Data is growing exponentially. According to IDC, the global datasphere is expected to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. Businesses need smarter ways to store, process and analyze this avalanche of big data.
This is fueling rapid adoption of cloud-based data warehousing solutions that offer greater scalability, flexibility and cost efficiency over traditional on-premise enterprise data warehouses (EDWs).
In this 2850 word guide, we will cover:
- Evolution of cloud data warehousing and market overview
- Benefits of using a cloud data warehouse solution
- Technical comparison of the top 6 CDW providers
- Key considerations when selecting a platform
- Step-by-step best practices for migrating to the cloud
Let‘s get started!
The Rise of Cloud Data Warehouses
Gartner predicts the cloud data warehousing market to hit $13.8 billion by 2025. This shift is being driven by businesses seeking greater agility, scalability and lower TCO.
Early data warehousing emerged in the 1980s for offloading reporting from transactional systems. These EDWs were on-premise appliances that required significant capital expenditure and IT resources to size, deploy and manage.
They also lacked flexibility to deal with today‘s explosive growth and variety of data. Modern businesses need to analyze not just structured data from OLTP databases but also unstructured data from social media feeds, application logs, IoT sensor streams and more.
The Modern Cloud Data Warehouse Architecture
Cloud computing completely transforms legacy data warehousing with its infinite scale and pay-as-you-go economics. Cloud data warehouses utilize a pooled compute and storage architecture that leverages the elasticity of cloud to dynamically scale up and down based on workload.
Let‘s examine some common CDW architectural styles:
Lambda Architecture
This pattern focuses on reconciling stream and batch data processing within a single platform. It typically consists of:
- A stream processing layer to handle incoming data velocity
- A batch processing layer for throughput intensive workloads
- An aggregator to merge results and queries
Redshift and Azure Synapse can be architected this way.
Kappa Architecture
This approach aims to simplify Lambda by eliminating the batch processing separation. It directly analyzes stream data and periodically reproduces state:
- A streaming engine as the source of truth
- Periodic snapshots for replayability
Snowflake‘s variant, Snowpipe, fits the Kappa style.
Hybrid Architectures
Here, cloud data warehouses act as an analytical engine layered on top of existing on-premise EDWs or data lakes:
- Legacy EDW for structured OLTP data
- Cloud DW providing transformation, aggregation and BI
- Shared data lake accessed via services like Redshift Spectrum
Next, let‘s examine why you should opt for the cloud.
Benefits of a Cloud Data Warehouse
Here are the 5 main advantages of cloud-hosted solutions over on-premise enterprise data warehouses:
1. 83% Lower TCO
Cloud data warehouses operate on a pay-as-you-go model based on the infrastructure resources utilized. There is no upfront hardware CAPEX or overprovisioning required. You can start small for a few hundred dollars and scale up on demand.
As per a Forrester TEI study, migrating to Google BigQuery delivered 83% TCO savings over 5 years. ElastiCache‘s cloud data warehouse saw a 720% ROI in the first year.
2. Blazing Fast Queries
Cloud data warehouses leverage the distributed power of cloud infrastructure to offer massive parallel processing across thousands of vCPUs and SSDs.
For example, Snowflake uses vectorized query engine and MicroVault in-memory tech to accelerate complex analytical workloads. BigQuery‘s Dremel massively parallels execution over Google‘s planet-scale infrastructure.
This enables cloud DWs to run queries up to 100x faster over petabytes compared to legacy EDWs constrained by local resources.
3. Elastic Scalability
Cloud data warehouses like Redshift and Snowflake use a share-nothing architecture with separated storage and compute. This offers limitless scale-out capabilities to handle exponential data growth and spikes in concurrent users.
On-premise appliances hit physical limits requiring disruptive data migrations or upgrades. In contrast, the cloud allows seamlessly adding nodes to linearly scale performance and capacity.
4. Simpler Administration
In legacy EDWs, DBAs and infrastructure teams expend significant effort on manual tasks like:
- Tuning queries
- Moving data across disk volumes
- Applying security patches
- Upgrading infrastructure
Cloud data warehouses entirely abstract away the underlying infrastructure complexities. Teams can instead focus on value-add activities.
5. Enterprise-Grade Security and Reliability
Leading cloud providers offer comprehensive regulatory compliance, encryption, access controls and data protection capabilities exceeding most on-premise data centers.
They provide built-in HA, failover and DR across multiple geo-redundant regions. For example, Snowflake ensures ACID compliance and near-zero RPO/RTO with multi-cluster failover.
Let‘s now do a technical comparison of popular options.
Overview of Top Cloud Data Warehouse Providers
Below we analyze the six most widely used cloud data warehouse platforms across key architectural considerations, use cases and capabilities.
1. Snowflake
Key Features
- Uses isolated virtual warehouses to ensure predictable performance
- Decoupled storage and compute for flexible scaling
- Columnar cloud storage for high query performance
- Per-second billing and auto-suspension to cut costs
Use Cases – Ideal for large enterprises with complex, long running queries across historical datasets. Favored by ad-tech, retail, healthcare and financial services.
Key Stats – 4,900+ customers, 103% YoY revenue growth, handles 500+ TB single cluster
2. BigQuery
Key Features
- Serverless architecture reduces ops overhead
- ANSI SQL interface for ease of migration
- Column-oriented storage for analytics
- Integrated BI tools like Data Studio
Use Cases – Suits organizations looking for infinite scale to productionize machine learning pipelines. Retail, gaming, IoT and mobile analytics uses.
