It’s expected that by 2025, we’ll be dealing with 175 zettabytes of global data. To understand its scale, imagine a tower of Blu-ray discs stretching from Earth to the moon — not once, but 23 times!
Businesses are shaping their growth strategies with this data explosion in mind. But simply applying data insights or predictive analytics in day-to-day operations is not enough today. Companies need to advance existing big data solutions to maximize the outcome. One way to do so is by switching to the cloud — big data cloud perspective is quite promising here.
On-premises data management systems may become insufficient. Adding more hardware when data grows exponentially is time-consuming and expensive. The same applies to upgrading to more powerful hardware each time data processing needs improvement.
The cloud, however, allows you to easily adjust resources, avoid large upfront hardware costs, and access analytics tools from anywhere. In fact, cloud adoption no longer requires time-consuming and tedious justification and validation by technology executives. Today, it’s merely a matter of “how quickly” and “to what extent, given the specific needs of the business”. Furthermore, the synergy of cloud computing and big data enables you to make smarter decisions and stay ahead of competitors. This combination changes how companies handle and use data, opening doors to new opportunities.
In this article, we discuss the benefits and challenges of moving your big data project to bespoke cloud solutions, as well as provide best practices to ensure smooth cloud adoption.
Big data in the cloud
When we talk about big data in the cloud, we refer to using cloud-based services to store and analyze large datasets. Seeing the cloud’s potential, companies turn to it to manage and analyze their information. In 2023, businesses spent $270 billion on cloud services, which is $45 billion more than in 2022.
The move toward cloud-based big data solutions has been driven by various factors, extending beyond the explosive growth in data volume and high costs of on-site infrastructure:
- The need to handle data from diverse sources (like CRM systems, financial transactions, IoT devices, and many more)
- Scalability requirements for unpredictable data fluctuations
- Demand for instant insights and competitive pressure
- Rise of remote work
Let’s explore the five main aspects that make big data platforms in the cloud so effective:
- Storage. Cloud platforms offer vast, scalable storage options using distributed file systems. This allows you to store multiple data types without space concerns.
- Processing. Cloud-based big data processing can be described in one word — speed. Cloud providers offer tools like Hadoop and Spark that simultaneously process data on several computers.
- Integration. Cloud solutions come with APIs that simplify integration with various data sources, both internal and external to the organization. In addition, cloud platforms provide a wide range of pre-integrated services.
- Analytics. ML and AI capabilities are frequently integrated into cloud platforms. Services like Amazon SageMaker or Google Cloud’s Vertex AI allow businesses to build, train, and deploy ML models to automatically uncover data patterns and insights.
- Security. While some companies doubt cloud security and prefer keeping their big data systems in-house for more control, cloud providers offer top-notch security measures, like strong encryption, strict access control, and regular security audits, and consistently improve them.
A real-world example from one of our clients highlights how the cloud can revolutionize big data in retail. Intellias developed a cloud-based platform for a European supermarket chain. The system collects data from temperature sensors in refrigeration units across 125 stores, processing this information in real-time. It provides instant alerts if any equipment issues are detected, allowing store managers to respond quickly to prevent food spoilage. One of the biggest strengths of our solution is that it can be easily expanded to more locations.
However, the success of big data platforms in the cloud largely depends on the right architectural approach. Let’s explore the various architectural types involving cloud computing for big data.
Architectures for cloud-based data platforms
When it comes to cloud computing for big data, you have several architecture options. Different types cater to various business needs, data volumes, and processing requirements. Here are some common approaches:
- Centralized architecture involves processing and storing all data in one central location. This approach offers simpler management and oversight and ensures data consistency. It generally comes with lower initial setup costs. Yet, centralized architecture can face scalability challenges when data reaches a certain threshold, while users far from the central location may experience latency issues. There’s also a risk of a single point of failure.
- Decentralized architecture, on the other hand, is designed for scalability from the ground up. It distributes data across multiple nodes and locations. This approach allows for horizontal scaling — adding more machines to the network. It offers better performance for geographically distributed users and improved fault tolerance. However, decentralized architecture is more complex to manage, so there’s a bigger risk for data consistency challenges. Also, the initial setup costs are typically higher.
- The hybrid architecture blends centralized and decentralized types, typically using a central warehouse for structured data and distributed lakes for unstructured information. The central warehouse can store data that needs to be quickly accessed, strictly managed, and consistently reported. Distributed lakes, on the other hand, are more suitable for experimental analytics, like ML training and data science projects.
