Data Science Data Warehouse Online Analytical Processing 
Smart Data Collective
JUNE 16, 2022
Data science is both a rewarding and challenging profession. One study found that 44% of companies that hire data scientists say the departments are seriously understaffed. Fortunately, data scientists can make due with fewer staff if they use their resources more efficiently, which involves leveraging the right tools.
Analytics Vidhya
JULY 1, 2022
This article was published as a part of the Data Science Blogathon. Introduction In the field of Data Science main types of online processing systems are Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP), which are used in most companies for transaction-oriented applications and analytical work.
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Sisense
SEPTEMBER 3, 2019
This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing).
CIO Business Intelligence
JANUARY 20, 2023
Improved employee satisfaction: Providing business users access to data without having to contact analysts or IT can reduce friction, increase productivity, and facilitate faster results. Increased competitive advantage: A sound BI strategy can help businesses monitor their changing market and anticipate customer needs.
AWS Big Data
MARCH 7, 2023
Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.
IBM Big Data Hub
SEPTEMBER 25, 2023
To address their performance needs, Uber chose Presto because of its ability, as a distributed platform, to scale in linear fashion and because of its commitment to ANSI-SQL, the lingua franca of analytical processing. Uber chose Presto for the flexibility it provides with compute separated from data storage.
Jet Global
AUGUST 14, 2019
Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.
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