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Establishing Connections and Putting an End to Data Silos

Dataversity

They must connect not only systems, data, and applications to each other, but also to their […]. The post Establishing Connections and Putting an End to Data Silos appeared first on DATAVERSITY.

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Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Why Graph Databases Are an Essential Choice for Master Data Management

Dataversity

Within the Data Management industry, it’s becoming clear that the old model of rounding up massive amounts of data, dumping it into a data lake, and building an API to extract needed information isn’t working. Click to learn more about author Brian Platz.

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Why Easier Governance Is Superior Governance

Alation

Ventana found that the most time-consuming part of an organization’s analytic efforts is accessing and preparing data; this is the case for more than one-half (55%) of respondents. 1 Data catalogs can significantly reduce this burden by making it easier for analysts to find and access relevant information. Curious to learn more?

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

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. Data fabric and data mesh as concepts have overlaps.

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Snowflake for Commercial Banks, Everything You Need to Know

phData

With its ability to cater to a large variety of workloads, which include AI/ML , data warehousing, data lake , and data engineering , Snowflake also enables banks to go beyond personalization and tackle additional use cases such as financial forecasting, risk management, and more.

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Data architecture strategy for data quality

IBM Journey to AI blog

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.