Dataconomy

APRIL 25, 2025

Dplyr simplifies this process significantly, enhancing data quality and facilitating thorough analysis. Benefits of using dplyr Using dplyr offers several advantages: Saves time in data preparation tasks. Improves comprehension through a user-friendly syntax. Facilitates easier conversion of datasets for visualization.

Data Analysis 91

Data Analysis Data Analysis Data Preparation Data Scientist 91

Data Science Blog

JUNE 27, 2023

The batch views within the Lambda architecture allow for the application of more complex or resource-intensive rules, resulting in superior data quality and reduced bias over time. On the other hand, the real-time views provide immediate access to the most current data.

Big Data 130

Big Data Big Data Apache Kafka Database 130

Towards AI

AUGUST 16, 2023

Data quality: ensuring the data received in production is processed in the same way as the training data. We can also identify some important differences with AI projects in the context of MLOps: the need to version code, data, and models; tracking model experiments; monitoring models in production. Russell and P.

Machine Learning 98

Machine Learning Machine Learning AI AI 98

Towards AI

JUNE 12, 2024

RNNs and LSTMs came later in 2014. This focus on understanding context is similar to the way YData Fabric, a data quality platform designed for data […] There is very little contention that large language models have evolved very rapidly since 2018.

Natural Language Processing 100

Natural Language Processing Data Quality AI AI 100

AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

AWS 121

AWS ML ML AI 121

phData

FEBRUARY 2, 2024

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. This search for efficiency led us to create the Data Source tool, which is part of the phData Toolkit.

SQL 52

SQL Database Data Quality Data Engineer 52

Alation

SEPTEMBER 28, 2022

DataOps is a set of technologies, processes, and best practices that combine a process-focused perspective on data and the automation methods of the Agile software development methodology to improve speed and quality and foster a collaborative culture of rapid, continuous improvement in the data analytics field.

DataOps 52

DataOps Data Pipeline Data Quality Analytics 52