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What is Snowflake Data Cloud?
Snowflake is Software – as – a – Service ( SaaS ) based Data Cloud platform that enables the organization to eliminates data silos present in their data by seamlessly unifying your data warehouses, data marts, data lakes, and other kinds of siloed data by making Single – Source – of – Truth i.e., one copy of data that can comply with data privacy regulations such as GDPR, CCPA, etc., and also allowing you to Integrate, Transform, Analyze, Share, and even Monetize your data with a Near – Zero management platform that delivers virtually unlimited scale and concurrency that can run on any of public cloud platforms like AWS, Azure & GCP.
For Modern Applications & Data Applications, Some kind of Database application acts as a backend to store & retrieve the data whenever necessary.Many application builders struggle to deliver on the promise of their apps because they’re wrestling with data stacks that can’t scale with ease or handle concurrency.
Provisioning databases becomes a recurring nightmare because it’s impossible to predict demand accurately, which makes the whole process inefficient and costly.
Any Ideal Data Warehouse Solution must have the following features to meet the Current & Near – Future demands.
Strong performance that supports fast user experiences.
On-demand Scalability & Elasticity where resources are allocated and deallocated exactly when needed.
Able to Support all kinds of data, which allows teams to use ANSI SQL to query structured and semi-structured data and ingest JSON, Avro, and Parquet without transformations.
Cost-effective compute and storage of data, including storage of historical data.
Concurrent workloads that avoid any contention between customers or users.
Unlimited concurrency for Analytics Engine for queries such as Business Intelligence ( BI ), Machine Learning, Real – Time Analytics and Ad – Hoc queries.
- Near-zero management by Automatic provisioning, availability, tuning, data protection, and other operations across multiple clouds, taking the burden off on DBA, DataOps & DevOps teams.
It doesn’t matter if your Data Solution is running on a RDBMS or NoSQL databases or on a Big Data Stack or Real – Time Data Stack, Data Lake Stack. Each has its unique challenges because none of them are built specifically for the cloud.
Here is where Snowflake Data Cloud comes into picture. Snowflake can easily fit into your existing architecture and can give ability to build data applications with no limitations on performance, concurrency, or scale. Snowflake equips organizations with a single, integrated platform that enables users to shift from traditional data
warehouse and big data platforms to a cloud-based system that natively loads and optimizes both structured and semi-structured data.
- Snowflake Object Hierarchy
- SF Account Creation
- Connecting to SF
- SF Classic Web UI
- SF SnowSQL
- SF SnowSight
- SF Connectors – Python, Spark, Kafka
- SF Drivers – JDBC/ODBC, GO, .NET, Node.js, etc
- SF System Architecture
- SF Pricing & Billing
- Creating & Managing SF Databases
- Creating & Managing SF Schemas
- Creating & Managing SF Tables & their Types
- Intro to Other SF Objects
- Best Practices on Accounts, Databases, Schemas & Tables
- Virtual Warehouse Optimization
- Constraints in SF Databases
- Micro – Partitions & Pruning
- Automatic Data Clustering
- Search Optimization Service
- Query Profiler & Query Optimization
- Snowflake Caching
- Result Cache
- Metadata Cache
- Local disk Cache
- Remote disk
- Query Tagging
- Infrastructure Security
- Data Encryption – At Rest & In Transit
- Snowflake Zero – Copy Cloning & Data Sampling
- Snowflake Time Travel
- Snowflake Fail – Safe
- Intro to Disaster Recovery – Database Replication & Failover/Failback
- Stages, File Formats, Integration Objects & Copy Command
- Bulk Data Loading
- Continuous Data Loading Pipelines
- Loading/Ingest & Querying/Parsing of Semi – Structured Data
- Data Lakes in Snowflake
- Insights on working with Unstrucutred Data
- SQL Concepts – Clauses, Sub-Queries, Joins, CTE, SF General Query Syntax, etc
- Snowflake Built – in Functions
- Stored Procedures in SF
- Snowflake Connector for Python
- Snowflake Connector for Spark
- External Functions
- Intro to Snowpark
- Intro to Secure Data Sharing
- Data Provider & Data Consumer Accounts
- SF Secure Direct Share
- Full & Reader Account Consumers
- Sharing across Cross-Region & Cross-Cloud
