Data Science Dojo

OCTOBER 10, 2023

IMPACT 2023- The Data Observability Summit (Virtual event – November 8) Focus on Data and AI : The summit will illuminate how contemporary technical teams are crafting impactful and performant data and AI products that businesses can rely on. Link to event -> Live!

AI 243

AI AI Data Observability Artificial Intelligence 243

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. The most important reason for using DBT in Data Vault 2.0 Managing a data vault with SQL is a real challenge.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

MAY 24, 2022

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. Data Profiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. Comprehensive Data Management: Supports data movement, synchronisation, quality, and management. Scalability: Designed to handle large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

FEBRUARY 8, 2022

While data fabric takes a product-and-tech-centric approach, data mesh takes a completely different perspective. Data mesh inverts the common model of having a centralized team (such as a data engineering team), who manage and transform data for wider consumption. But why is such an inversion needed?

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52