Data’s complicated issues haven’t suddenly vanished as systems, devices, and transactions generate more data. As a result, data management is more important than ever. This article will talk about:
Data Management Definition
Data management is the process of consuming, storing, organizing, and preserving data generated and gathered by an organization.
The data management process involves several activities that work together to ensure that data in business systems is correct, accessible, and available. Most of the work gets completed by IT and data management teams. Still, business users are usually involved in various phases to verify that the data fulfills their needs and ensure that they are on board with the regulations that govern its usage.
Historically, businesses have prioritized data performance, whether the frequency of updates or query execution time. When it comes to business data governance, though, receiving data as soon as possible isn’t exactly a top priority. You probably have a slew of additional concerns about your data:
What is Snowflake
Snowflake is a cloud data warehouse that provides the speed, concurrency, and ease of use required to store and analyze all of your company’s data in one place. It creates data storage repositories that you may use to store structured data for reporting and analysis.
Snowflake’s capacity to take mountains of unprocessed data in multiple forms from various sources (across the three large public data clouds) also makes it an appealing Data Lake option for many IT decision-makers. You can extend your data lake’s storage capacity dynamically without respect to compute nodes and resize your computing clusters elastically to meet needs only when they are needed, thanks to Snowflake’s flexibility to segregate storage from computing resources.
Data Management in Snowflake
While many data management policies necessitate human auditing and validation, Snowflake has features that decrease the number of controls necessary. Snowflake can help automate data governance policies in specific cases.
Let’s look at each data management concern we discussed earlier and how they get handled in Snowflake.
To learn more about cloud-data management, visit our comprehensive data management guide.
Your data must be accessible when your company requires it. AWS, Azure, and Google Cloud Platform offer Snowflake, and it inherits many of these cloud providers’ high-availability capabilities.
Access to data when you need it is another important feature of availability. Your information gets scattered across numerous databases or clusters in many older systems. You’ll need to establish a data pipeline to transport data from one system to another to aggregate that data.
It’s critical to have processes and procedures that guarantee your data is useful, recorded, labeled, and easily accessible to customers. You must implement controls for reliable, usable data during data intake. Data is provided in various forms by services, and your tools need to enforce the rules your company has placed in place.
Snowflake’s centralized data model also exhibits usability. While many businesses only have one Snowflake account, the platform enables numerous accounts to separate expenses and arrange data by business unit. A single data point may be referenced across many Snowflake accounts using Snowflake’s data sharing capability.
After getting ingested and saved, your data must be durable, accurate, complete, and consistent.
To guarantee that your business can rely on your data, you need to implement controls. Within Snowflake, there are two sorts of data integrity that an organization should be worried about: physical and logical.
The physical integrity of your data must remain intact. Ensure your data is reliably readable and savable without losing information.
If an availability zone or data center is lost, you should preserve the data in a replicated environment and when the availability zone or data center gets restored. As previously discussed, snowflake automatically duplicates your data across several availability zones to prevent data loss and ensure physical integrity.
In data management, logical integrity ensures that your data is logically correct and complete throughout your company and individual domains.
To guarantee that you have up-to-date data and that the data continues to adhere to the required types, values, and restrictions, you must establish and evolve controls inside your data ingestion and storage.
In every digital system, security should always be a top priority. Users should receive the minimum set of permissions necessary to do their job or task. You should also encrypt your data in transit and at rest to prevent attackers from gaining access to it through intercepted traffic or data breaches.
You should encrypt your data client-side before starting the ingestion process into Snowflake if you want to take data from outside of it. Snowflake encrypts this data before storing it if it hadn’t gotten encrypted before ingestion. This security protocol is the greatest practice since it assures the security of your data.
Always remember that your data is at rest more than it is in transit. So, you want to ensure that it remains encrypted throughout its life cycle.
When you submit a query to Snowflake, it checks to see if the current session’s role has permission to do the action in the question. Snowflake offers a variety of access control layers, each with its own set of applications.
Snowflake combines RBAC and DAC into a single security paradigm, giving you more flexibility depending on your use case. Every resource has an owner, and users can get allocated roles to interact with resources.
To learn more about Snowflake security, read our comprehensive Snowflake security guide.
Controls should constantly be in place to ensure that your data gives value to your business. Also, you should require that your data only gets accessed by authorized parties, which keeps it safe against data breaches and hackers. Yet, it also ensures your information is highly available. There is an immediate impact on your organization if sensitive data falls into the wrong hands or becomes inaccessible.
Snowflake comes with many built-in controls to help you avoid the bespoke procedures and technical expenditures that come with data warehouses. When building a data warehouse or pipeline, be sure to assess the essential data governance rules and re-evaluate and update them regularly.
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