Data engineers and organizations can promptly transit a great number of data to extract value from the data with the help of Snowflake Data Cloud. It enables groups to process and store the data in a simple way. We have already discussed why the use of Snowflake is increasing day by day and why many data engineers are adopting it, you can check snowflake data catalog here.
Although it’s convenient to make more value from data, it can also undermine the control of the organization over the metadata and increasingly challenging questions such as:
- Who’s the operator of each Database, Table, and Schema?
- Who can get access to what sort of data?
- Where, within our Petabytes of data, across hundreds or thousands of tables, do we have a particular type of data? For example, where do we store given sort of PII, PHI, or bank details?
- Can some people get unwanted access to sensitive data?
- When new data was entered into our Snowflake Data Cloud? Or what PHI, PII, or other types of sensitive data introduced last time?
Moreover, there are a lot of questions related to metadata that need to be answered. The usability of data can be improved by simple processes, its accuracy, and the freshness of meta. Reducing security threats and satisfying compliance needs can have a significant impact on metadata’s visibility which alternately enables you to identify vulnerabilities and address them.
You should keep a complete and precise record of stored data and maintain an up-to-date repository with an inventory of your data.
What is Data Inventory?
Data inventory is a bunch of all datasets collected by data engineers or an organization. It is also called central metadata collection. The data inventory consists of the locations and types of each dataset. The data engineers evaluate what kind of data is available and how they can access it. They also define access policies relevant to each set of given data.
Is it necessary to perform a data Inventory?
There are many motivates to accomplish and maintain a data inventory. But we discussed two basic grounds in this article. First, it is important to give a starting point to data consumers including data engineers and analysts for getting access to data and discovery. A data inventory provides a root for streamline and broad access to data, which stimulates a functional plan for the operations and use of data.
Turning to the second main reason. A data inventory is a core to compliance with data protection policies. Most policies, including GDPR, require organizations to know where their details are stored (not mentioned openly). Also, they need a defacto-data inventory.
The requirements of your data inventory depend on your data architecture and the way, you use it to collect metadata. It means what means you are using to collect metadata and what plans to do with it. After all, we suggest our autonomous data inventory to take or integrate with a data catalog platform. Because keeping a manual inventory for your data is generally costly.