IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Reduce data duplication and fragmentation.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

phData

JANUARY 10, 2024

This tool provides functionality in a number of different ways based on its metadata and profiling capabilities. Imagine you wanted to build a dbt project for your existing source data warehouse in your migration to Snowflake. While this may seem like a trivial thing in concept, it’s actually incredibly powerful.

Data Warehouse 52

Data Warehouse Data Profiling Data Pipeline Database 52

The MLOps Blog

MAY 17, 2023

Focus Area ETL helps to transform the raw data into a structured format that can be easily available for data scientists to create models and interpret for any data-driven decision. A data pipeline is created with the focus of transferring data from a variety of sources into a data warehouse.

ETL 59

ETL Data Pipeline ML ML 59

Alation

SEPTEMBER 7, 2021

With Snowflake, data stewards have a choice to leverage Snowflake’s governance policies. First, stewards are dependent on data warehouse admins to provide information and to create and edit enforcement policies in Snowflake. Alation’s deep data profiling helps data scientists and analysts get important data profiling insights.

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

Alation

DECEMBER 21, 2022

MDM is a discipline that helps organize critical information to avoid duplication, inconsistency, and other data quality issues. Transactional systems and data warehouses can then use the golden records as the entity’s most current, trusted representation. Data Catalog and Master Data Management.

Data Quality 52

Data Quality Data Warehouse Data Profiling Data Governance 52

DagsHub

AUGUST 23, 2024

A data quality standard might specify that when storing client information, we must always include email addresses and phone numbers as part of the contact details. If any of these is missing, the client data is considered incomplete. Data Profiling Data profiling involves analyzing and summarizing data (e.g.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

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

Alation

JANUARY 13, 2022

Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. Let’s turn our attention now to data mesh.

Data Lakes 59

Data Lakes Data Governance Data Quality Data Warehouse 59

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Credits can be purchased for 14 cents per minute.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Alation

AUGUST 26, 2021

Data intelligence has emerged as the solution to the garbage-in, garbage out problem that’s long stymied AI and BI efforts. Data intelligence is an amalgamation of categories, which include: Metadata management. Data quality. Data governance. Master data management. Data profiling. Data curation.

Data Governance 64

Data Governance ML ML Artificial Intelligence 64