Knowledge Hut
MAY 2, 2024
To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly. Also, Spark and MapReduce do complement each other on many occasions.
Knowledge Hut
NOVEMBER 28, 2023
The data goes through various stages, such as cleansing, processing, warehousing, and some other processes, before the data scientists start analyzing the data they have garnered. The data analysis stage is important as the data scientists extract value and knowledge from the processed, structured data.
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Ascend.io
JULY 17, 2023
If you work with data, you’ll have come across the term data mesh by now. This decentralized but interconnected approach to structuring data has become increasingly popular since the term was coined by Zhamak Dehghani 4 years ago.
Knowledge Hut
JANUARY 18, 2024
The field of Artificial Intelligence has seen a massive increase in its applications over the past decade, bringing about a huge impact in many fields such as Pharmaceutical, Retail, Telecommunication, energy, etc.
AltexSoft
DECEMBER 15, 2021
Telecommunications: predicting equipment failure. Standing for Mobile Broadband Network LTD, MBNL is a leading provider of telecommunication services, jointly owned by two British most innovative mobile operators. Why and when do you critically need data scientists? The company takes care of 22,000 network towers across the UK.
Rockset
FEBRUARY 24, 2023
Lastly, and perhaps most importantly, streaming data is unique because it’s high-velocity and high volume, with an expectation that the data is available to be used in the database very quickly after the event has occurred. Streaming data has been around for decades. Today, streaming data is everywhere.
AltexSoft
AUGUST 4, 2021
A data warehouse (DW) is a data repository that allows for storing and managing all the historical enterprise data, coming from disparate internal and external sources like CRMs, ERPs, flat files, etc. Initially, DWs dealt with structured data presented in tabular forms. Independent data marts.
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