Data Cleansing is the process of correcting and standardizing data to ensure datasets are accurate, complete, and formatted correctly. If data is inaccurate or corrupt, then workflows and algorithms become unreliable. Companies who are innovating with machine learning and artificial intelligence rely on clean data. According to current analyst projections, the volume of data is increasing from 79 zettabytes in 2021 to 181 zettabytes in 2025. Common challenges to ensure the data is clean and ready for use as soon as it enters the organization include:
- Corrupt data
- Inaccurate data
- Invalid data
- Data is in an inconvenient format
- Data is duplicated
InfinyOn Cloud facilitates data cleansing with a premiere feature called SmartModules that allows users to have full control over their streaming data by providing a programmable API for inline data manipulation. Filters, Maps, FilterMaps, ArrayMaps and Aggregate SmartModules are user-defined functions and offer flexibility for building and cleansing your data pipelines for any use-case.
- SmartModule filters are used to examine each record in a stream and decide whether to accept or reject it.
- SmartModule Maps are used to transform or edit each record in a stream.
- SmartModule FilterMaps are used to both transform and potentially filter records from a stream at the same time.
- SmartModule ArrayMaps are used to break apart Records into smaller pieces.
- SmartModule Aggregates are functions that define how to combine each record in a stream with some accumulated value.
Developers can use Fluvio open-source software that offers built-in packaging for multiple operating systems, from Raspberry PI to various Linux distributions. Support for the most common programming languages makes it easy to build custom connectors to virtually any server or data store for data cleansing.