Becoming a successful crypto trader requires a grouping of knowledge, strategy, discipline, and a willingness to adapt to a highly volatile market. Here's a comprehensive guide on how to embark on this journey: 1. Educate Yourself: Understand Blockchain and Cryptocurrency: Familiarize yourself with how blockchain works and the fundamentals of various cryptocurrencies. Market Analysis: Learn technical analysis (TA) and fundamental analysis (FA). Understand charts, indicators, market trends, and news affecting the crypto market. 2. Choose Your Trading Strategy: Day Trading vs. Long-Term Holding: Decide whether you want to be an active day trader, aiming for short-term gains, or a long-term investor. Risk Management: Develop a risk management strategy, including setting stop-loss orders and managing position sizes to protect your capital. 3. Select Reliable Exchanges and Wallets: Research Exchanges: Choose reputable exchanges with high liquidity and security measures...
Cloud vs. Edge - as previously posted on the IoT agenda
For many industrial automation professionals, the cloud has
become the backbone of IIoT. But for companies to truly move forward with their
IIoT vision, they must begin to understand that the cloud is only one part of
their IIoT universe. Operators looking for real-time computing are finding that
there are certain things that cannot or should not be moved to the cloud -
whether for security, latency, or cost reasons - and are therefore beginning to
move more and more computing to the cloud. the edge of your networks.
Advances in advanced computing have not only created more
data, but also increased the need for speed by making this information
available to other systems and analytics. Cloud computing is convenient, but
its connectivity is often not reliable enough for certain industrial
situations. Some computing will always need to run at the edge, such as real-time
processing, decision support, SCADA functions, and more. There is no point in
limiting these features when 100% cloud adoption is simply not required and can
be used for non-real-time workloads such as post-process analytics or
scheduling.
Cloud vs. Edge: a real world example
Consider an example from the energy industry that
demonstrates that the cloud vs. edge, each of which performs its most suitable
function. Companies can have hundreds of oil rigs scattered across a region,
and a company's headquarters are hundreds or even thousands of miles from a
data center or cloud. Systems must be in place at every oil platform or at
every boundary to continuously monitor and analyze key parameters such as well
pressure levels, with the ability to determine when critical limits may be
exceeded, allowing operators to take immediate remedial action. Expecting data
to go back to the datacenter, get analytics, and access the platform directly
can be an irrational risk.
In this case, the cloud is better suited to support planning
and trend detection, collecting metrics from all oil platforms and periodically
sending them to the data center or cloud where they can be aggregated and
analyzed.
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