Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Advanced Analytics is a data analysis method that aims to gain in-depth insights using big data and advanced technologies to improve organizations' strategic decision-making processes.
Micro-service architecture is a software architecture approach based on the development and deployment of large and complex applications as small, independent and specialized services.
Product Analytics is a data-driven process used to understand how a product is interacting with users, measuring and improving its performance.
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We were able to increase the data processing speed by 13 times on average and 30 times at maximum with this project.
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