Hi Viewers, if you have any queries on this topic or you want any topic to learn from us, drop them in our comments section. Please make sure you subscribe to our UA-cam channel 👉Please ping on WhatsApp wa.me/9916961234
, SQL and Snowflake certifications are a great way to validate your skills and increase your marketability in the data job market. However, don't stop at certifications. Continue learning, build practical experience, and stay updated with the latest trends in data technologies.
Hi brother, i have one doubt support if a person going with 3 to 4 years of experience as snowflake developer what are all the skillsets that we have to know , i mean interviewer will expect python and pyspark also or sql plus snowflake plus aws/azure are enough??? Why I'm asking i don't have programming knowledge and i have to put some experience on snowflake, hope u understand my question
I understand your situation. While it's ideal to have a strong programming background for a Snowflake developer role with 3-4 years of experience, it's not necessarily impossible to land a job without it. Here's a breakdown of the skills you might encounter and alternative approaches: Expected Skillset (3-4 Years Experience): Strong SQL proficiency: This is foundational for data querying, manipulation, and analysis in Snowflake. You should be comfortable writing complex queries, working with views, stored procedures, and user-defined functions (UDFs). Snowflake-specific features: In-depth knowledge of Snowflake features like time travel, zero-copy cloning, materialized views, and security best practices is essential. Data modeling and warehousing concepts: Understanding data modeling principles, star schema design, and data warehousing concepts is crucial for designing efficient data structures in Snowflake. ETL/ELT processes: Familiarity with data integration techniques like Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) is often expected. While you might not need to code these pipelines yourself, understanding the concepts is essential. Cloud platforms: Familiarity with cloud platforms like AWS or Azure, where Snowflake is often deployed, can be beneficial. This might involve basic knowledge of cloud storage services, compute resources, and security configurations. Your Scenario (Limited Programming Experience): Focus on core Snowflake expertise: Demonstrate a strong grasp of SQL and Snowflake-specific features. Highlight your experience building data models, writing complex queries, and managing user access within Snowflake. Highlight transferable skills: Emphasize your analytical thinking, problem-solving abilities, and experience working with data in other contexts. This shows potential employers you can learn new skills quickly. Be upfront and honest: If asked about programming experience, be honest about your current skill level. Express your willingness to learn and grow in areas like Python or cloud platforms. Target junior/mid-level roles: Focus on job descriptions mentioning 2-3 years of experience. These roles might be more forgiving of limited programming experience as long as you possess strong core Snowflake skills. Alternative Approaches to Gain Experience: Snowflake certifications: Consider pursuing Snowflake certifications to validate your knowledge and commitment to the platform. Personal projects: Work on personal projects using free trials or community resources to build experience with Snowflake and potentially Python for data manipulation. Contribute to open-source Snowflake projects if possible. Online courses: Enroll in online courses or tutorials specifically designed for beginners in Snowflake or Python for data analysis. Remember, the key is to demonstrate your passion for data and willingness to learn. Highlight your existing Snowflake expertise, and showcase your potential to grow into a well-rounded data professional. By combining these strategies, you can increase your chances of landing a Snowflake developer role despite limited programming experience at this stage. Good luck!
we will make a video on this, meanwhile below are the some reasons . Snowflake: Best for data warehousing and SQL-driven analytics. Ideal for structured data and complex queries. Focus on query performance and separation of storage/compute. Great for sharing data securely between organizations. Databricks: Unified platform for data analytics, ML, and big data processing. Powered by Apache Spark for distributed data processing. Suitable for real-time processing and machine learning. Collaboration through notebooks for data exploration. *Choose Snowflake: If you need robust SQL analytics, structured data, and secure data sharing.* Choose Databricks: For integrated big data processing, real-time analytics, and machine learning capabilities.
. " Let me put this way, both databricks and snowflaje are desfined for different workloads, like databricks is majorly used for big data analytics (data lake, ML, AI and advance analytics), which needs a learning curve. Snowflake is designed as a modern data warehouse at core, it's more towards to implementing efficeint BI and SQL-based analytics workloads for both batch and near realtime. Also, we need to take into consideration, Snowflake is having more experience then databricks in this area. Thanks for raising this question, hope this reply helps. "
Hi Viewers, if you have any queries on this topic or you want any topic to learn from us, drop them in our comments section. Please make sure you subscribe to our UA-cam channel 👉Please ping on WhatsApp wa.me/9916961234
Hi, Very useful content. Please do videos on DAX, POWER QUERY. Thank you!
Sure
Nice !!! Quick recap of lot of snowflake concepts !!!
