Globally, an increasing number of data-driven organizations are using cloud platforms to efficiently utilize their data assets. As per recent data, over 80% of organizations use cloud environments to perform data analytics operations. This rapid growth in adaption is also driven by the importance of cloud data analytics in modern business operations.
One of the key reasons for the rapid growth of cloud data analytics is its automation capabilities. Since all the big data analytics occur on cloud infrastructure, data practitioners don’t have to worry about infrastructural management and focus on leveraging actionable insights.
In this article, we will discuss cloud data analytics in detail, and you will know its benefits, best practices, and tools.
Cloud Data Analytics Benefits
Organizations consider big data analytics on the cloud because they offer many advantages. Some of the key benefits of cloud data analytics are mentioned below:
| Deployment | With cloud data analytics, you can establish a robust analytics infrastructure with a few clicks.
This simplicity makes it more accessible and allows anyone to set up enterprise-level solutions with little expertise. |
| Collaboration | In the cloud, all the analytical solutions are accessible through the Internet, which allows team members to collaborate irrespective of their geographical location.
Professionals can exchange files, manage projects, do version control, and integrate new solutions while collaborating in real time. |
| Scalability | Cloud data analytics allows you to scale up or down resources easily depending on your business requirements.
This scalability enables you to rapidly adjust your data analytics strategy’s capacity and efficiently use computing resources. |
| Cost Efficiency | Most cloud data analytics solutions follow a pay-as-you-go model, eliminating the need for huge investments in hardware and software.
Using this model, you can control the expenditure on resources and allow organizations to optimize infrastructural costs based on actual usage. |
Cloud Data Analytics Best Practices
For organizations aiming to extract meaningful insights from cloud data analytics, it is crucial to adapt its best practices. Here are some of the best practices of cloud data analytics:
- Validate Your Data Analytics Solutions
Since you are using a service provided by cloud vendors, validating analytics solutions before adding them to the data stack is crucial. You need to first verify if the solutions meet expectations and specifications according to the business requirements. Each business has unique needs, including compliance with industry regulations, scalability for handling growth, and the ability to support specific data formats. Defining testing objectives, documenting testing results, and conducting regular reviews are some tasks you can do to carry out this best practice.
- Follow Cloud Security Standards
When it comes to cloud data analytics, security is one of the major concerns. You have to be extra careful to protect your data from unauthorized access, data modification, or loss and comply with required policies and regulations. Some practices to follow cloud security standards include encrypting your data at rest and transit, implementing backup plans, and monitoring cloud activities. SaaS tools like Hevo, by default, take care of cloud security standards by connecting sources to destinations using SSH tunnel, encrypting data with the AES algorithm, and providing HTTPS security.
- Use Additional Tools
Using an end-to-end cloud data analytics platform is sufficient to carry out most of the analytical operations. However, using additional third-party tools or frameworks on top of analytical solutions can streamline certain tasks, which directly affects analytical performance. For instance, using SaaS platforms like Hevo can streamline data integration tasks from various sources to the cloud data analytics platform of your choice. With its intuitive interface and automated workflows, you don’t have to worry about most of the tasks other than data analytics.
- Optimize Data Architecture
The data architecture is the blueprint of how the data is stored, organized, and accessed in the cloud. It directly affects the performance, scalability, and reliability of your data analytics solution. To optimize your existing data architecture, you can follow best practices, such as choosing the right storage solution and processing solutions, applying data partitioning and compression techniques, and considering robust data governance to ensure your data architecture supports your analytical needs.
Best Tools for Cloud Data Analytics
When it comes to data analytics, a plethora of tools are available on the cloud. Here are some of the platforms you can have a look at to carry out analytical operations efficiently:
Databricks
Image Source : Databricks
Created by the developers of Apache Spark, Databricks is a leading cloud-based data analytics solution. Its analytical services include generating visualizations, data discovery, machine learning, and AI.
Key features of Databricks
- Apache Spark integration: Databricks is built on top of Apache Spark, another modern analytics engine used for data processing. With this connectivity, you can leverage the features and capabilities of Spark with the existing functionalities of Databricks.
