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Data Security in Snowflake: Encryption, Data Masking, and Role-Based Access Controls 

With the increasing volume of sensitive data stored in cloud environments, data security is a top priority for organizations. Snowflake, a leading cloud-based data platform, provides robust security mechanisms to ensure data confidentiality, integrity, and access control.

This blog explores three essential security features in Snowflake:

✅ Encryption — Protecting data at rest and in transit. ✅ Data Masking — Controlling visibility of sensitive data. ✅ Role-Based Access Control (RBAC) — Managing user permissions securely.

1. Encryption in Snowflake

How Snowflake Secures Data with Encryption

Snowflake encrypts all data by default, ensuring that unauthorized users cannot access it. Encryption is applied at two levels:

🔹 Encryption at Rest — Data is encrypted using AES-256 before being stored. 🔹 Encryption in Transit — Data moving between clients and Snowflake is protected using TLS 1.2 or higher.

Key Features of Snowflake Encryption:

✅ End-to-End Encryption — Ensures data remains protected from ingestion to storage. ✅ Automatic Key Management — Snowflake manages encryption keys internally without user intervention. ✅ Bring Your Own Key (BYOK) — Enterprises can use customer-managed encryption keys for added control.

How to Check Encryption Status in Snowflake:

To verify if encryption is enabled for your Snowflake account, use:sqlSHOW PARAMETERS LIKE 'ENCRYPTION%' IN ACCOUNT;

2. Data Masking in Snowflake

What is Data Masking?

Data masking allows organizations to control visibility of sensitive data based on user roles. It ensures that only authorized users can see actual data, while others see obfuscated values.

Types of Data Masking in Snowflake:

🔹 Dynamic Masking — Data is masked at query time based on user roles. 🔹 Static Masking — Data is permanently altered before being written to storage.

Creating a Masking Policy in Snowflake

A masking policy defines how data should be masked when accessed by unauthorized users.

Example: Masking Credit Card Numbers

sqlCREATE MASKING POLICY mask_credit_card AS (val STRING) RETURNS STRING -> CASE WHEN CURRENT_ROLE() IN ('FINANCE_ADMIN') THEN val ELSE 'XXXX-XXXX-XXXX-XXXX' END;

Applying the Masking Policy to a Column:

sqlALTER TABLE customers MODIFY COLUMN credit_card SET MASKING POLICY mask_credit_card;

Now, only users with the FINANCE_ADMIN role can see full credit card numbers. Others will see masked values.

3. Role-Based Access Control (RBAC) in Snowflake

What is Role-Based Access Control?

RBAC ensures that users can only access data and operations based on their roles. It helps organizations implement the principle of least privilege (PoLP), minimizing security risks.

Key Concepts in Snowflake RBAC:

🔹 Users — Individuals accessing Snowflake. 🔹 Roles — Define what actions users can perform. 🔹 Privileges — Specific permissions granted to roles. 🔹 Schemas & Tables — The objects being secured.

Setting Up Role-Based Access Control in Snowflake

1️⃣ Create a Custom Role:

sqlCREATE ROLE data_analyst;

2️⃣ Assign Privileges to the Role:

sqlGRANT SELECT ON DATABASE sales_db TO ROLE data_analyst;

3️⃣ Assign the Role to a User:

sqlGRANT ROLE data_analyst TO USER john_doe;

Now, john_doe can only query sales_db but cannot modify or delete data.

RBAC Best Practices in Snowflake:

✅ Follow the Least Privilege Principle — Only grant necessary permissions. ✅ Use Custom Roles Instead of Default Roles — This improves security and access control. ✅ Regularly Audit Role Assignments — Ensure that users have appropriate access.

Conclusion

Snowflake provides strong security features to protect sensitive data:

🔒 Encryption ensures data confidentiality. 🔍 Data Masking prevents unauthorized access to sensitive information. 🔑 Role-Based Access Control (RBAC) enforces granular user permissions.

By implementing these security features, organizations can safeguard their data while ensuring compliance with privacy regulations like GDPR, HIPAA, and CCPA.

WEBSITE: https://www.ficusoft.in/snowflake-training-in-chennai/

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Building a Data Analytics Portfolio for Job Applications

Building a Data analytics portfolio generally deal with highlighting your work, showcasing your personal brand and personality, your communication skills, etc. The personal information that you provide in your portfolio can greatly reflect your abilities and showcase your skills to prospective employers.  In other words, building a data analytics portfolio can accentuate your abilities and what you can offer in this domain.  The data analytical project can be a bit mundane at times and this is where a strong portfolio is vital.  Your portfolio should demonstrate your ability to work with real-world data and solve complex problems using data analysis techniques and highlighting your best work, values, and achievements.

Elements of a strong data analytics portfolio

 Homepage (landing page) stating your name, photograph, contact details, personal description, professional details, and area of interest. Make it as impressive as possible as it is the first page your recruiters observe.

 Career Summary and List of Accomplishments

 Area of Interest

 Projects that are done (links to GIT HUB page). Using Jupyter Notebook and R Notebook whose formats are a bit like MS Word documents combine interactive code with text and visual elements to present data analytics clearly. In other words, these web applications allow us to share live code, text, and visualizations in an interactive way and give the recruiters a more hands-on look at your work.

 Personal Blogs: Employers observe the ability of the candidate to comprehend complex insights in a clear and comprehensive manner which can be demonstrated through a blog post. They can also identify those skills by observing their projects.

 Resume or Curriculum Vitae

 Testimonials (Letters of Reference)

 Contact Details

The following things need to be kept in mind while creating a data analytics portfolio:

it should look professional

it should accurately reflect your skills.

Should be specific and occupationally focused.

it should be concise, easy to navigate, and visually appealing.

it is self-explanatory.

Support the information mentioned in your resume.

Include the best projects in your portfolio

While your resume summarizes your qualification and experience, your portfolio showcases your skills in action; making it an essential tool to convey to the recruiter your capabilities and suitability for the job, and bringing you closer to your dream job as a Data Analyst.

URL : https://www.edujournal.com/building-a-data-analytics-portfolio-for-job-applications/

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