Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Data integration.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Pickl AI

FEBRUARY 21, 2023

Understanding the appropriate ways to use data remains critical to success in finance, education and commerce. Accordingly, data collection from numerous sources is essential before data analysis and interpretation. The data locations may come from the data warehouse or data lake with structured and unstructured data.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

Alation

SEPTEMBER 23, 2021

Data Catalogs for Data Science & Engineering – Data catalogs that are primarily used for data science and engineering are typically used by very experienced data practitioners. It also catalogs datasets and operations that includes data preparation features and functions.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Alation

FEBRUARY 13, 2020

Staff are encouraged and incentivized to access and analyze data and to share their knowledge about working with data and share the insights that they derive from data. Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data.

Data Governance 52

Data Governance Data Analysis Data Analysis Data Preparation 52

Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

OCTOBER 10, 2024

Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. Common use cases include integrating data across hybrid cloud environments, managing data lakes, and enabling real-time analytics for Business Intelligence platforms.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet.

SQL 52

SQL Database Data Scientist Python 52

The MLOps Blog

MAY 9, 2023

The pipelines are interoperable to build a working system: Data (input) pipeline (data acquisition and feature management steps) This pipeline transports raw data from one location to another. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

ML 98

ML ML Machine Learning Machine Learning 98

Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. We recently announced an integration with Trifacta to seamlessly integrate the Alation Data Catalog with self-service data prep applications to help you solve this issue.

Data Lakes 40

Data Lakes ETL Data Analyst Data Preparation 40

Dataconomy

FEBRUARY 26, 2025

KDD provides a structured framework to convert raw data into actionable knowledge. The KDD process Data gathering Data preparation Data mining Data analysis and interpretation Data mining process components Understanding the components of the data mining process is essential for effective implementation.

Data Mining 91

Data Mining Data Mining Data Mining Data Preparation 91