phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Data Acquisition: Extracting data from source systems and making it accessible. as well as calculating business keys.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

IBM Journey to AI blog

JULY 3, 2024

This includes integration with your data warehouse engines, which now must balance real-time data processing and decision-making with cost-effective object storage, open source technologies and a shared metadata layer to share data seamlessly with your data lakehouse.

AI 45

AI AI Data Quality Database 45

Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

JULY 8, 2024

NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data. Data Warehouses : Centralised repositories optimised for analytics and reporting. Data Lakes : Scalable storage for raw and processed data, supporting diverse data types.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

Alation

FEBRUARY 8, 2022

Signals around the quality and integrity of the data are essential if people are to understand and trust it. Data provenance and lineage, for example, clarify an asset’s origin and past usages, important details for a newcomer to understand and trust that asset. Self-describing. Plane 3: Mesh Supervision Plane.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

Precisely

OCTOBER 16, 2023

The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and data warehouses have been replaced by modern data lakes and cloud analytics solutions.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72