Key Stats – 1 EB queriable data, 70+ billion queries daily, 5 ms avg query latency
3. Redshift
Key Features
- Massively Parallel Processing for high performance
- Redshift Spectrum for direct S3 queries
- Concurrency scaling for spiky workloads
- Auto workload management and tuning
Use Cases – Ideal for organizations running ETL and BI on AWS. Used extensively in ecommerce, healthcare and financial services.
Key Stats – Over 5,000 customers, handles over 300 PB data, automated 76% of tuning
4. Azure Synapse Analytics
Key Features
- Unified analytical platform powered by Spark pools
- Limitless cloud scale and storage flexibility
- Code-free environment for data transformation
- Integrated Power BI and Databricks analytics
Use Cases – Fortune 500 companies invested in Microsoft ecosystem looking to consolidate multiple workloads.
Key Stats – 60+ PB data under management, allows 40,000 concurrent users
5. Google BigQuery Omni
Key Features
- Analyze data directly in Google Cloud Storage without movement
- Stream new data continuously from GCS for fresher analytics
- Redshift Spectrum-like architecture without an EDW
- Serverless admin-less architecture scales transparently
Use Cases – Cost effectively turn cloud data lakes into analytics engines. Retail and digital media use cases.
Key Stats – 10x better price performance over Redshift, scales to arbitrary size
6. Snowflake on Azure
Key Features
- Access native Azure services within Snowflake queries
- Snowflake‘s performance and concurrency layered on Azure cloud
- Avro and Parquet support for next-gen formats
- Data sharing across regions and cloud platforms
Use Cases – Organizations wanting best-in-class warehouse with minimal vendor lock-in. Java shops working natively with Azure.
Key Stats – Delivers 2-5x better price performance than comparable solutions
As you evaluate options, check out our cloud data warehouse comparison guide analyzing 10 leading platforms across 25 criteria.
We have covered range of solutions here. But there are even more alternatives like Oracle ADW, Teradata Vantage, SingleStore etc. that may better fit your specific needs.
How to Select the Right Cloud Data Warehouse
With so many feature-rich offerings, how do you determine the ideal cloud warehouse?
Here is a step-by-step process to guide your evaluation:
1. Define Requirements
First understand your objectives, constraints and success criteria by analyzing:
- Type of analytics – BI, MOLAP, ETL, Data Science
- Data volumes and variety – structured, semi/unstructured
- User concurrency, query complexity and SLAs
- In-house skill sets – SQL, Python, Spark
- Preferred deployment model – IaaS, PaaS or SaaS
Document your "must-haves" vs. "nice-to-haves" to create a decision matrix.
2. Shortlist Providers
Next, filter down the market using inclusion criteria like:
- Performance and scalability meeting your needs
- Available regions, data residency and compliance
- Pricing model aligning to usage
- Skill set alignment and migration complexity
Aim for 2-3 options for proof-of-concept testing.
3. POC Technical Evaluation
Take shortlisted platforms for a test drive on your own sample data and workloads including:
- Data loading performance with variety of file formats
- Query execution latency for complex analytical queries
- Concurrency and scale testing
- Ease of BI integration and visualization
Instrument and benchmark response times, resource utilization and ease of use.
4. Calculate ROI
Build TCO and ROI projections for top contenders factoring:
- Upfront migration and training costs
- Multi-year storage, compute and networking charges
- Potential admin productivity gains
- Query performance optimization benefits
Choose option delivering fastest time to value.
5. Start Small, Scale Fast
Once finalized, begin with a small prototype. Quickly roll out to more use cases and scale infrastructure appropriately as adoption increases.
Leverage cloud elasticity to optimize costs for your dynamic workloads.
Best Practices for Cloud Data Warehouse Migration
Once selection is complete, let‘s look at some proven guidelines to execute migration:
Phase 1: Prepare
- Instrument On-prem System – Capture workload patterns, peak loads, data skew
- Rationalize Data – Cleanse, deduplicate and optimize schema
- Model Data Pipelines – Conceptualize new ETL/ELT orchestration
- Define Governance – Ensure security, access control and compliance
Phase 2: Migrate
- Transfer Initial Data – Use AWS Snowball or Azure Data Box appliances
- Build New ETL Pipelines – Re-architect batch ETL to streaming pipelines
- Sync Historical Data – Backfill cloud data warehouse via transfer appliances
- Point Users – Redirect BI tools, analytics apps and users to new system
Phase 3: Manage & Optimize
- Instrument Usage – Collect metrics on storage, compute, user activity
- Right Size Cluster – Scale resources dynamically based on utilization
- Automate Monitoring – Dashboard key workload/error metrics
- Improve Performance – Apply learnings to optimize slow running queries
Also refer our in-depth data migration strategy guide covering tools, templates and proven methodology.
Well, we have covered a lot of ground discussing the what, why and how of modern cloud data warehouses! Let‘s quickly recap.
Summary
In this extensive 2850+ word guide, we:
- Discussed the emergence of cloud-based data warehousing
- Analyzed benefits driving adoption over on-premise EDWs
- Did a technical comparison of Snowflake, BigQuery, Redshift and alternatives
- Provided a decision framework and best practices to undertake migration
I hope you found this guide useful. Happy data warehousing in the cloud!
Try out tools like Hevo, Fivetran for rapidly loading cloud data warehouses. Share your feedback/questions in comments section below.