Thank you so much
Data Sharing is a feature in Redhift also ...by Datashare Objects
right sir
Sir can we get a job with sql & snowflake certification.
Please reply 🙏
, SQL and Snowflake certifications are a great way to validate your skills and increase your marketability in the data job market. However, don't stop at certifications. Continue learning, build practical experience, and stay updated with the latest trends in data technologies.
well explained ..
Thank you :) have a look at live interview recorded video of snowflake -- ua-cam.com/video/-zvysh3snv8/v-deo.html
Thanks
Welcome
Hi brother, i have one doubt support if a person going with 3 to 4 years of experience as snowflake developer what are all the skillsets that we have to know , i mean interviewer will expect python and pyspark also or sql plus snowflake plus aws/azure are enough??? Why I'm asking i don't have programming knowledge and i have to put some experience on snowflake, hope u understand my question
I understand your situation. While it's ideal to have a strong programming background for a Snowflake developer role with 3-4 years of experience, it's not necessarily impossible to land a job without it. Here's a breakdown of the skills you might encounter and alternative approaches:
Expected Skillset (3-4 Years Experience):
Strong SQL proficiency: This is foundational for data querying, manipulation, and analysis in Snowflake. You should be comfortable writing complex queries, working with views, stored procedures, and user-defined functions (UDFs).
Snowflake-specific features: In-depth knowledge of Snowflake features like time travel, zero-copy cloning, materialized views, and security best practices is essential.
Data modeling and warehousing concepts: Understanding data modeling principles, star schema design, and data warehousing concepts is crucial for designing efficient data structures in Snowflake.
ETL/ELT processes: Familiarity with data integration techniques like Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) is often expected. While you might not need to code these pipelines yourself, understanding the concepts is essential.
Cloud platforms: Familiarity with cloud platforms like AWS or Azure, where Snowflake is often deployed, can be beneficial. This might involve basic knowledge of cloud storage services, compute resources, and security configurations.
Your Scenario (Limited Programming Experience):
Focus on core Snowflake expertise: Demonstrate a strong grasp of SQL and Snowflake-specific features. Highlight your experience building data models, writing complex queries, and managing user access within Snowflake.
Highlight transferable skills: Emphasize your analytical thinking, problem-solving abilities, and experience working with data in other contexts. This shows potential employers you can learn new skills quickly.
Be upfront and honest: If asked about programming experience, be honest about your current skill level. Express your willingness to learn and grow in areas like Python or cloud platforms.
Target junior/mid-level roles: Focus on job descriptions mentioning 2-3 years of experience. These roles might be more forgiving of limited programming experience as long as you possess strong core Snowflake skills.
Alternative Approaches to Gain Experience:
Snowflake certifications: Consider pursuing Snowflake certifications to validate your knowledge and commitment to the platform.
Personal projects: Work on personal projects using free trials or community resources to build experience with Snowflake and potentially Python for data manipulation. Contribute to open-source Snowflake projects if possible.
Online courses: Enroll in online courses or tutorials specifically designed for beginners in Snowflake or Python for data analysis.
Remember, the key is to demonstrate your passion for data and willingness to learn. Highlight your existing Snowflake expertise, and showcase your potential to grow into a well-rounded data professional.
By combining these strategies, you can increase your chances of landing a Snowflake developer role despite limited programming experience at this stage. Good luck!
Subscribed your channel
Thank You So much
Why snowflake when we have better functionalities in databricks
we will make a video on this, meanwhile below are the some reasons .
Snowflake:
Best for data warehousing and SQL-driven analytics.
Ideal for structured data and complex queries.
Focus on query performance and separation of storage/compute.
Great for sharing data securely between organizations.
Databricks:
Unified platform for data analytics, ML, and big data processing.
Powered by Apache Spark for distributed data processing.
Suitable for real-time processing and machine learning.
Collaboration through notebooks for data exploration.
*Choose Snowflake: If you need robust SQL analytics, structured data, and secure data sharing.*
Choose Databricks: For integrated big data processing, real-time analytics, and machine learning capabilities.
. " Let me put this way, both databricks and snowflaje are desfined for different workloads, like databricks is majorly used for big data analytics (data lake, ML, AI and advance
analytics), which needs a learning curve.
Snowflake is designed as a modern data warehouse at core, it's more towards to implementing efficeint BI and SQL-based analytics workloads for both batch
and near realtime.
Also, we need to take into consideration, Snowflake is having more experience then databricks in this area. Thanks for raising this question, hope this reply helps. "