Why Snowflake Data Architecture?
- Provides flexibility to accommodate rapid business change
- Consolidates data silos into a single source of truth
- Enables advanced analytics like ML/AI for actionable insights
- Offers enterprise-grade performance, security, and governance
- Minimizes infrastructure costs with pay-as-you-go pricing
- Positions you for the data-driven future
Why Choose Beyond Key for Snowflake Services?
At Beyond Key, we differentiate ourselves by blending deep expertise with unwavering commitment to customer success.
Certified Snowflake Professionals - Our consultants hold multiple Snowflake certifications and participate in ongoing
End-to-End Solutions - We provide comprehensive services from assessment to strategy, migration, implementation, and support.
Tailored Architecture - We customize Snowflake architecture based on each client's unique goals and environment.
Data Migration Mastery - Our proven methodologies seamlessly transition legacy data into Snowflake.
Query Optimization - We optimize queries, indexes, and caching for peak performance.
Success Stories - We have delivered measurable ROI and transformation for diverse organizations.
Collaborative Approach - We work closely with your team to deeply understand needs and challenges.
24/7 Support - Our experts are available around the clock to address any issues or questions.
Continuous Learning - We stay ahead through nonstop Snowflake training on the latest features.
Future-Proof Strategies - We design Snowflake environments optimized for long-term relevance and efficiency.
About Beyond Key
As a decision maker, selecting the right data architecture to support your organization's digital transformation is a crucial choice.
With Snowflake's separation of storage and compute, you only pay for the resources you use. This elasticity allows your data architecture to scale up or down on demand, avoiding the costs of overprovisioning.
Our Snowflake data architecture services help you:
- Design a future-proof architecture to accommodate rapid business change
- Migrate legacy systems to Snowflake's cloud data platform
- Modernize your architecture with advanced analytics capabilities
- Optimize performance, security, and costs
Our Snowflake Data Architecture Services
With deep expertise in Snowflake implementation, our consultants follow a proven approach focused on your success:
Discovery Phase
We thoroughly assess your business needs, data landscape, use cases, and goals. This phase involves:
- Stakeholder interviews
- Current state analysis
- Requirements gathering
- Success criteria definition
Architecture Design Phase
Our certified architects design an optimized Snowflake data architecture tailored to your environment and objectives. Activities include:
- Logical and physical design
- Security planning
- Metadata strategy
- Integration planning
- Transition roadmap
- Reference architecture
Implementation Phase
Our engineers rapidly configure and implement Snowflake following best practices for security, performance, scalability, and maintainability. Steps involve:
- Environment setup & configuration
- Data loading & transformation
- Testing & validation
- Performance tuning
- Team enablement & knowledge transfer
Optimization Phase
We provide ongoing management to maximize the value of your Snowflake investment. This includes:
- Performance monitoring & tuning
- Bug fixing & issue resolution
- Feature enhancement
- Architecture evaluation & improvement
- Platform upgrades
- Cost optimization
Take the first step towards unlocking more value
from your data.
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Our Core Data
Architecture Services
Our core data architecture services for Snowflake include:
Data Visualization
We deliver interactive reporting and dashboards in tools like Tableau, Looker, Power BI to bring insights to business users.
Enterprise Data Warehouse Design
We design performant and scalable enterprise data warehouses optimized for complex analytical workloads. This includes schema design, table partitioning, clustering, materialized views, and other performance tuning techniques.
Data Pipelines
We develop robust data pipelines to move data from 100+ sources into Snowflake using ELT best practices. This includes building schemas, maintaining metadata, and ensuring data quality.
Data Lake Integration
We integrate Snowflake with your cloud data lake for unified batch and streaming analytics. Our data engineers handle ingestion, transformation, and tailored data access control.
Data Modeling
We create analytical data models using dbt to standardize business logic and metrics. Our dbt expertise drives efficiency through modular, testable code.
With deep expertise in Snowflake implementation, our consultants follow a proven approach focused on your success:
-
Discovery
We assess your business needs, data landscape, and goals. -
Architecture Design
We create a phased migration plan tailored to your environment. -
Implementation
Our engineers configure Snowflake following best practices for security, performance, and scalability. -
Data Migration
We rapidly migrate your data volumes into Snowflake with minimal business disruption. -
Optimization
We continuously tune your architecture as needs evolve to maximize ROI.
