Prateek Arora is an Author at InsideBitcoins. With expertise in the crypto niche, he specializes in breaking down complex topics related to Fintech, cryptocurrency, and decentralized finance for a broader audience. Prateek's work has been featured in BioRxiv, Cryptonews, Maxbit - Crypto News, BitcoinMax Official, DM2I, Datafloq, DZone.
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United Kingdom (National)
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Prateek Arora's articles predominantly focus on the cryptocurrency and blockchain industry, with a specific emphasis on trading software, legitimacy, and scams. He seems to be open to providing expert commentary on new developments and announcements in this sector.
Given the nature of his coverage, Prateek may be interested in pitches that offer insights into the latest trends or controversies related to cryptocurrency trading platforms or any regulatory changes impacting these platforms' operations. Additionally, he might be interested in hearing from experts who can provide valuable analysis regarding the legitimacy of various trading software within the crypto space.
It's important to note that there isn't a specified geographic focus for Prateek's work based on the provided article titles.
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6 min read. The rise of enterprise data lakes in the 2010s promised consolidated storage for any data at scale.
7 min read. When I first noticed it, it was just a simple spreadsheet. I was in a sales meeting, watching colleagues discussing a set of
7 min read. Infrastructure mapping across the UK, from highways and railways to utilities and public works, has long faced the challenge of incomplete, outdated or fragmented data.
7 min read. In today's high-velocity digital economy, I found that many sectors require automated decision loops measured in milliseconds
By: Prateek Arora
5 min read. As someone who has spent years guiding organisations through the evolution of business intelligence, I've witnessed firsthand
9 min read. In my experience working with National Health Service (NHS) data, one of the biggest challenges is balancing the enormous potential of NHS patient data with strict privacy constraints.
Metric mutation testing adapts software testing to analytics, helping teams catch flawed business logic before bad metrics and errors reach users.
A mutation testing pattern for analytics metrics that checks if validation catches realistic business logic errors early.