- SF Data Marketplace
- Data Exchange
- Snowflake Authentication Types
- Snowflake Network Access Control
- Snowflake Data Access Control
- Discretionary Access Control – DAC
- Role-Based Access Control – RBAC
- Column – Level Security
- Row – Level Security
- SF User Management – Privileges, Roles, Users
- SF Auditing
- SF Metadata Management
- Information_Schema Schema – Tables & Views
- Account_Usage Schema – Tables & Views
- Object Tagging
- Resource Monitoring & SF Object Monitoring
- Intro to SnowAlert
- Integration with BI Tools
- Loading Data into BI Tools
- Sample Reports on Loaded Data
- Summary of Best Practices on Data Storage, Virtual Warehouse, Table Design, Views Design
- Best Practices on Data Loading & Unloading
- Best Practices while Working with Semi-Structured Data & UnStructured Data
- Network Security Considerations
- Best Practices on Roles & Users
- Naming Conventions
- Best Practices for Query Performance
- Best Practices for Data Pipelines
Snowflake was architected from scratch i.e., Snowflake has been built up from the ground. For cloud computing, no legacy technology was carried forward in the process. Snowflake reimagined many aspects of data management & data operations. The result was a cloud data platform with massive scale, blistering performance, superior economics, and world-class data governance. Snowflake is innovative in the number of vectors that wants to deliver this breakthrough.
Apart from all these what really makes this platform is its unique Architecture i.e., Decoupling or Separation of Compute Resources and Storage Resources. Customers can also scale Storage, & Compute independently of each other, something that was not possible before in any of the earlier Technologies. Snowflake is completely designed for cloud-scale computing, both in terms of data volume, computational performance, and concurrent workload Distribution. Not only can snowflake customers spin up as much capacity for as long as they deem necessary, but the utility model ensures they only get Charged for what they consumed by the machine by a Second, highly granular measurement of utilization ( “Pay – for – what – use” Pricing Model ).
Snowflake is a cloud-agnostic platform, meaning that it doesn’t know what it’s running on. Snowflake completely abstracts the underlying cloud platform. Initially, Snowflake is available on AWS. By July 2018, it is available on MS Azure and by Nov 2019, it is available as preview on GCP.
Cloud Agnostic Feature is a key differentiator that Snowflake has always talked about, even when they were only available on AWS. Snowflake’s availability on AWS, GCP and Azure makes it the only cloud-agnostic data warehouse that is delivered as a service. Redshift, BigQuery and Azure Data Warehouses all require a relationship with a single cloud vendor, and each of these cloud vendors sells their own tools for ETL, visualization and analysis.
Snowflake does not require a relationship with a cloud vendor, nor do they sell tools for ETL, visualization or analysis. They have one game, and that game is being the best data warehouse available. When you bring together the best data warehouse, the best ETL tool, the best visualization tool and the best analytic tool, you’re going to end up with the best solution. Snowflake’s cloud-agnostic approach makes that very much possible.
Snowflake has Multi-Cluster Shared Data Architecture. This means, Snowflake incorporates centralization of Near unlimited amounts of data (Structured, Semi-Structured or Unstructured) from different kind of data sources. And Coupled with the ingestion of Not only Structured data, but also Semi – Structured data like JSON, XML, Avro, Parquet, and other data without transforming bcoz Snowflake has Schema – on – Read Modelling.
Through a Multi-Cluster, shared data architecture, dedicated compute clusters can be spun up to support a nearly unlimited number of concurrent workloads on shared tables.
Snowflake Workload Management
The Snowflake Data Cloud supports a massive range of solutions for data processing, data integration and analytics and its capability of handling a diverse range of workloads including Data Warehouse, Data Engineering, Data Lake, Data Science, Data Applications and Data Sharing & Exchange.
SF Data Engineering General Solution Architecture
Data engineering is a practice of designing and building the system that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and take data driven decisions.
