Analysis Business Intelligence Compliance Data Engineering 
Xebia
JANUARY 19, 2023
The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. The authors state that the target audience is technical people and, second, business people who work with technical people. Nevertheless, I strongly agree.
Altexsoft
FEBRUARY 11, 2023
It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);
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Altexsoft
FEBRUARY 8, 2021
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.
Altexsoft
AUGUST 29, 2023
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.
Capgemini
NOVEMBER 14, 2022
Data has to be easy to find, understand, access, and use for everyone in the chain: data engineers, analysts, data scientists, and business users. It makes the data more accessible and understandable to everyone, especially less-skilled data consumers. The next wave of catalog and self service.
Lacework
NOVEMBER 1, 2023
In many cases we see that customers prefer to have their data stored and managed locally in their home region, both for reasons of regulatory compliance and also business preference. A mart is a group of aggregated tables (e.g.,
O'Reilly Media - Ideas
FEBRUARY 11, 2019
Temporal data and time-series analytics. Text and Language processing and analysis. Foundational data technologies. Machine learning and AI require data—specifically, labeled data for training models. Data Platforms. Data Integration and Data Pipelines. Automation in data science and big data.
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