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How to Address Big Data Challenges (PART 2 of 6)

Latest reply: Feb 1, 2022 10:26:57 1258 76 51 0 0

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Continution to previous post - How to Address Big Data Challenges (PART 1 of 6)



Managing the complexity of data integration and preparation


Big data platforms address the challenges of gathering and storing massive amounts of data of various types, as well as the rapid retrieval of data required for analytics. The data collection process, however, can be difficult.


Constant updating is required to maintain the integrity of an organization's acquired data repositories. This necessitates access to a wide range of data sources as well as specialized big data integration solutions.


Some businesses utilize a data lake as a catch-all repository for large amounts of big data gathered from many sources without considering how the different data would be combined. Various business domains, for example, generate data that is useful for joint analysis, but this data typically has ambiguous underlying semantics that must be resolved.

 

Ad hoc project integration is discouraged since it can result in a lot of rework. It's often advisable to adopt a strategic strategy to data integration for the best ROI on big data projects.

 


Effortlessly and economically scaling huge data systems


Businesses might waste a lot of money holding huge data if they don't plan how to use it. Big data analytics begins with data ingestion, which organizations must comprehend. Curating organizational data repositories also necessitates consistent retention strategies to cycle out old data, which is especially important since as data from before the COVID-19 epidemic is typically inaccurate in today's market.


As a result, before adopting big data systems, data management teams should map out the types, formats, and uses of data. However, it is easier said than done.


Frequently, you start with one data model and expand out, only to discover that the model no longer fits your new data points, resulting in technical debt that must be resolved.


A generic data lake with the right data structure can let you reuse data more efficiently and cheaply. Within a data lake, Parquet files, for example, frequently offer a superior performance-to-cost ratio than CSV dumps.

 


To be continued... How to Address Big Data Challenges (PART 3 of 6)

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Herediano
Created Jan 17, 2022 22:03:29

Great, thank you for sharing!
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user_4358465
user_4358465 Created Jan 24, 2022 15:31:16 (0) (0)
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lucian2003
Created Jan 18, 2022 01:19:13

How to Address Big Data Challenges (PART 2 of 6)-4608427-1
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user_4358465 Created Jan 24, 2022 15:31:22 (0) (0)
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Unicef
MVE Created Jan 18, 2022 01:43:31

WELL DONE FRIEND
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user_4358465 Created Jan 24, 2022 15:31:28 (0) (0)
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zaheernew
MVE Author Created Jan 18, 2022 05:48:07

useful info
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user_4358465 Created Jan 24, 2022 15:31:33 (0) (0)
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faysalji
Moderator Author Created Jan 18, 2022 11:27:34

Good share, thanks
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user_4358465 Created Jan 24, 2022 15:31:39 (0) (0)
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Serges_armel
Created Jan 18, 2022 20:27:24

Good to know
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user_4358465 Created Jan 24, 2022 15:31:45 (0) (0)
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Lucfabrice
MVE Author Created Jan 19, 2022 05:44:51

Great content !!!
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AbhishekBOSE
Created Jan 21, 2022 20:56:17

Excellent share
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Sara_Obaid
Created Jan 21, 2022 21:16:05

Very interesting, thanks for sharing!!
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