Cloudera
JULY 26, 2021
From my discussions with Cloudera clients, data product development has been on top of the growth agenda in many industries such as Financial Services, Healthcare and Telecommunications. a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g.,
Cloudera
SEPTEMBER 15, 2022
Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.
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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.
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.
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.
Precisely
OCTOBER 5, 2023
Determine what data you’ll need Once you’ve determined the use case, brainstorm and dig deeper into what your end goals are and what you need to know to get there. For example, will you need structured data, unstructured, or a combination?
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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