- Serverless architecture relies on cloud provider-managed infrastructure. It reduces operational management needs and often uses a pay-per-use pricing model, which can be cost-effective. Automatic scaling based on demand is a key advantage. On the other hand, serverless architecture offers less control over underlying infrastructure and may lead to vendor lock-in.
- Event-driven architecture is designed to respond to specific events in real-time. This enables immediate responsiveness to data changes and efficient resource utilization, as the system is only active when events occur. On the contrary, event-driven architecture can be complex to design and debug.
Hybrid and serverless systems often appear to be the most suitable solutions. With hybrid architecture, you can achieve cloud operational excellence using centralized and decentralized approaches. Serverless architecture comes with managed services, including a pay-per-use pricing model and numerous pre-integrated tools.
Benefits of cloud-based big data solutions
The global big data market is experiencing explosive growth. MarketsandMarkets projects it to reach $273.4 billion by 2026, growing at a CAGR of 11.0% from 2021 to 2026. Interestingly, this growth is driven by increased data volume and the adoption of cloud-based big data across various industries. Over 50% of IT spending will have been redirected from standard solutions to the public cloud by 2025, up from 41% in 2022.
In fact, the practice of accessing, managing, and analyzing big data in the cloud is now referred to as “Big Data as a Service” or BDaaS.
So, what are the business benefits of marrying big data to cloud computing to implement a more cohesive, all-in-one solution?
Cost efficiency
Unlike on-prem data centers that are inherently expensive and often underutilized, a big data cloud service offers the benefit of paying just for the resources consumed and not a penny more. This automatically results in tangible savings, given that the application is properly designed and configured for the cloud. Companies moving their operations to AWS typically cut costs by about 31%. AWS also offers a free cost evaluation tool that helps businesses achieve future cost savings on the cloud.
When you sign up with a big data cloud service, you delegate the upkeep hassle to the corresponding cloud service provider (CSP): equipment maintenance, qualified technical staff, power bills, network troubleshooting, physical security, software updates, and so on. These organizations are typically very well-equipped for these tasks.
In case of conventional SQL-based data warehouses, the cost of constant upscaling and reconfiguration would be peaking and lots of effort would be going into dropping old (yet historically valuable) data to free up space. Big data analytics in cloud computing based on such tried and tested technologies as Hadoop can bring substantial cost advantages for organizations dealing with an ever-growing amount of unstructured data.
Rapid elasticity
Another key advantage of working with big data in the cloud is its natural elasticity. A big data cloud can shrink and expand depending on the immediate workload and storage requirements, allowing the client organization to pay only for the resources used over a period of time (as mentioned above) and maintain a certain predefined target level of application performance.
Elasticity — often fully automated — also helps reduce resource management efforts that would normally be added to the overall cost of operation in case of a more conventional, on-prem setup. This capability comes in especially handy for resource-intensive applications prone to occasional/seasonal/situational spikes of user activity.
Some good examples would be streaming services or large e-commerce sites where spikes are observed during holidays, weekends, or after the release of popular titles or products.
Finally, the ability to dynamically match the demand also facilitates the process of working with cloud-based big data analytics, enabling data scientists and analysts to always have unobstructed, fast access to historical data.
Contextual reporting and decision intelligence
The advent of the cloud-based big data analytics may steal the glory from the best, most elaborate BI dashboards out there. The latter are usually complex, multi-layered, and require business users to know where to look for the information they need. The transition to cloud computing and big data allows for real-time, highly personalized, contextual reporting intended for particular managers, user roles, or technical experts.
Contextual reporting can be based on a broad variety of technologies, including advanced ones like natural language processing (NLP), augmented analytics (use of AI and ML to help analyze data), real-time streaming anomaly detection, and many more.
The convergence of big data and cloud computing also creates fertile soil for practical data science in general and decision intelligence in particular. This complex discipline is a fusion of decision management and decision support manifested through the use of innovative, intelligent analytical systems based on big data.
Source: Gartner Hype Cycle for Analytics and Business Intelligence
Better business continuity and disaster recovery
Implementing effective fault-tolerance and business continuity mechanisms for on-prem data centers is a complex and expensive undertaking that not many companies can handle technically and financially. Cloud computing for big data, however, comes with all of these features readily available as free or reasonably priced, low-maintenance options.
