Comment: The Rise of Big Data Engineering in 2020: Cloud, AI & Analytics Success

Gartner defines data engineering: “Data engineering is the practice of making the appropriate data accessible and available to various data consumers. It is a discipline that involves collaboration across business and IT.”

n the year 2020, big data isn’t a new trend that leads the world anymore, but it still deeply affects how people think and act, not to mention that the enormous impact it caused on almost every industry. As organizations hope to modernize their analytical environments, data engineering is on the rise. Here is how we got here and what you need to know about data engineering.

The role of data engineering users is the challenge of AI analysis
While cloud, Spark, serverless, and Kafka are essential technologies of data engineering, data scientists and data analysts are typical user roles for data engineering.

Why data engineering is critical to the success of AI and analytics

According to Databricks research, many companies have little success in AI projects due to insufficient data. Despite a large amount of money invested in data and analysis plans, there are still many companies that cannot succeed and think it is difficult to put into production. Another CrowdFlower report pointed out that if a piece of data can be used for analysis and modeling, data users need to spend 80% of the time to prepare the data. The common theme of all these is to have good clean data, and companies can trust their AI and analysis projects, and this is exactly the impact of end-to-end data engineering.

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