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.

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.