All major CSPs offer data redundancy as part of their standard service offering and take care of creating multiple copies of their clients’ data at multiple levels and in various geographically distributed data centers. For example, Microsoft Azure ensures your data is always available and long-lasting by having several copies in different locations. This protects your information from issues, ranging from simple hardware failures to major disasters. Coupled with modern containerization technologies such as Kubernetes supporting one-click or fully automatic deployment (in case all the infrastructure was described as code), these measures guarantee fast and damage-free recovery of your applications and data.
Finally, every big data analytics cloud is reliably protected from most types of cybersecurity threats to an extent that is hardly attainable by in-house solutions. Additional cybersecurity consulting services can be obtained from corresponding CSPs or qualified third parties.
Data multi-sourcing
Cloud computing for big data dramatically eases the task of aggregating heterogenous data from any number of sources, which may include sensor arrays, IoT devices, remote databases, web applications, online partner networks, users, and many more. These data can then be processed with a high degree of parallelism and assigned to corresponding data pipelines.
Rolls-Royce, a world-renowned engineering company specializing in aircraft engines, has started to cooperate with Azure to make it easier to gather and analyze data from many sources and improve engine management. They collect worldwide fuel usage, air traffic control, and engine health data, and Azure IoT Suite brings all this data together in one place. At the same time, Cortana Intelligence Suite helps process it and derive valuable insights.
Despite the obvious advantages of big data in cloud computing, the implementation of the necessary components and their integration is by no means a leisurely walk in the proverbial park. The challenges are plentiful and a weighted approach to creating a cloud and big data strategy is required.
AI and ML integration
AI has grown so quickly that it’s hard to find a big tech company that hasn’t considered AI to improve its performance. Moreover, this trend has become so persuasive that core CSPs offer built-in AI and ML-based services. The full list of AWS’ AI and ML solutions is huge. Amazon Personalize lets you provide dynamic recommendations tailored to your customer preferences.
The AI revolution hasn’t bypassed Spotify, which has been parenting with Google Cloud since 2016. Recently, Spotify has started exploring Google’s AI offerings to suggest the right content and filter out potentially harmful material.
Challenges for adopters of data cloud solutions
Understanding application dependencies is the top cloud adoption challenge, with 54% of respondents in the 2024 State of the Cloud Report by Flexera citing it as a major concern. This is closely followed by assessing on-premises versus cloud costs, which 46% of all respondents find challenging. Technical feasibility is the third top challenge. Let’s look at these main obstacles to cloud implementation, as well as some other issues businesses often face when migrating to the cloud.
Understanding application dependencies
First, companies need to understand how their different software systems work together. This can be tricky because many businesses have complex IT setups that have grown over time. Missing even a small connection can cause big problems.
Recommendation: list all your software and investigate how it connects. You can use special tools to map these connections. It’s also valuable to involve key stakeholders from various departments to understand software dependencies.
Assessing on-premises vs. cloud costs
The second challenge is comparing the cost of current systems to cloud options. This isn’t easy because cloud pricing can be complicated — the diversity of services and pricing models makes it challenging to estimate total costs accurately. Plus, current on-premises costs often have indirect expenses, like electricity, physical space rent, and hardware maintenance.
Cost prediction becomes even more complex since big data workflows aren’t static. So, data usage may spike during specific periods, leading to unexpected expenses.
Recommendation: conduct a comprehensive audit of your IT expenses, including hardware, software, maintenance, and personnel costs. Cloud pricing calculators provided by major CSPs can help estimate potential cloud costs. It’s also important to consider long-term factors such as scalability needs and high-demand periods.
Technical feasibility
Some legacy systems were often designed for specific hardware configurations and may rely on outdated programming languages. The challenge becomes more complex when dealing with custom-built applications. That’s why, in some cases, legacy service migration to the cloud may not be technically feasible.
Recommendation: conduct thorough assessments of each system. This may include compatibility testing, performance benchmarking, and security audits. In some cases, you may need to consider modernizing or replacing certain systems to make them cloud-ready. With comprehensive cloud migration services, you can address any technical incompatibility.
Losing control over data
As the size of your cloud and big data goes up, you may see a proportionate decline in the degree of control you have over them. There are still tons of cybersecurity threats out there and the human factor isn’t going anywhere. Human negligence and oversight are among the top factors leading to data leaks and damage, especially in large infrastructures with incomplete coverage by automation and monitoring tools.
