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In today’s business environment, data is everything. No organization can ever grow without collecting information from different data sources and analyzing it to uncover hidden patterns. Conventionally, companies used to build massive server rooms full of costly hardware to maintain their data storage and extract valuable insights. Nowadays, cloud consulting services are offering a better way: cloud data analytics. By executing data analytics in cloud computing, businesses can save costs, get access to real-time processing, and unlock endless scalability. In this article, we’ll discuss cloud and data analytics, take a look at the important components of cloud tools, and explore the benefits.
Let’s begin by answering the most basic question: What is cloud data analytics? Broadly speaking, cloud data analytics is a model in which data analytics and other processes are executed using cloud-based resources rather than on a company’s servers. In other words, businesses can yield similar results like data exploration and statistical analysis but shift the processes to the cloud instead of dealing with an on-premise infrastructure.
Although managing their own servers gives organizations more control over their data and security, the costs of maintaining data centers are enormously high. On top of maintenance expenses, the costs of scaling are even more devastating, making on-premise infrastructure unsustainable for many companies.
On the other hand, data analytics in the cloud offers improved scalability, sometimes even auto-scaling, and numerous savings derived from cloud cost optimization. Firms dealing with vast datasets can especially benefit from more efficient data processing and analysis.
However, it’s important to mention hybrid cloud data analytics, a middle ground in which companies can take advantage of the perks of cloud computing without moving all their assets to the cloud. In case a business cannot fully commit to the cloud infrastructure due to data security concerns or data governance requirements, it can opt for hybrid cloud data analytics.
How cloud analytics works is by following four steps, from ingesting data through processing and storing and all the way to analyzing. We’ll take a closer look at the processes:
The first stage of cloud data analytics is data ingestion, meaning data collection. Sourced from various locations—including databases, social media, different internal software solutions like ERP or CRM, IoT devices, and more—data is then consolidated into a unified view.
Using a number of tools and techniques, the cloud platform processes the aggregated data to clean, transform, and filter it. Sorting and transforming data allows data scientists to prepare data for later analysis while minimizing biases and mistakes. Through different methods of processing like ETL and real-time streaming, companies can make sure their datasets are in the proper format to be stored and analyzed.
Cloud storage solutions are impressively scalable, allowing businesses to store virtually unlimited amounts of data without ever worrying about running out of space. Typically, companies store raw and unstructured data in data lakes and structured data in data warehouses.
Finally, it’s time to analyze data and identify valuable insights into the dataset. Using machine learning and predictive modeling, businesses can draw very powerful conclusions and make data-driven decisions. Cloud analytics solutions usually create user-friendly interfaces to allow their customers to perform various analytical efforts like modeling and visualization. Once the data analysis is complete, it’s important that the findings are then shared with the stakeholders and everyone else who needs access to the reports.
Let’s take a closer look at the components that make up the cloud analytics ecosystem.
Data warehouses are large analytical databases that excel at indexing and query optimization, allowing them to store and retrieve information faster and more efficiently. Using an array of techniques, data warehouses separate business data facts from descriptive attributes to improve cloud data analytics capabilities.
The best cloud tools are Google Cloud BigQuery and Snowflake. Google Cloud BigQuery is a serverless enterprise data warehouse created to handle complex querying and cloud data analytics. Similarly, Snowflake is an excellent cloud-based data warehouse with cross- and multi-cloud capabilities and superior scalability and performance.
While on-premise data warehouses can deliver tailored hardware and software integration that fits your organization, they do also require substantial investment and physical space. These prerequisites complicate the scaling capacity and increase costs, which may halt future growth. Cloud warehouses offer on-demand scalability and flexible pricing structures allowing companies to grow seamlessly.
The extract, transform, load (ETL) process acts as an intermediary between data sources and analytics solutions. First, ETL pulls data from different sources, cleans and transforms it, and finally loads it into a storage system. Modern cloud analytics tools offer ETL software with advanced transformation capabilities, error handling, and data quality monitoring to improve the potential for comprehensive cloud data analytics.
