been the default choice since 2015. Prefect and Dagster both emerged to address Airflow's pain points, bu
A manufacturing company with facilities in twelve countries ran its operational reporting on a traditional BI stack: a data warehouse, an ETL pipeline, and a dashboard tool that had been deployed six
A logistics company processing two million shipments per day ran their entire operational reporting stack on nightly batch ETL. Every morning at 6 AM, operations managers reviewed dashboards built on
Every analytics team eventually faces the same choice: how do you transform raw data into something analysts can actually use? For years, dbt was the only serious answer. SQLMesh arrived with a differ
A financial services firm running analytics on trade settlement data came to us with a specific complaint: their cloud data platform cost had tripled in eighteen months, and nobody could explain why.
A recommendation system team built their tenth model. Each model required feature engineering. Each feature engineering project started by copying code from the previous project, then modifying it for
Traditional centralized data architectures worked for BI but struggle with AI workloads. Centralized teams become bottlenecks as data volumes grow. Domain experts who understand the data are separated
Existing data infrastructure often cannot support ML workflows. The modern data stack offers a foundation, but it requires adaptation to become AI-ready. This article covers building a data architectu
Event-driven architectures treat changes in state as events that trigger immediate actions and data flows. Rather than processing data in batches or through scheduled jobs, components react to changes
# Serverless Data Pipelines: Architecture Patterns Serverless computing eliminates server management and provides automatic scaling with pay-per-use billing. These benefits matter for data pipelines
A semantic layer provides business-friendly abstraction over technical data structures, enabling self-service analytics and consistent metric interpretation. Implementing one involves technical challe
Data lakehouses combine lake flexibility with warehouse performance but introduce security challenges from their hybrid nature. Securing these environments requires layered approaches covering authent