Recommendation: create and maintain strict cloud usage policies; ensure timely security updates; use automation where possible. Moreover, incorporating MLOps services contribute to improved security measures, ensuring the confidentiality and integrity of vast datasets stored in the cloud.
Reliance on third parties
Clouds are super-reliable, but they aren’t infallible. Occasionally, important services go offline without prior warning and leave millions frantically trying to access their mailboxes, documents, and data.
Recommendation: big data in cloud computing requires users to consider native and implement custom/third-party monitoring tools combined with detailed risk mitigation and remediation plans. Adopting a multi-cloud approach may be an option as well.
Network can cause a bottleneck
Cloud computing for big data is rarely done on premises. When you move all or most of your data and analytics to the cloud, you risk becoming completely dependent on your Internet connectivity. If your primary and secondary lines go offline, you will be left with no access to your data cloud solutions (although the data itself will keep flowing into the cloud).
Recommendation: make sure you have an auxiliary line with an alternative ISP; leave critical components in your on-prem infrastructure; assess the risks of going offline; come up with a mitigation plan.
Best practices for deployment of cloud-based big data solutions
While we’ve discussed solutions to core cloud migration challenges, there are additional best practices you can implement from the start.
Set goals and establish a long-term vision
Before you deploy big data in the cloud, define what you want to achieve with it. For the most cost-effective setup and to avoid major overhauls down the line, you need to think beyond your immediate needs. While questions like “Where do you see yourself in five years?” may seem cliché or even irritating in some contexts, that’s exactly the case when it comes to cloud migration.
Moving from one cloud platform to another isn’t easy or cheap if your chosen platform no longer satisfies your requirements. This is especially true for big data workloads, where the volume of data can make migration a time-consuming and expensive process.
Assess your existing data infrastructure
Another key step is to examine your current data engineering strategy. It’s important to understand what data you have, where it’s stored, and how it’s used. This could include everything from traditional databases to spreadsheets on individual computers, data from IoT devices, and even paper records that haven’t been digitized yet.
This assessment will help you identify which workloads are best suited for cloud migration and which might need to stay on-premises or be modernized. To get a comprehensive view of your data and estimate the level of your cloud readiness, you can turn to the Intellias cloud assessment services.
Select appropriate cloud providers
Cloud providers offer various services tailored for big data. These include data storage, processing, and analytics tools. As you prepare to implement big data analytics in the cloud, it’s essential to choose services that best fit your specific requirements. If you operate in the telecom industry, it’s worth exploring specialized telecom analytics solutions designed to handle the particular challenges of telecom data.
AWS, Azure, and Google Cloud are the leading, the most reliable, and, thus, the most expensive CSPs. If you require more affordable options, consider Hetzner and DigitalCloud. However, stability and low cloud spending rarely go hand in hand.
You can also consider a multi-cloud setup, where you can place your most critical systems on more resilient (yet costlier) providers. These might be systems that demand the highest levels of uptime and security. Meanwhile, less critical workloads can be hosted on cheaper cloud solutions.
Start small and scale up
It’s often wise to begin cloud migration with smaller, less critical datasets or applications. This allows you to learn and adjust your processes before moving more crucial data. As you gain experience and confidence, you can gradually scale up your cloud deployment for big data.
This approach also gives your team time to adapt. Cloud technologies often require new skills and ways of thinking. By scaling gradually, you allow your staff to learn alongside your cloud development.
Bring in a technology partner
You can also discover the power of cloud optimization services to squeeze every drop of value from your cloud investments. The right technology partner can help you fine-tune your cloud setup, ensuring you use resources cost-effectively. For example, they can set up auto-scaling solutions so your resources scale up or down based on demand. Or they might suggest adjustments to your cloud architecture or recommend new services, whether cloud-native or third-party, that could streamline your performance.
The cloud advantage: Translating big data into business value
The blend of cloud computing and big data has major benefits. The cloud transforms mountains of information into valuable insights that companies can actually use.
But here’s the important part: adopting cloud solutions for big data isn’t just a tech upgrade—you might need to rethink your operations. The payoff comes in the form of better customer experiences, cost savings, and the ability to innovate at a pace that was once unthinkable.
Ready to upgrade the speed and flexibility of your data via the cloud? Reach out to our experts for a guided tour through your options and implementation scenarios. We’ve done it before and will gladly apply our knowledge and expertise to transform your business for higher operational effectiveness.