Among the top ETL solutions is AWS Glue, a serverless cloud tool that handles complex data preparation and transformation. The platform provides cloud automation for schema discovery and code generation as well as supports batch and streaming data processing. Other competitive cloud analytics solutions are Google Cloud Dataproc and Azure Data Fabric, both offering complete data integration while guaranteeing robust security and compliance.
Much like on-premise data warehouses, traditional ETL systems require additional upfront costs for dedicated servers and software licenses. Such tools lack the flexibility and scalability that cloud-based analytics solutions provide, demanding more careful planning around elevated loads. Companies can rely on on-premise solutions when dealing with sensitive data that cannot land in someone else’s hands—otherwise, a reputable cloud analytics platform can offer superior support for business needs.
Another vital component of cloud data analytics is the data lake, a massive repository for all your data, including raw unstructured datasets. Unlike traditional databases that can only handle certain formats, cloud analytics solutions are equipped to deal with raw, native formats without any transformations. This is due to the so-called schema-on-read approach that enables businesses to first ingest data and only define it when or if the need comes up.
Azure Data Lake is one of the most beloved cloud analytics tools capable of handling large volumes of structured, semi-structured, and unstructured data. Optimized for data-intensive workloads, Azure Data Lake is the number one choice for cloud-based analytics. Although AWS technically doesn’t offer a data lake solution, its Glue service comes with a data catalog that organizes data from various sources with a centralized metadata overview.
In addition to format flexibility, cloud analytics solutions also enable pay-as-you-go pricing models, allowing businesses to take advantage of the data lake capabilities without first expending a fortune on hardware. Cloud computing and data analytics also deliver improved scalability with automatic disaster recovery and replication, taking a load off of your IT team.
The final piece of the puzzle is the business intelligence (BI) tools that analyze data and transform rows of numbers into insightful dashboards, visualizations, and reports. Aiming to democratize cloud analytics, BI solutions empower non-tech employees to generate cloud reporting without relying on technical experts.
The most popular and robust business intelligence software includes Power BI, a Microsoft product that seamlessly integrates with Excel. The tool’s user-friendliness and functionality facilitate complex calculations and codeless data transformations. Companies utilize Tableau, a powerful platform for visual analytics and an intuitive drag-and-drop interface to visualize data.
While the power of cloud computing allows businesses to swiftly perform data analysis even without extensive technical in-house expertise, traditional BI platforms are mostly governed by an IT team, creating hurdles for non-tech business users. On-premise BI solutions require dedicated servers, which in turn demands significant financial and time investments.
In this part, we’ll explore the benefits that working with a cloud data analytics tool will bring to your organization.
Among the most potent benefits of cloud analytics is the inherent scalability of cloud computing. Instead of acquiring additional hardware and setting up physical infrastructure, organizations can solely rely on the cloud analytics platform’s auto-scaling. Not only is this a much cheaper alternative, it’s also a simpler one that doesn’t require a team of in-house experts.
Unlike on-premise infrastructure, cloud-based data analytics can be accessed by anyone from anywhere as long as they have permissions and internet access. Allowing simplified access to a firm’s data analysis fosters collaboration between teams—even those overseas. Additionally, cloud analytics platforms traditionally provide collaboration features like snapshots, comments, tags, and more, allowing teams to partake in discussions remotely.
Furthermore, cloud data analytics provides a more holistic approach to data analysis by easily integrating with other cloud services like monitoring, security, and networking tools.
Inside any cloud data analytics software is a security toolkit with an extensive variety of services like identity and access management (IAM), cloud data security monitoring, threat detection, data encryption, and many others. While traditional servers are managed by an internal team requiring seasoned professionals, cloud data analytics platforms take on the responsibility for end-to-end security themselves.
The ability to entrust your data to a reputable cloud analytics provider also cultivates the system of shared responsibility. Instead of tackling every issue themselves, companies—especially smaller ones—can rely on trustworthy cloud vendors to manage infrastructure, deliver updates, and produce disaster recovery plans.
