Data virtualization, when combined with Lyftrondata, lets you access your data in an easy way. You can use Amazon Redshift as a source for cached views. Data virtualization might otherwise seem superfluous when used with Amazon Redshift, but if you consider the whole data architecture responsible for data storage, processing, and analytics, you will clearly understand how well the Lyftrondata data virtualization platform and Amazon Redshift augment each other to enable a flexible, scalable data architecture.
Lyftrondata data virtualization integrates many kinds of data and allows it to be queried as one database. Out of that database, one can query data sources like Amazon Redshift. Lyftrondata Data Virtualization also lets you access data from many different sources, including spreadsheets, SQL databases, and even other services. This technology essentially helps with the inherent heterogeneity of current data processing systems.
Stakeholders can consolidate security across disparate data sources. Lyftrondata Data virtualization eliminates the need for different security specifications for various data sources by handling all security specifications in one uniform way.
Moreover, Data Virtualization hides the SQL dialect of each data source in use. It delivers database server independence and defines all integrations, aggregations, filtering, and transformation specifications using views. Lyftrondata Data Virtualization offers consumers API or language-neutral access to data stored in Amazon Redshift.
Lyftrondata’s data virtualization solution allows users to execute complex queries, including distributed joins, without having to get the data into a central system. By defining views and their associated columns, users can search metadata and discover which views depend upon which sources.
Moreover, lineage can be used to know what adjustments have been established overtime on the particular data. Thus, data lineage can also be utilized to trace the source of the information used for a view, and this will also give insight into how changes could impact a different view. This can be done through impact analysis and people can understand the consequences of modifications before they are actually implemented.