The growing demand for data warehouses as a service is propelling the growth of the global cloud data warehouse market. This is mainly attributed to the rising need for enterprises to store and manage their data and have real-time access to it. With a cloud-based data warehouse, you can save a lot of money spent on maintaining internal hardware and infrastructure and improve cost efficiency.
The global cloud data warehouse market was valued at $4.7 billion in 2021 and is forecasted to reach $12,9 billion by 2026, growing at a CAGR of 22.3% during the period.
This blog post is an overview of Cloud Data Warehouse, answering the questions what cloud data warehouse is, what cloud data warehouse solutions are. Let’s explore!
What is a Cloud Data Warehouse?
A cloud data warehouse is a collection of data organized by computing power in a public cloud. It allows data to be stored or accessed from other sources for the purposes of business intelligence, analytics, and reporting.
Cloud based data warehouse is a new approach that leverages the scale and computing performance of modern cloud platforms. The cloud, as an infrastructure platform, allows you to store data in the most cost-effective way and analyze it with powerful servers. This enables organizations to save time, money, and other resources by deploying their data warehouses in the cloud.
Hence, companies can focus on interpreting data rather than managing the data center infrastructure, which is costly to acquire and maintain.
Cloud data warehouses have become a popular choice for business intelligence. Unlike traditional data warehouses, a cloud-based data warehouse delivers enormous flexibility and agility.
|Learn more about Data Warehouse: Definition, Benefits, Architecture Explained|
Benefits of Cloud Data Warehouse
Cloud data warehouses are becoming more popular because they allow businesses to use a pay-as-you-go model. This type of data warehouse provides a number of advantages over traditional data warehouses, including:
Cloud data warehouses are designed to help you analyze vast amounts of data in a fraction of the time with traditional solutions. They offer the ability to handle multiple data streams at various velocities. This means that they can load and query both real-time streaming data as well as historical data in an automated structure. This enables businesses to access faster insights and better decision-making.
A cloud data warehouse offers elasticity by being able to scale up or down as needed, and in most cases, businesses can handle the scaling by themselves. This makes it possible for organizations to quickly support new projects or usage patterns. This helps reduce costs by only paying for what you need when you need it.
For example, you could scale it out for some heavy-duty processing during the day, then scale it down at night to save money.
Since there is no physical hardware involved, cloud computing will be less expensive than traditional systems. There are also no upfront costs associated with building your own in-house cloud data warehouse.
Key Features of Cloud Data Warehouse
Cloud data warehouse vs. On-premises data warehouse
|On-premises data warehouse||Cloud data warehouse|
|Scalability||Depends on the in-house infrastructure. Can be costly to purchase/ reconfigure hardware, software…||Up-scale or down-scale instantly|
|Availability||Depends on the quality of the available hardware, software and the in-house IT team’s ability||99.99% of uptime with leading cloud providers|
|Security||Depends on the in-house IT team’s ability||Infrastructure and data safety is ensured|
|Performance||Excellent query performance if scalability is ensured||Multiple geographic locations. Great query performance|
|Cost-effectiveness||Need investments: hardware, IT team, training…||Pay-as-you-go. No hardware costs|
Traditional data warehouses usually store large amounts of enterprise historical data. These systems are often built with conventional relational databases like IBM DB2, or Oracle Autonomous Database. The main use case for these systems is to store and analyze historical data from operational systems. While traditional databases are still commonly used, many organizations find that they no longer serve their needs for analytics and offer limited flexibility for changes in requirements or queries.
Cloud-based warehousing is new; it appeared after the emergence of big data and cloud computing technologies. There are various options available when it comes to choosing a cloud-based data warehouse service. Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse are some examples.
Cloud data warehouse is a modern-generation approach in storing and managing data. It is an effective business practice, especially if you are running low on your current storage capacity.
Cloud data warehouse capabilities
1. Data integration and management
Cloud-based warehouse builds a centralized repository for all your data (structured, semi-structured, or unstructured data). This data can then be processed and analyzed using the same platform, which reduces the need for manual work and guesswork.
Users can also query and manage the data stored in the cloud warehouse using standard Structured Query Language (SQL).
