Big Data Engineer Write For Us
Big Data Engineer is an information technology professional responsible for designing, structure, testing, and maintaining complex data meting out systems that work with large data sets. This type of data scientist aggregates cleanses, transforms, and enriches different data forms so intermediate data consumers such as business analysts and data scientists can extract insights systematically.
What is big data?
Big Data is a label that labels massive sizes of operational, product, and customer data, typically in the terabyte and petabyte range. Big data analytics can be used to advance key business and operational use cases, mitigate regulatory and compliance risks and create new streams of bottom-line revenue.
Data sources include:
- transactions with credit cards and points of sale;
- electronic commerce transactions;
- engagements in social networks;
- smartphone and mobile device commitments; Y
- Device readings generated by the Internet of Things.
The information that can be obtained through big data includes:
- optimize key business and operational use cases;
- mitigate regulatory and compliance risks;
- create new sources of net income; Y
- create engaging and differentiated experiences for customers.
What is the part of a big data engineer?
The role of a Big Data engineer is to create, maintain, and ensure a production-ready Big Data environment. The environment in which this role works will include architecture, technology standards, open source options, and data preparation and management processes. The role of the prominent data engineer is:
- Design, build and maintain large-scale data processing systems. This collects data from various data sources, structured or unstructured.
- Stock data in a data warehouse or data lake repository.
- Manage raw data using data processing algorithms and transformations to create predefined data structures. Put the results in a data warehouse or data lake for further processing.
- Transform and integrate diverse data into a scalable data repository (such as a data warehouse, data lake, cloud).
- Understand the different tools, techniques and algorithms for data transformation.
- Implement technical processes and business logic to alter collected data into meaningful and valuable information. This data must meet the necessary quality, governance, and compliance considerations to be considered reliable for operational and commercial use.
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