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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Altexsoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

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Supply Chain Analytics: Opportunities in Data Analysis and Business Intelligence

Altexsoft

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

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Apiumhub among top IT industry leaders in Code Europe event

Apiumhub

An International speaker, books & video author, and writer for Java Magazine, IBM Developer, Oracle, and InfoQ. Mr. Hypponen sits on the advisory boards of t2 and Social Safeguard. Also, he serves as the Program Director for Data science/Data Engineering Educational Program at Skillbox. Dave Aronson – T.

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Doing Business in a Big Data World

Strategy Driven

Big Data involves not just the structured data (customer name and details, products purchased, how much was spent and when, etc.) that every company is used to capturing, but also unstructured data (data scraped from the Internet and social media channels that may come in a wide variety of formats, from video to voice).

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Data Collection for Machine Learning: Steps, Methods, and Best Practices

Altexsoft

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. For this task, you need a dedicated specialist — a data engineer or ETL developer.

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Data Lakehouse: Concept, Key Features, and Architecture Layers

Altexsoft

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

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Procurement Analytics: Challenges, Opportunities, and Implementation Approaches

Altexsoft

Check a brief video explaining how demand forecasting works. Monitoring your partners’ performance will help you choose the right supplier, reduce overspending by securing best prices, mitigate quality and delivery risks, and establish strong business relationships. Extract data. Consolidate data.