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.
What is Cloud Data Analytics?
Cloud data analytics refers to analyzing large datasets using cloud-based platforms and tools. It enables businesses to store, process, and analyze data on scalable infrastructure without managing physical hardware.
Hevo simplifies cloud data analytics by automating the process of extracting, transforming, and loading (ETL) data from multiple sources into cloud-based platforms for analysis. Here’s how Hevo helps:
- End-to-End Automation: Automates the ETL process for efficient, error-free data migration, improving analytics capabilities.
- Real-Time Data Streaming: Moves data from diverse sources to cloud warehouses like Snowflake, BigQuery, and Redshift with zero lag.
- No-Code Data Pipeline: Offers a user-friendly interface for seamless integration without writing code.
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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:
- Scalable Infrastructure: Choose scalable cloud-based solutions that can handle growing data volumes and analytics demands.
- Centralized Data Platform: Use a unified platform to manage and access all data sources for comprehensive insights.
- Data Governance: Establish strict protocols for data accuracy, security, and privacy to ensure reliable analytics.
- Automated Data Pipelines: Streamline the data flow process with automated tools to improve efficiency and reduce manual errors.
Discover the key differences between Data Analytics and Data Analysis to better navigate cloud-based analytics tools and practices.
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.
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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
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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.
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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.
FAQ
What do you mean by cloud data?
Cloud data refers to digital information that is stored, managed, and accessed via remote servers hosted on the internet, rather than on local devices or servers.
What is the meaning of data analytics?
Data analytics involves examining datasets to draw conclusions, identify patterns, and make informed decisions through statistical and computational techniques.
What are the 5 types of data analytics?
Descriptive Analytics: Summarizes past data.
Diagnostic Analytics: Explains why events happened.
Predictive Analytics: Forecasts future trends.
Prescriptive Analytics: Suggests actions to achieve outcomes.
Cognitive Analytics: Mimics human thinking using AI/ML.
Jalaj has over four years of extensive experience in technical writing within the data industry. He is passionate about simplifying the complexities of data integration and data analysis, crafting informative content that aids those delving deeper into these subjects. Through his work, Jalaj aims to make sophisticated data concepts accessible and understandable, empowering readers to enhance their knowledge and skills.
