One niche field in the computer industry that has the potential to blow up in the near future is quantum computing. While quantum computing is not a new concept, it is still in its early stages of development and has not yet reached its full potential. Quantum computing utilizes the principles of quantum mechanics to perform complex computations at a much faster rate than traditional computers. It has the potential to revolutionize various industries, including cryptography, drug discovery, optimization problems, and machine learning. One example of the potential impact of quantum computing is in the field of cryptography. Quantum computers have the ability to break many of the encryption algorithms that are currently used to secure sensitive data. This has led to a growing interest in developing quantum-resistant encryption methods to protect against future attacks. Companies and organizations are investing in research and development to prepare for the post-quantum era. Another example is in the field of drug discovery. Quantum computing can significantly accelerate the process of simulating and analyzing molecular interactions, leading to the development of new drugs and treatments. By harnessing the power of quantum computing, researchers can explore a much larger chemical space and identify potential drug candidates more efficiently. Furthermore, quantum computing can greatly enhance optimization problems, which are prevalent in various industries such as logistics, finance, and supply chain management. Quantum algorithms can provide faster and more accurate solutions to complex optimization problems, leading to improved efficiency and cost savings. While quantum computing is still in its infancy, there has been significant progress in recent years. Companies like IBM, Google, and Microsoft are investing heavily in quantum research and development, and they have already made breakthroughs in terms of qubit stability and error correction. Additionally, startups like D-Wave Systems are already offering quantum computers for commercial use. However, it is important to note that quantum computing still faces several challenges, including the need for stable and scalable qubits, error correction, and the development of quantum algorithms for practical applications. These challenges need to be addressed before quantum computing can become mainstream. In conclusion, quantum computing is a niche field in the computer industry that has the potential to blow up in the near future. Its ability to solve complex problems at an unprecedented speed and its potential impact on various industries make it an exciting area of research and development. While there are still challenges to overcome, the progress made so far and the investments being made by major companies indicate that quantum computing is on the cusp of a significant breakthrough.
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User Comments
Adan Larsen
a year ago
Big Data has little to do with the actual size of data. Rather it actually means finding value in data where previously no value could be found. Previously when specialized hardware was needed to store huge amounts of data, now commodity hardware can be used to build distributed stores. This allows companies to build data stores with no upper bounds.
Jamison Davenport
a year ago
Right now Data Science is growing by leaps and bounds. Previously storing data was costly. Slowly the cost of storing data became cheaper and cheaper so people started storing all kinds of data. This gave rise to Big Data.
Alexis Massey
a year ago
Explain how engineering cost decisions impact product outcomes. Help product teams make intelligent tradeoffs Ex: Reserved Instances are cheap, but using them limits the product’s ability to burst spend upward to meet demand (they are only cost effective if load is steady). Ex: Being able to fail over to a different region in a catastrophic downtime event in 10 seconds, 1 minute, 1 hour, or 1 day each require different amounts of investment. Figure out what level of resiliency is an actual product requirement.
Roberto Huff
a year ago
These engineers can embed on engineering teams within an organization and: Identify applications which can safely run on Spot Instances Calculate how many Reserved Instances should be purchased across an organization, for what reservation length. Redesign a distributed application to minimize cross-AZ traffic Prune S3 data, and move rarely-used data to cold storage. Use high-compression formats on the coldest data. Choose the right disk type for a given application. Evaluate whether an application needs a persistent volume (eg, Persistent Disk), transient instance storage (LocalSSD), or object storage (GCS). Evaluate a big data stack holistically to choose the tool with the right cost structure. Choose between Dataproc and Dataflow. DynamoDB vs Aerospike. Kafka vs Kinesis. Talk in language finance can understand, translating between engineering’s cost decisions and product requirements.
Drake Cameron
a year ago
Big data and big enterprises are moving to the cloud, and spending goes through the roof when developers naively translate on-premise infrastructure into AWS, GCP, or Azure infrastructure. There will be huge demand for engineers who identify and execute on cost-saving measures.