phData

NOVEMBER 9, 2023

The primary objective of this idea is to democratize data and make it transparent by breaking down data silos that cause friction when solving business problems. What Components Make up the Snowflake Data Cloud? What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. A data fabric is comprised of a network of data nodes (e.g.,

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata.

AI 45

AI AI Data Quality Database 45

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

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

phData

SEPTEMBER 20, 2023

A data mesh is a decentralized approach to data architecture that’s been gaining traction as a solution to the challenges posed by large and complex data ecosystems. It’s all about breaking down data silos, empowering domain teams to take ownership of their data, and fostering a culture of data collaboration.

Data Silos 52

Data Silos Database Data Quality Data Engineering 52

Pickl AI

JUNE 28, 2023

Understanding Data Integration in Data Mining Data integration is the process of combining data from different sources. Thus creating a consolidated view of the data while eliminating data silos. It involves mapping and transforming data elements to align with a unified schema.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

phData

MAY 13, 2024

Even if organizations survive a migration to S/4 and HANA cloud, licensing and performance constraints make it difficult to perform advanced analytics on this data within the SAP environment. Additionally, change data markers are not available for many of these tables.

Database 52

Database Analytics Analytics Machine Learning 52

Alation

APRIL 19, 2022

Data producers and consumers alike are working from home and hybrid locations more often. And in an increasingly remote workforce, people need to access data systems easily to do their jobs. This might mean that they’re accessing a database from a smartphone, computer, or tablet. Today, data dwells everywhere.

Data Governance 52

Data Governance Cloud Data Database Data Silos 52

IBM Journey to AI blog

FEBRUARY 2, 2023

The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery. In 2021, cloud databases accounted for 85% 1 of the market growth in databases.

Data Warehouse 58

Data Warehouse Cloud Data Analytics Analytics 58

Pickl AI

OCTOBER 6, 2024

By employing ETL, businesses ensure that their data is reliable, accurate, and ready for analysis. This process is essential in environments where data originates from various systems, such as databases , applications, and web services. The key is to ensure that all relevant data is captured for further processing.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

phData

APRIL 2, 2024

By leveraging cloud-based data platforms such as Snowflake Data Cloud , these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products.

ML 52

ML ML Data Silos Data Lakes 52

Alation

AUGUST 2, 2022

The problem many companies face is that each department has its own data, technologies, and information handling processes. This causes data silos to form, which can inhibit data visibility and collaboration, and lead to integrity issues that make it harder to share and use data.

Data Silos 52

Data Silos Data Lakes Data Analyst Data Scientist 52

The MLOps Blog

MAY 17, 2023

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

ETL 59

ETL Data Pipeline ML ML 59

AWS Machine Learning Blog

NOVEMBER 1, 2024

According to sources from government databases and research institutions, there are around 300,000–600,000 clinical trials conducted globally each year, amplifying this impact by several hundred thousand times. Decentralized clinical trials, however, often employ a singular data lake for all of an organization’s clinical trials.

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AWS Data Lakes Machine Learning Machine Learning 117

Flipboard

DECEMBER 4, 2024

The use of separate data warehouses and lakes has created data silos, leading to problems such as lack of interoperability, duplicate governance efforts, complex architectures, and slower time to value. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

Data Lakes 139

Data Lakes Data Warehouse AWS Database 139

phData

SEPTEMBER 27, 2024

. “ This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’? Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52