Some common tasks you might perform as a Data Engineer are:
- Collaborate with management to understand company objectives
- Acquisition of data that align with business needs
- Develop algorithms to transform data into useful, actionable information
- Build, test, and maintain database pipeline architectures
- Create new data validation methods and data analysis tools
- Ensure compliance with data governance and security policies
The General Snowflake Data Engineering Solution Architecture is shown below.
The steps involved are data acquisition, ingestion of the raw data history followed by cleaning, restructuring, enriching data by combining additional attributes and finally preparing it for consumption by end users.
Data Ingestion & Transformation: Which involves capturing the raw data files and storing them on cloud storage including Amazon S3, Azure Blob or GCP storage. To migrate On-Premises Databases or Cloud Data warehouses, one can also use 3rd – Party Data Replication Tools or ELT/ETL Tools. After loading the data into a Snowflake table from this point it can be cleaned & transformed and stored into the Tables as neccessary. It’s good practice to initially load data to a transient table which balances the need for speed and resilience and simplicity and reduced storage cost. Unless the data is sourced from a raw data lake, it’s good practice to retain the history of raw data to support machine learning in addition to data re-processing as needed.
Data Presentation and Consumption: Whereas the Data Integration area may hold data in 3rd Normal Form or Data Vault, it’s normally good practice to store data ready for consumption in a Kimball Dimensional Design or denormalized tables as needed. This area can also include a layer of views acting as a semantic layer to insulate users from the underlaying table design.
Data Governance, Security and Monitoring: Refers to the ability to manage access to the data including Role Based Access Control in addition to handling sensitive data using Dynamic Data Masking and Row Level Security. This also supports monitoring Snowflake usage and cost to ensure the Snowflake platform is operating efficiently.
Finally, the data consumers can include dashboards and ad-hoc analysis, real-time processing and Machine Learning, Business Intelligence or Data Sharing.
What are the advantages of Snowflake Data Cloud?
With All things said and done, Snowflake has numerous advantages over its competitors & Traditional Data Platforms like
- Snowflake provides Unlimited Storage & Unparalleled performance combined with its simplicity. No other tools can compete Snowflake in this regard.
- Snowflake can easily fit into your existing architecture and can give ability to build data applications with no limitations on performance, concurrency, or scale.
- Decoupled nature of Storage Layer & Compute Layer.
- Snowflake can provide Single Source of Truth for all your Data Needs.
- Every virtual warehouse can access all data.
- Fast Analytical Queries Using Elastically Scaled Warehouses with ANSI SQL.
- Native Support for Semi-Structured Data (Ingest & Query without defining Schemas in advance – Schema – on – Read Modelling).
- Virtual Warehouse Workload Isolation Ensures Unlimited Concurrency with Instant Scalability.
- Near-Zero management by automatically handling provisioning, availability, tuning, data protection, etc.
- Snowflake Releases Updates with no downtime by implementing Staged Releases.
- Snowflake can Able to handle Semi-Structured Data with existing Skillset like SQL, Python.
- Independently Sized warehouses for specific requirements XS for PowerBI, XL for Data Engineering Tasks.
- Finally, Low Entry Point, Quickly Scalable as per your needs(Elasticity), Inexpensive Data Storage, Pay-as-you-use Policy,, Secure Data Sharing and very low administration burden.
Based on the Polling conducted by Hashmap, a technology partner of Snowflake, polled their customer base (they’ve completed 90+ Snowflake projects over the last 3 years) going into Snowflake Summit 2021, to get a sense of how Snowflake is being used and the value that is being delivered. You can see the summary of polling below.
* Very in-depth training material with real-time scenarios for each topic through the Snowflake Training Sessions.
* We also offer Case Studies & Assignments for Snowflake training.
* We schedule courses according to your convenience by trained and highly qualified experts in real time.
* We provide a recorded session for future reference.
* We also provide a regular, fast and weekend course for Online Snowflake Training.
* We also offer profitable and flexible payment plans.
* Get Snowflake Training In Hyderabad @ Analytics Benchmark Trainings.
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Comprehensive and Up-to-date Material On Topic.
Interview Tips & Mock Exams
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