Some of Snowflake’s industry use cases
Here are a few examples of the transformative solutions we have delivered leveraging Snowflake:
BFSI
- Consolidated 5 legacy data warehouses into Snowflake
- Improved query performance by 10x
- Enabled real-time customer 360 analytics
Retail
- Migrated 500TB of data from Teradata to Snowflake
- Reduced costs by 70%
- Unlocked advanced ML/AI capabilities
Healthcare
- Created a governed central data hub on Snowflake
- Connected 40+ operational systems via Snowflake integration
- Cut reporting time in half for analytics team
Snowflake Data Migration
We offer end-to-end Snowflake data architecture services and also migration services:
-
Discovery
We analyze your legacy systems and data sources to develop a migration strategy. -
ETL Conversion
We convert legacy ETL/ELT jobs to optimized code leveraging Snowflake's native performance. -
Data Migration
We rapidly migrate TBs or PBs of historical data from legacy systems into Snowflake. -
Validation & Testing
We thoroughly test migrated data for accuracy and business logic. -
Post-Migration Support
We provide ongoing tuning, issue resolution, and optimizations for long-term success.
Data Engineering & Modernization
We modernize your analytics stack leveraging Snowflake's cloud capabilities:
-
Data Pipelines
We develop scalable, real-time data pipelines for transforming data in Snowflake. -
Machine Learning
We build and operationalize ML models using Python, Spark, and Snowflake ML tools. -
App Integration
We create seamless integrations between Snowflake and internal apps/SaaS solutions. -
Security & Compliance
We enhance governance, access controls, and compliance using Snowflake's enterprise-grade capabilities. -
Billing Optimization
We provide ongoing cost optimization and savings tracking to maximize ROI.
Snowflake vs Redshift vs BigQuery
Key differences between Snowflake, Redshift, and BigQuery:
Data Types
It is essential to understand the estimated volume and types of data you will be managing, as well as the sources from which this data will originate.
AWS Redshift
Supports:
JSON
Google BigQuery
Supports:
JSON
XML
Snowflake
Supports:
JSON
XML
Avro
Parquet (using a special data type)
Support and Maintenance
Setup:
- Sizing appropriate cluster as storage & compute are not separated
- Designing data workflow to match resource size
- Data optimization required
Maintenance:
- Requires Vacuuming/ Analysing tables periodically
Management:
- Difficult to manage without skilled AWS architect
- Customers may spend hours doing maintenance like updating
Setup:
- No sizing required as storage & compute are Separated
Maintenance:
- Low maintenance
-
Limitations:
- No indexes
- No column Constraints
- No performance tuning capabilities
Management:
- Fully managed service
- Backend configuration & tuning is handled by Google
Setup:
- No sizing required as storage & compute are separated
- Selection of cloud provider required
Management:
- Zero management from end users
Maintenance:
- Low maintenance
- Automatic and rapid provisioning of greater compute resource
Scalability: horizontally vs. vertically
- Local storage configuration; customers cannot scale resource independently
- Resizing or changing machine instance type requires cluster reconfiguration, which can take several hours while data is redistributed and places the cluster in read-only mode for the duration
-
Storage & compute are separated
- Full elasticity
- Automatic and rapid provision for greater compute resource to handle large data loads
- Storage & compute are separated
- Storage and compute can be scaled up and down independently and immediately; and the metadata service will scale up & down as necessary
- Trillions of rows can be sliced up with ease by multiple concurrent users
Security
- Strong focus on security
-
In addition to database security, other security features provided are:
- Sign-in credentials
- SSL connection
- Load data encryption & more
- Takes security very seriously
- All data is encrypted and in transit by default
- Administration can be fine-tuned via Cloud Identity and Access Management (IAM) and more
- Secured as per cloud provider's features
Pricing Models
Assessing which solution offers the best value for money is challenging, as it heavily relies on the specific use case. Therefore, we will outline the most effective use cases for each platform. First, let's examine the pricing models.
AWS Redshift
- Per for instance/ clusters
- On-Demand Pricing at an hourly rate depending on instance type & number of nodes
- Spectrum Pricing for bytes scanned while querying against S3
- Reserved Instance Pricing, prepayment of hourly rate at discounts of up to 75% over on-demand rates
Google BigQuery
-
Query-based pricing model for compute resources, in which users are charged for the amount of data that is returned for their queries
- $5 per terabyte scanned
- Discounted rate: 500 slots at a monthly flat rate of $10,000, or an annual flat rate of $8,500
- First terabyte of queries every month is free
-
Storage pricing
- $20 per terabyte per month for active storage
- $10 per terabyte per month for long-term storage
- first 10 gigabytes of storage every month are free
Snowflake
-
Time-based pricing model for compute resources, in which users are charged for execution time
- Approximately $0.00056 per second
-
Storage pricing
- $23 per terabyte per month if paid upfront
- $40 per terabyte per month if on-demand
Leverage The Experience of Our Snowflake Certified Expert
Partnered with Snowflake to provide you with the best possible solutions, Beyond Key has also got Core and Advanced Snowflake qualifications. Let our experience design the ideal warehouse for your needs.