2. Data storage
Cloud-based solutions offer unlimited storage capacity for your business data. Data storage is simple to scale up or down to meet the demands of concurrent users accessing large amounts of complex data.
3. Security and compliance
Cloud-based data warehousing is backed by industry-leading security practices for authentication, identity and access controls, encryption of all customer data at rest and in transit.
Moreover, a cloud data warehouse can help businesses comply with regulatory standards such as HIPAA/HITECH, GDPR certification.
4. Cloud Data Warehouse Automation
Here is the best practice for structuring data warehouse with three zones:
- Landing zone: Data in this landing zone is structured as tables. It mirrors the data from the transactional systems
- Curated zone: It conforms the data to some well-known methodology. This is the data vault.
- Analytics and reporting zone: It is typically structured as star schema, where there is a central fact and dimensions emanating from the central fact. This is the data mart.
There are a lot of SQLs needed to make it work step by step from landing to curated to data mart. With data warehouse automation, we can generate, model and create SQL for each of these zones without writing any code. The data flow is also automated from one zone to another.
6 Steps to Select Cloud Data Warehouse
Step 1: Define your business needs
Though cloud data warehouses are developed to be generalizable across industries and business departments, it’d be better if you outline your plan to use yours, as the factors for evaluating the providers can vary based on the use case and your business’s own demand.
Step 2: Check the technical internals
Data warehouses in the cloud are different regarding data requirements and assumptions. Some warehouses provide semi-structured data in structures such as Object or Array, while some others don’t.
The degree of flexibility required by a business shall determine which approach works best. For example, if a business needs to store data where the structure is not necessarily predefined, the warehouse with the looser structure above may be a good choice.
Step 3: Select cloud architecture: Cluster vs Serverless
There are two types of cloud data warehouse architectures:
- Cluster-based cloud: They are clustered derivatives which are ported to run as a service in the cloud. For example, Amazon Redshift, Azure SQL Data Warehouse.
- Serverless cloud: They are shared across many clients which make the database cluster invisible.
|Serverless cloud||Cluster-based cloud|
|Elasticity||Customers don’t need to manage clusters. If data expands, the queries are automatically scaled up.||Customer needs to expand cluster size when data and load expand.|
|Management||Cloud vendor manages the service.||Customers may need to manage cluster health and capacity.|
|Cost||Price per query. Difficult to predict.||Price per node. Easy to predict.|
Step 4: Set up the ecosystem
When selecting a cloud data warehouse, a company should consider where existing data and applications reside. For instance, if most of the data in a system is already in S3, using Redshift or Snowflake on AWS could result in performance gains due to physical data locality.
And even if you have to sync data from computers and storage resources in various availability zones, staying within AWS makes sure the data transfer paths are on highly optimized infrastructure rather than having to traverse the public Internet.
Step 5: List out security requirements
The cloud data warehouse you choose should support the level and type of security it needs. Though all major cloud data warehouse providers update their security systems regularly and patch vulnerabilities, the systems’ configurations and defaults are various.
For instance, how encryption is handled varies across the major cloud data warehouses. Specifically, BigQuery encrypts data at rest and in transit by default, while Redshift requires database encryption to be explicitly enabled.
Besides, you should also consider factors such as key management and access control.
|Know more about Data Warehouse Costs|
Step 6: Understand resource bundling and billing
Different cloud data providers bundle resources and calculate costs differently. Redshift, for example, bundles storage and compute resources together. This means simple pricing, but users also have to accept predefined instance type values for memory, storage, and I/O.
For example, BigQuery has a more granular pricing structure and charges for storage, bytes read, and streaming inserts. Unlike Redshift, its hardware resources are free. Its total costs are therefore less predictable, as they’re primarily a function of bytes read in queries, so it can be hard to accurately predict usage.
Azure’s data warehouse bundles the lower-level technical factors of cost related to compute, like logical CPU cores and I/O, into a “Data Warehouse Unit” (DWU). Cost calculations therefore turn out to be a function of storage and DWUs. With this, users can pause DWU usage, and charges then accrue only for storage.
