Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

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. For example, neptune.ai Check out the Kubeflow documentation.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. But governance is a time-consuming process (for users and data stewards alike).

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

Dataconomy

JULY 28, 2023

Data preprocessing ensures the removal of incorrect, incomplete, and inaccurate data from datasets, leading to the creation of accurate and useful datasets for analysis ( Image Credit ) Data completeness One of the primary requirements for data preprocessing is ensuring that the dataset is complete, with minimal missing values.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Alation

DECEMBER 7, 2021

As a platform for data intelligence , Alation boasts open APIs with which Bigeye can easily integrate. This integration empowers all data consumers, from business users, to stewards, analysts, and data scientists, to access trustworthy and reliable data.

Data Quality 52

Data Quality Data Pipeline Data Observability Data Profiling 52

Alation

MAY 24, 2022

Prime examples of this in the data catalog include: Trust Flags — Allow the data community to endorse, warn, and deprecate data to signal whether data can or can’t be used. 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

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. This allows data scientists to keep their focus on the creation of models or their continuous improvement.

ETL 59

ETL Data Pipeline ML ML 59