- Notebook: Databricks offers a notebook interface that supports multiple languages in the same environment, including Python, R, and SQL. Therefore, developers can collaborate on different tasks to streamline data management with languages of their choice.
- Auto Scale: Databricks provides auto-scale capabilities that allow you to adjust resources dynamically based on your business requirements. This ensures efficient use of resources and cost optimization.
Pros of Databricks
- Databricks is a one-stop solution that integrates data processing, machine learning, and collaborative data science.
- Databricks supports multiple programming languages, including Python, Scala, R, and SQL.
- Databricks provides built-in data visualization tools that allow you to create interactive visuals directly in their notebook of the platform.
Cons of Databricks
- If you haven’t used Apache Spark before, Databricks has a huge learning curve.
IBM Cloud
Image Source: IBM Cloud
IBM is a leading analytics solution provider with a suite of tools and services, including predictive modeling, business intelligence dashboards, and cognitive search.
Key tools for analytics in IBM
- IBM Cloud PAK: Cloud PAK is a modular set of integrated software components for data analysis and management. The platform unifies analytics, machine learning, and data management with its containerized environment and cutting-edge features.
- IBM Watson Studio: IBM Cloud provides Watson Studio to streamline analytical operations. It is an IDE for data science and machine learning that helps developers build, manage, and run algorithms to extract insights from data.
- IBM Cognos Analytics: Cognos is a powerful business intelligence tool provided by IBM Cloud. It allows you to create interactive dashboards, reports, and visualizations that facilitate data exploration across organizations.
Pros of IBM
- IBM is best known for its enterprise solutions. It offers services ranging from cloud computing artificial intelligence to data analytics and cybersecurity.
- For organizations with international presence, IBM has a vast network of data centers across the globe.
- IBM takes strong measures on data security to protect organizations from cyber threats. Its services include threat intelligence, identity management, and data encryption services.
Cons of IBM
- Just like Databricks, the learning curve of IBM is steep for users and administrators.
- Achieving seamless integration across third-party solutions may cause challenges because of its unique architecture and API designs.
Alteryx
Image Source
Alteryx is an end-to-end data analytics platform that allows users to prep, analyze, and blend data in a drag-and-drop user interface. The platform is designed to be user-friendly. Therefore, anyone with little or no programming expertise can quickly construct, execute, and publish data analytics processes.
Key features of Alteryx:
- Spatial Analytics: Spatial analytics is a unique feature of Alteryx. It allows users to perform location-based analysis and geospatial processing. This is crucial for many industries, including logistics, retail, and urban planning, that rely heavily on spatial insights for decision-making.
- Automation and Orchestration: Using Alteryx, you can automate repetitive tasks, create reusable workflows, and orchestrate end-to-end data analytics processes. This ensures data consistency and integrity while saving time and resources.
- Data Governance: Alteryx offers robust data governance features, such as audit trails, version control, and access controls, to maintain data quality and adhere to organizational standards.
Pros of Alteryx
- Alteryx features an easy-to-use drag-and-drop interface for users with different backgrounds to work with data.
- Alteryx supports advanced analytics and predictive models by seamlessly integrating with popular statistical and machine learning libraries such as R and scikit-learn.
Cons of Alteryx
- Alteryx struggles with huge datasets due to its reliance on in-memory processing.
- While Alteryx is excellent in data blending, its data visualization capabilities are not as advanced as other platforms like Tableau or Power BI.
Conclusion
Data analytics with cloud solutions are often superior to the on-premise options. It has profoundly impacted the landscape of business analytics. With its cutting-edge features like rapid deployment, collaborative workspace, scalability, and enhanced security, cloud data analytics ensures that data is more accessible, secure, and used efficiently. By following the best practices and tools mentioned above, you can make the most out of cloud data analytics.
But as the volume of data increases, you need a permanent solution for tackling data silos and making data-driven decisions through data analytics. Here’s where Hevo Data, our automated data pipeline platform can help you.
Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Do check out the pricing details to understand which plan fulfills all your business needs.
Tell us about your experience of completing the process to import Excel into MySQL! Share your thoughts with us in the comments section below.
Jalaj has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.