Businesses generate large volume of data which is essential for organisations in decision making. In the long run they would have generated zeta bytes of data which might not have relevance in current business scenario, yet businesses might need access to this data as it can have record of key events with historical significance or can even help resolve issues. Data archiving can help businesses to preserve these data while minimising the storage cost.

Efficient Data Archiving

Dataplatr as a snowflake elite partner enables organizations to efficiently preserve and store the data without any data loss. We help our clients in assessing and identifying the data that is not relevant in the existing business environment and assists them in preparing the data for archiving by cleaning, transforming, and formatting the data into a standardized format  as required.  Also, we help them in migrating the data to the archived storage system.

Scalable and Secure Data Archiving

With Dataplatr as a Snowflake tech partner businesses can leverage the scalable storage architecture of snowflake to archive large volumes of data without worrying about capacity constraints. Organizations can utilize the snowflakes data compression capabilities to minimise the storage cost by compressing the data while ensuring security and ability to retrieve data as and when required.

Retrieval and Analysis of Archived Data

Our data management expertise ensures that the data is efficiently organized, formatted and governed, while our snowflake partnership ensures that data is secure in snowflakes cloud-based warehouse. When required businesses can make use of Snowflake query archive feature to retrieve the data and analyze it for getting a better understanding of data and derive insights.

Organizations by partnering with Dataplatr can benefit by archiving historical data that align with regulatory requirements for data retention, preserve knowledge and information for future use while ensuring minimum storage costs and improve the organizations data management efficiency.