Alation

FEBRUARY 20, 2020

It’s no longer enough to build the data warehouse. Dave Wells, analyst with the Eckerson Group suggests that realizing the promise of the data warehouse requires a paradigm shift in the way we think about data along with a change in how we access and use it. Building the EDM.

Data Warehouse 52

Data Warehouse Data Lakes Hadoop Data Preparation 52

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 101

Tableau Data Quality Analytics Analytics 101

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

Precisely

JULY 18, 2024

Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods. It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud.

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

FEBRUARY 21, 2023

The data locations may come from the data warehouse or data lake with structured and unstructured data. The Data Scientist’s responsibility is to move the data to a data lake or warehouse for the different data mining processes.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

IBM Journey to AI blog

MARCH 14, 2024

. With Db2 Warehouse’s fully managed cloud deployment on AWS, enjoy no overhead, indexing, or tuning and automated maintenance. Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose.

AWS 93

AWS Database AI AI 93

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. Who needs data literacy training? Who can provide the training?

Data Governance 52

Data Governance Data Analysis Data Analysis Data Preparation 52

The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

SQL 52

SQL Database Data Scientist Python 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 Machine Learning Machine Learning 52

Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And we view Snowflake as a solid data foundation to enable mature data science machine learning practices.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And we view Snowflake as a solid data foundation to enable mature data science machine learning practices.

SQL 52

SQL ML ML Python 52

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