Data management as a service is a type of cloud service that provides enterprises with centralized storage for disparate data sources. The label “as a service” references a pay-per-use business model that does not require the customer to purchase or manage infrastructure for data management.
Database migration is the process of migrating data from one or more source databases to one or more target databases by using a database migration service. When a migration is finished, the dataset in the source databases resides fully, though possibly restructured, in the target databases.
Our data experts, design the research plans used in data gathering and analysis. They participate in interpreting data analysis and developing action plans accordingly and assist in making strategic data-related decisions by analyzing, manipulating, tracking, internally managing, and reporting data.
Data storage essentially means that files and documents are recorded digitally and saved in a storage system for future use. Storage systems may rely on electromagnetic, optical or other media to preserve and restore the data if needed. Data storage makes it easy to back up files for safekeeping and quick recovery in the event of an unexpected computing crash or cyberattack.
Data storage can occur on physical hard drives, disk drives, USB drives or virtually in the cloud. Some of the most important factors to consider in terms of data storage are reliability, how robust the security features tend to be and the cost to implement and maintain the infrastructure.
Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization. The destination is typically a data warehouse, data mart, database, or a document store. Sources may be almost anything — including SaaS data, in-house apps, databases, spreadsheets, or even information scraped from the internet.
The data ingestion layer is the backbone of any analytics architecture. Downstream reporting and analytics systems rely on consistent and accessible data. There are different ways of ingesting data, and the design of a particular data ingestion layer can be based on various models or architectures.
It ensures that datasets are complete, well-described, and in a format and structure that best facilitates long-term access, discovery, and reuse.
