Snowflake in Data science

Sifting sand for gold is how it feels like for a data scientist to find accurate data in an ever-growing ocean of information. You might not find gold in the sand but your search for accurate data from several sources end here with Snowflake. In this Tutorial, we’ll learn about the features of Snowflake for Data Science.

What is Snowflake?

In simple words, Snowflake is a cloud-based data warehouse, which provides structured, non-structured, and semi-structured data from one unified source. Snowflake can run on AWSprovides, Azure, and a Google cloud Platform (GCP). Data science is a field that comprehends large amount of amounts to gain insight and make more informed decisions. Snowflake came as a boon for data scientists and engineers. as it solved numerous problem . We will be talking about the Top 3 Problems encountered by data scientists and how they are seamlessly solved by Snowflake.

Overcoming Data Scientist Challenges with Snowflake

Data scientists grapple with a myriad of challenges in their quest for meaningful insights. However, Snowflake, with its innovative features, adeptly addresses some of the most pressing issues encountered in the realm of data science.

Searching For Relevant Data

In the Era of exploding data, it can be hard to find the right data for your task it con be (refining model , identifying growth and sales of a product , to conduct research and to identify risk and opportunity , etc. ) Sometimes the data is trapped in a individual system , or there is need of more data to make more informed system for that data scientist have to collect more data from several source’s which is labor intensive. Data Scientist spend most of their time in searching for data from several sources which leads to slog -fest. So that searching for right data doesn’t become Everest of to do list Snowflake comes into play.

Snowflake: A Seamless Solution

Imagine a ABC Ltd company ,who wants to analyze its sales of laptop among its 7 store. Where every store has maintained a data set for sales of laptop that is specific to that store only. Snowflake unifies all the data set into single one .This eliminate Data Silos .Data silos in simple words is when your data is scattered in different places that places is silos. Snowflake make sure all user can access accurate and updated data .

Snowflake is known for its non discriminative behavior with data format. It stores all the kind of data format whether it is semi structured excel sheet or unstructured social media post. Automatic data optimization, the key feature of Snowflake can process the unstructured and semi structured data . Snowflake is future proof as it can effectively and efficiently adapt to changes in data format and data structure.

Performance

Traditional cloud platform encounter enormous and complex data set and query their performance results to become tardy. Which leads to Exasperated data scientist as traditional platform store their data in centralized server .When more data is to be added it require more hardware for which is not cost effective .

Snowflake: A Seamless Solution

Snowflake doesn’t store data at one specific location rather it store data in vast space in optimized structure which make data easily accessible. Snowflake use Parallel processing. Parallel processing enhance the performance of snowflake. In parallel processing when a query for a data is accepted , the query is broken down into small task .Each smaller task is assigned to warehouse and warehouse perform their assigned task. Snowflake store data in column which make data retrieval faster. Snowflake is based on cloud native design meaning it can scale naturally to meet the client need which makes it cost effective.

Security

Whenever we store our data at any platform we are concerned about its security. Due to rising cases of ransomware, phishing etc securing your data from any threat has become very crucial.

Snowflake: A Seamless Solution

Snowflake used AES(Advance Encryption Standard) 256 robust security to safeguard your data from any possible threat.

Lets say government uses AES 256 to protect their confidential data like -financial transaction. AES 256 will convert it into scrambled/unreadable gibberish mess which is called ciphertext which can’t be read rightly. It makes it practically impossible to hack the financial transaction. But what if someone from government itself want to access the financial transaction how can it be done ? It can be done through encryption keys. Encryption Key is a 256 bit long string of random character. Every Encryption key is unique. Only matching encryption key can convert ciphertext into its original form.

They are themselves highly protected as only limited individuals have access to them and have to go through multi factor authorization. The usage and access of keys are always monitored. Using Hardware security modules (HSMs) in simple term using hardware algorithm to keep keys isolated from outside world , which makes it difficult to extract. Keys are regularly replaced to be safe from any potential threat.

Snowflake Key Features for Data Scientist

• Automatic Data optimization: Snowflake quickly analyze your data and organize it into better format and structure.
• Automatic Data Compression: Snowflake to save storage space reduce the bit of your data without compromising the quality of data.
• Automatic Data Encryption: Snowflake strongly encrypts your data , for security of your data.
• Snowflake Support Standard SQL: you can insert multiple tables and merge multiple tables etc.
• Zero-Copy Cloning Innovation: Snowflake introduces a groundbreaking zero-copy cloning feature, empowering data scientists to generate replicas of entire databases or specific tables without redundantly copying the underlying data.
• Seamless Integration with Data Pipelines and ETL: Snowflake’s compatibility with diverse data integration tools simplifies its integration into data science workflows. The interoperability is then used to move data seamlessly between different stages of analysis using well-known ETL (Extract, Transform, Load) and data pipeline tools.
• Storage: It can store structured, semi structured , unstructured data.
• Rapid query processing: Snowflake is designed in a way for rapid query processing.
• Faster data processing

Snowflake is used by Disney, Netflix, Sony Pictures Entertainment , Nike , Twitter etc.

Snowflake Features in Action: Machine Learning Workflow in Data Science

Let’s align these Snowflake features with a standard machine learning/data science workflow to gain insights into how Snowflake seamlessly aligns with and enhances our processes.

Data Ingestion and Storage

Snowpipe: Snowflake’s continuous data ingestion feature allows for real-time data loading, ensuring that new data is seamlessly integrated into the data warehouse without manual intervention. This aligns with the initial step of acquiring and storing data for analysis in machine learning workflows.

Data Transformation and Feature Engineering

Collaboration is simplified through data sharing, allowing teams to work on feature engineering and transformation tasks across different departments or teams within an organization. It enhances the data transformation phase by promoting collaborative efforts.

Enables efficient manipulation and transformation of data for robust feature engineering.

Model Training

Snowflake’s Support for Python and R: Snowflake allows users to run Python and R scripts directly within the platform. This facilitates model training within Snowflake, eliminating the need to move data back and forth between different environments. This integration streamlines the model development process.

Model Evaluation and Validation

Snowflake’s support for versioning data helps in tracking changes over time. This is crucial during model evaluation, allowing data scientists to compare model performance across different versions of the dataset.

Model Deployment

Snowflake allows the deployment of user-defined functions (UDFs) and external functions, providing flexibility in deploying machine learning models within the Snowflake environment. This simplifies the integration of models into production systems.

Scalability and Performance

Snowflake’s Multi-cluster, Multi-warehouse Architecture: Snowflake’s architecture allows for the easy scaling of resources to handle varying workloads. This ensures that machine learning workflows can scale seamlessly as data and computational demands grow.

Monitoring and Optimization

Snowflake’s Query History and Performance Monitoring: Snowflake provides tools to monitor query performance, enabling data scientists to optimize and troubleshoot queries efficiently. This is essential for maintaining and improving the efficiency of machine learning models over time.

Conclusion

Snowflake, a cloud-based data warehouse, addresses data scientist challenges by unifying diverse data sources, ensuring performance through parallel processing, and enhancing security with advanced encryption. It seamlessly integrates into machine learning workflows, offering scalability, monitoring tools, and efficient data handling.