By combining Lyftrondata Data Virtualization engine with Snowflake’s robust framework, users are empowered to integrate data from disparate sources, provide greater flexibility in data access, limit data silos, and automate query execution for faster time-to-insight. Data Virtualization for Snowflake with a Powerful Combination of Lyftrondata allows you to transform data on the industry’s leading cloud data warehouse with complement processes like data preparation, data quality management, and data integration.
With Lyftrondata’s ultimate data virtualization architecture, Snowflake users could perform data replication and federation in a real-time format, allowing for greater speed, agility, and response time. Lyftrondata enables effective data mining, predictive analytics, machine learning, and artificial intelligence. With Lyftrondata, encapsulate critical information from the outside world and ensure users cannot change the data intentionally. It is faster and cheaper to maintain data than it is to replicate and spend resources transforming it into different formats and locations making Lyftrondata a cost-effective option.
This blog further explains the advantages of using Lyftrondata for data visualization on Snowflake.
For operational effectiveness, a profoundly virtualized model diminishes time to change raw data to report-prepared information because information isn’t being moved during this handling.
While implementing data virtualization in Snowflake, you may not necessarily obtain resiliency if you use views to virtualize data as it moves between different zones.
Data Virtualization with Lyftrondata a game changer and revenue generator
Data Virtualization for Snowflake with a Powerful Combination of Lyftrondata
Data Virtualization when combined with Lyftrondata serves the advantage of streamlined data access of virtualization along with scalability, speed, and flexibility of Snowflake. Snowflake can also be used as a source for cached views. Data virtualization might otherwise seem superfluous when used with Snowflake, 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 Snowflake augment each other to enable a flexible, scalable data architecture.
One interface for all the data simplifies analytics and dashboard, reports making. Lyftrondata Data Virtualization helps in making a heterogeneous set of data sources and enables them to look like one logical database, out of which one of the data sources can be Snowflake.
Lyftrondata Data virtualization can also access data from various sources, including many file formats, service busses, SQL databases, spreadsheets, and applications. This refined technology was essentially developed to address the inherent heterogeneity of all the current data processing systems.
Stakeholders can centrally manage security across disparate sources. Lyftrondata Data virtualization eliminates the need of having to define different security specifications for various data sources with varied specification languages. Lyftrondata helps handle all security specifications in one uniform way.
Moreover, Data Virtualization defines all integrations, aggregations, filtering, and transformation specifications using views. Lyftrondata Data Virtualization delivers database server independency and hides the SQL dialect of the data source in use. All the data that is stored in Snowflake can be accessed through SQL. Consumers can deploy other APIs or languages depending on the requirements of the data customer.
Views that join multiple sources are processed more efficiently with Lyftrondata data virtualization. The efficient query optimizer runs distributed joins. It also allows metadata definition. Views and their associated columns can be defined, described, and tagged. Business users and professional IT developers can search this metadata. One can also learn which views are dependent on which sources.
Also, lineage can be leveraged to know what type of operations have been applied over time to the particular data and also trace the source of the data used. Thus, you can also determine the impact on other views when the definition of a view is modified. This can be done through impact analysis and helps in understanding the impact of changes beforehand.
Conclusion
Lyftrondata data virtualization platform empowers Snowflake users to integrate data from disparate sources, provides greater flexibility in data access, limits data silos, and automates query execution for faster time-to-insight.
Book a demo to explore how Lyftrondata’s ultimate data virtualization architecture combined with Snowflake’s scalability, could help you drive a single source of truth from your data.
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