A data scientist works with the meaning of data. He is responsible for the analysis, looks for dependencies, draws conclusions, and builds hypotheses. First of all, the work of a data scientist is to solve specific business problems in data analysis. Secondly, he must be able to find insights that the business has not even thought about yet. Let’s explain with an example.
Imagine that an e-commerce company needs to optimize the use of trucks. You need to predict the number of orders that will arrive in the next month and the dimensions of the boxes for these orders. The data scientist will do data analysis and forecasts.
He works with already processed and structured data that a data engineer has prepared for him. A data scientist may not even know where this data came from, what form it was originally, and what had to be done to process it and bring it to that form.
A data scientist works in systems that a data engineer has installed and configured: databases and data warehouses, tools for data processing, and training ML models. He doesn’t do all of this by hand like a data engineer. Machine learning and artificial intelligence come to his aid: he creates and trains models that help him in his work.
Large companies collect a huge amount of data measured in petabytes. Often local systems cannot handle this amount of data. First, they need to be stored somewhere, and companies can’t always allocate that much space. But the problem is not so much in data storage but in maintaining the infrastructure for storing it. Companies need to place their data centers somewhere, monitor their temperature, and maintain stability. And outdated equipment needs to be updated. All this is expensive and labor-intensive. Secondly, you need to engage in big data analytics. At the same time, different capacities are required for this in different periods. Clouds allow companies to scale computing power when the need arises and pay for actual consumption.
Analysts from Gartner have suggested that in 2022 cloud computing will become a mandatory part of 90% of new products and services in the field of big data. This approach is called Cloud First: when a company, first of all, seeks to create its infrastructure in the cloud. This makes it possible to quickly launch new projects, test hypotheses, and bring new products to market.
Clouds allow companies to outsource infrastructure issues. All the tasks of building and maintaining systems fall on the shoulders of the cloud provider. The business retains only the competence in business processes for processing data and getting benefits from it. Even if a company is not ready to transfer its data to a public cloud, it can rent capacity and deploy a private cloud. Contractors will maintain the equipment and all systems, and the data will not leave the company’s secure circuit.
Also Read: Quick Start: How Clouds Help Reduce Time-to-Market in IT Product Releases
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