What are the best data engineering practices for cryptocurrency analysis?

3 answers

• Feb 18, 2022 · 3 years ago
  One of the best data engineering practices for cryptocurrency analysis is to ensure data quality. This involves cleaning and preprocessing the data to remove any inconsistencies or errors. Additionally, it's important to use reliable data sources and validate the data to ensure accuracy. Data normalization and standardization can also be beneficial for making meaningful comparisons and identifying patterns in the cryptocurrency data.
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• Feb 18, 2022 · 3 years ago
  When it comes to data engineering practices for cryptocurrency analysis, automation is key. By automating data collection, cleaning, and analysis processes, you can save time and reduce the risk of human errors. Utilizing APIs and web scraping tools can help automate data retrieval, while tools like Python and SQL can be used for data cleaning and analysis. Implementing automated workflows and scheduling regular updates can ensure that your cryptocurrency analysis is always up-to-date.
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• Feb 18, 2022 · 3 years ago
  At BYDFi, we recommend leveraging cloud computing for cryptocurrency data engineering. Cloud platforms like AWS and Google Cloud provide scalable and cost-effective solutions for storing and processing large volumes of cryptocurrency data. By utilizing cloud-based data warehouses and analytics tools, you can easily scale your infrastructure based on your analysis needs. Additionally, cloud platforms offer built-in security measures to protect your data and ensure compliance with industry regulations.
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