Likewise, Snowflake abstracts physical resources into credits, which rise in number proportionally with the number of virtual warehouses and the amount of resources within each. A virtual warehouse is a cluster of machines that load data, handle queries, and perform other data manipulation operations. Storage is separate and billed per terabyte monthly.
Most cloud data warehouse services also have flat-rate pricing available. For example, Redshift provides a pricing model named Reserved Instances that offers discounts if an organization commits to and pays for resource usage for a year or more. Reserved Instances enable businesses with large deployments to manage their costs since usage is more predictable.
It’s true that sometimes estimating costs with high accuracy before using a data warehouse might be a hassle. However, you can conduct a simple analysis of expected workflows so things can be easier. You just need to ask relevant questions, such as:
- How much data do you expect to integrate each month?
- How frequently is the data updated?
- How often are the analytics jobs that you run and how much data do they read?
Answering these questions can help you calculate expected workflows so that you can compare the providers and make the final decision.
What’s more, all the major vendors in the market provide free trials. Consider requesting a demo or trial to define a rough estimate of what costs will look like on a scale.
Top 10 Cloud Data Warehouse Solutions Compared
There are many data warehouse vendors out there, which is fortunate as you are spoilt for choices. On the other hand, the abundance of cloud data warehouse providers might make it more challenging to select the right and most reliable one.
But don’t worry. We have collected and listed out all the criteria for choosing the best cloud data warehouse in this blog.
Amazon Redshift is one of the most popular cloud data warehouse firms on the market. This service powers the analytical initiatives of countless leading businesses including startups and fortune 500 companies.
It integrates perfectly with your data lake and AWS environments and allows developers to query vast amounts of data from a host of settings.
Pricing: Starting at $0.24 per GB per month.
Snowflake is designed for organizations that want a variety of choice for public cloud snowflake is now one of the market’s leading data warehousing Solutions.
Businesses can take advantage of this offer to become more data-driven and create amazing customer experiences. It also comes with per second pricing so you only pay for what you use.
Pricing: Pay as you go. Usage-based, per-second pricing.
Google Bigquery is a component of the Google cloud platform environment this highly scalable. Serverless cloud data warehouse is ideal for companies that want to keep costs low.
It gives businesses a quick way to make informed decisions by analyzing petabytes of data. It’s also notable for being a highly accessible solution.
Pricing: Starting at $4 per 100 slots.
IBM db2 warehouse is a relational database solution that delivers advanced analytics and data management solutions to businesses worldwide.
The operational database flies on delivering actionable insights and data availability to companies. It also integrates with the in-memory columnar database engine from IBM, making a particularly high-performance database solution.
Pricing: Starting at $0.
Microsoft Azure Synapse is the evolved version of Microsoft Azure SQL data warehousing. It is a state-of-the-art analytic solution that combines enterprise data warehousing with the latest big data analytics.
It also enables you to unlock the power of machine learning and business intelligence solutions as part of your full data framework.
Pricing: $1.20- $360/hour. $122.88/TB/month.
Oracle Autonomous is a fully managed cloud service that provides provisioning for a data warehouse.
It offers businesses an easy-to-use and accessible system that scales with their operations. It also provides fast and elastic query performance without the need for endless administration.
Pricing: Starting at $0.0255 per month.
SAP is a cloud-hosted solution for businesses that want to make more intelligent business Decisions.
This enterprise-ready data warehouse can bunch all your unique data sources into a single environment allowing you to enhance the security and credibility of your information. SAP data warehousing is also elastic, flexible, scalable and open.
Pricing: Starting at $1.12 per month Capacity Unit.
Yellowbrick takes a unique approach to cloud data warehousing by offering access to data solutions for hybrid cloud.
On a mission to make data warehousing and analytics simpler for every business, Yellowbrick delivers a turnkey appliance for optimized analytics. Companies can also run any ad-hoc queries that they like alongside large batch queries and business reports.
Pricing: Starting at $10,000 per month.
Teradata integrated data warehouse for over 35 years. It has delivered enterprise-wide data warehousing to global companies that want a competitive advantage built from the ground up.
It offers a free 360-degree view into a business’s data and access to Teradata query grid for actionable insights.
Pricing: Get quote.
