Category: Data & Analytics

The best Python libraries for parallel processing

Do you need to distribute a heavy Python workload across multiple CPUs or a compute…

Overcoming data inconsistency with a universal semantic layer

Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic,…

How to support accurate revenue forecasting with data science and dataops

Data science and dataops have a critical role to play in developing revenue forecasts business…

Dataframes explained: The modern in-memory data science format

Dataframes are a staple element of data science libraries and frameworks. Here's why many developers…

Better together? Why AWS is unifying data analytics and AI services in SageMaker

Demand for end-to-end platforms, the convergence of data and AI, and the evolution of roles…

5 ways data teams must lead in AI-driven organizations

The future of work requires data teams to lead with data governance, ops, and products…

Teradata adds Enterprise Vector Store to augment RAG

Teradata’s partnership with Nvidia will allow developers to fine-tune NeMo Retriever microservices with custom models…

Google’s AlloyDB is looking more and more like PostgreSQL

New filtering and observability functionality is similar to that of PostgreSQL, but Google has optimized…

Exploring the Apache ecosystem for data analysis

How Apache Arrow, Apache Parquet, Arrow Flight, and DataFusion bring enhanced data processing capabilities to…

How to choose a data analytics and machine learning platform

A brief guide to data visualization, data analytics, and data science platform capabilities and differences,…