The culture of shared responsibility typically reduces IT burden, fosters innovation, and improves the security and reliability of services. Delegating infrastructure management to third parties allows agencies to focus on their products and decision making, leading to a more efficient business..
Cloud analytics platforms create a single source of truth by consolidating all essential scattered data into one centralized location. All of your important ERP, social media, marketing, and other information is automatically collected and ingested into a pipeline to transform and analyze it. Having different datasets in one spot allows you to see the full picture without missing any vital aspects.
Another major advantage of data analytics in the cloud is the ability to process data in real time. This allows cloud providers to analyze data on the go and identify patterns without any delays. Real-time data processing and data analysis enable companies to make data-driven decisions in time.
Finally, cloud data analytics is conducive to significant cost savings, especially when compared to classic on-premise setups. Not only do businesses avoid costly hardware and software investments, they also take advantage of the flexible pay-as-you-go model.
Besides expensive initial capital, traditional infrastructure requires ongoing maintenance and updates, which demands an internal team of seasoned tech professionals. Cloud analytics systems take the entire maintenance process on themselves, allowing you to focus on business goals and reduce the cost of cloud computing.
Finally, let’s apply the knowledge we’ve gained about what cloud data analytics is and how it works, and identify tips for selecting the right platform.
Diversity of Choices: Reputable cloud providers offer a variety of options, including hybrid cloud, private cloud, public cloud, and various analytic models. Moving your entire database to the cloud is an expensive endeavor, with AWS migration costs ranging from $5,000 and $100,000 depending on the project. Go for a platform that gives you choice and tailors its services to cover your needs.
User-friendly UI: If you need to hire an expensive expert to use cloud data analytics services, consider choosing a different provider. The best cloud-based data analytics tools are user-friendly and can be easily deciphered by an average business user.
Augmented Analytics: Another vital aspect is augmented analytics, which harnesses the power of artificial intelligence to offer you insights into your data analysis. As opposed to conventional AI features that don’t share their chain of thought, augmented analytics explains its decision making, giving you more control.
Integration Capabilities: The ability to seamlessly integrate with various data sources is imperative for any cloud data analytics tool. From databases, CRMs, and ERPs to external platforms, make sure your cloud provider offers an extensive integration framework.
Robust Security Features: Look for a cloud-based analytics tool that encrypts your data in transit and at rest, offers powerful access management, and launches regular security updates. Additionally, make sure your partner has the following security certifications: SOC 2 Type 2, SOC 3, and ISO27001.
Cloud analytics allows businesses to put their vast data repositories to use and harness important insights. While data analysis can be done using traditional infrastructure, cloud analytics allows for faster, more efficient, and cheaper results. If you’re interested in exploring cloud and data analytics in more detail, reach out to our specialists at NIX. As certified cloud partners, we’ll help you choose the right platform and leverage the power of cloud analytics to reach your goals.
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While data analytics is a broad term describing the process of analyzing data, cloud analytics specifies the usage of a cloud infrastructure. Traditionally, companies use on-premise hardware to capture, store, and study their data, and now they can rely on cloud providers to handle the whole process themselves.
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Cloud analytics tools provide access to an array of security features designed to protect your sensitive data. With solutions like multi-factor authentication, permission control, audit logging, and others, cloud providers guarantee the highest grade of security and compliance.
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Basically, any industry with a lot of information, especially sensitive data, can benefit from relying on cloud data analytics. The primary examples are healthcare, finance, and retail—all industries that depend on real-time processing, predictive capabilities, and personalization.
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A good example of cloud analytics is using AWS consulting services to work out ways to optimize an e-commerce platform. By pulling data from website and social media traffic, sales data, customer reviews, and other sources, a retailer can generate a full-scale report to track its performance in real time. Using predictive analytics capabilities, a retail company can build forecasts, develop an AWS cost optimization plan, and create personalized product suggestions for its clients.
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