Panoply is an ETL-less and easy-to-access data management and warehousing system built exclusively for the cloud.
It delivers integrated visualization features and a wide range of storage optimization algorithms to help businesses thrive. It also works with other business intelligence tools such as Saleforce, Hubspot.
Pricing: Starting at $639 per month.
3 Criteria to Assess Whether Your Cloud Data Warehouse is Succeeded
It meets data security and governance requirements
Security is often the number one concern when it comes to cloud data warehouses. You might be asking yourself if your data is safe in the cloud. The answer is that it has to be safer and more secure than hosting your data on-premises.
When you use a cloud-based tool such as Amazon Redshift, Google BigQuery, Snowflake, they will be responsible for securing your database so that only people with appropriate permissions can access and manipulate it.
Your company needs to set specific data security and protection requirements and ensure that vendors can fulfill these requirements. Cloud-based data warehouse vendors may offer the following features:
- Encryption: Strong encryption protects data while it’s at rest and in transit.
- Authorization or User authentication: These controls ensure that no unauthorized users can access data.
- Audit logging: Auditing allows enterprises to confirm that their security standards are satisfied. It tracks the process of who accessed what and when.
- Secure network topology: The network topologies of cloud-based data warehouses follow the best practices for security by design.
You should consider how long a cloud data warehouse vendor has been in business. This indicates the vendor’s stability and financial ability to support its services. You should also review the vendors’ backgrounds to ensure they have got adequate industry-standard certifications, obeyed regional and international laws.
It provides vast integration capabilities
Enterprises are likely to be using a data warehouse alongside existing systems. They need a data warehouse solution that integrates seamlessly with your current infrastructure. The following factors should be considered:
- Network connectivity: Be sure your data warehouse tool can access the multitude of sources you need, whether they are located on-premises, in the cloud, or a combination of both.
- Data movement: When it comes to moving data into your data warehouse, it’s crucial to follow how this process works and what the outcomes are. Can the tool cleanse and transform the data as it moves? Can it build reports based on the censored data while leaving the original source untouched?
- Data profiling and analysis: It’s essential that your data warehouse software can profile and analyze all incoming data so you can limit or eliminate insufficient data before it reaches production.
- Data compression: A good data warehouse solution should compress data at rest and in motion for faster processing.
As successfully supporting the integration of businesses’ current systems and a new system, those cloud data warehouses will be essential for your business. They will help you effectively move your data to the cloud and to keep pace with today’s analytics demands.
It offers an optimal pricing model for your data storage
When choosing a cloud data warehouse, you should understand the various pricing models available, weigh the pros and cons of each model. The optimal pricing model depends on your specific requirements. Some of the elements could be involved:
- Procurement costs: The staffing costs for system selection and decision-making, hardware and software license fees, and related hardware maintenance fees.
- Deployment cost: The cost of planning the project, system design as well as hiring professional services to test, implement and support it.
- Data development and management costs: The cost of developing applications and other interfaces to support business analysis and data warehouses.
- Business opportunity cost: The value generated by historical business opportunities that were missed because the system was not available during that time.
- O&M (Operations and Maintenance) cost: Maintenance fees for software licenses, data space, and system upgrades.
Each enterprise has different economic conditions and needs for implementing cloud data warehouses. Some are willing to invest in upfront hardware and software license purchases knowing that they will have a longer-term asset amortization and payback period. Others prefer to have a lower up-front investment but recognize that there will be higher ongoing costs associated with paying for computing, storage, and database licenses hourly or monthly.
A cost model in this situation can be used as a decision support tool for organizations to identify the necessity and efficiency of using cloud-based data warehouses.
|Building the data warehouse from scratch is a daunting start for beginners, so it’s best to seek advice from experienced data scientists and data analysts.|
Synodus provides Data warehouse (DWH) services, including advisory, implementation, support, migration, BI reporting components, and managed services to help companies benefit from a high-performing DWH.
As companies continue to seek new ways to innovate, more and more businesses are getting involved in the data warehousing in the cloud. However, not all cloud data warehouse users are seeing the same results that they want. Therefore, it’s vital to know how to look for the right cloud-based data warehouse solutions beforehand.
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