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

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


The rise of the big data industry in recent years and data engineers is one of them. Data engineering organizes data users across the enterprise to transform the enterprise into better business insights and actions.Overall, the industry is moving toward data management environments that deliver insights from AI and machine learning while leveraging the cloud for agility. But how exactly does big data evolve into data engineering? The toughest challenge for AI and advanced analytics is not AI, it’s actually data management at scale. However, because the data size is too large, it is much larger than the traditional management technology, so other technologies have evolved. Such as Hadoop, MapReduce, Yarn, HDFS, can handle a large amount of data of various types and complexity. The adoption of cloud and advent of technologies such as Spark, serverless, and Kafka have all ushered in the era of big data engineering, effectively uncoupling storage and compute, enabling faster processing of multi-latency petabyte-scale data with auto-scaling and auto-tuning. So what role does the data engineer play? Data engineering, data engineers, data scientists, and data analysts are quintessential user personas of data engineering. For the business department, we must know how the data will help me predict what, and how it will help me understand the situation, or how to thank employees for having a lighter source of data. In addition, many data scientists spend more than half of their time collating data. How can they find suitable and trustworthy data, and how can they simplify the data and have a plan for the next step.
Because all industries and industries will face a large amount of network data, data engineering is one of the hottest jobs in the technology industry. The seven main functions of a data engineer are discovered the right dataset with an intelligent data catalog, bring the right data into your data lake or ML environment with mass ingestion, operationalize your data pipelines with enterprise-class data integration, process real-time data at scale with AI-powered stream processing, desensitize confidential information with intelligent data masking, ensure trusted data is available for insights with intelligent data quality at scale, and simplify data prep and enable collaboration with enterprise-class data preparation. Overall, many companies need to support the technology that led to the emergence of digital engineering: cloud, Spark, serverless, and Kafka, which makes this project very difficult.

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