The digital economy is fueling the growth of new data creation leading to finding better solutions for better storage, management, and analysis

There has been an exponential growth of data creation and usage in the recent past due to the emergence of the Digital economy. The growth of data in recent years has been manyfold. Here are some key statistics highlighting this trend. For the sake of analysis, we are taking some numbers from global data volume, data creation per day, data in the cloud, internet users, social media data, and data due to IOT devices. A popular saying in the big data world is that data is the new oil.

In 2020, the world generated approximately 64.2 zettabytes (ZB) of data. This volume is expected to reach 175 ZB by 2025, representing a compound annual growth rate (CAGR) of 27%. By 2020, it was estimated that 1.7 MB of data was created every second for every person on Earth. As of 2021, over 50% of corporate data is stored in the cloud, up from 30% in 2015. The number of internet users has significantly contributed to data growth.

As of January 2023, there were over 5.16 billion internet users worldwide, accounting for 64.4% of the global population. Social media platforms are major contributors to data creation. For instance, Facebook users upload more than 300 million photos and send over 500,000 comments every day. The proliferation of Internet of Things (IoT) devices has fueled data growth. By 2025, it's predicted that there will be over 75 billion IoT devices worldwide, generating vast amounts of data.

These statistics illustrate the rapid increase in data generation, driven by digitalization, increased internet usage, the rise of IoT, social media platforms, and the rapid adoption of mobile applications.


Reasons for Transition from traditional data warehousing to big data in the recent past
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The transition from traditional data warehousing to big data technologies has been driven by several key factors and developments:

• Volume, Velocity, and Variety of data have been instrumental in transitioning the old paradigm of data to a new concept of big data.

The sheer amount of data being generated today far exceeds the capacity of traditional data warehouses. Big data technologies, such as Hadoop and NoSQL databases, are designed to handle massive volumes of data.
The speed at which data is generated and needs to be processed has increased dramatically. Traditional data warehousing systems struggle with real-time or near-real-time data processing, whereas big data technologies are designed to handle high-velocity data streams.

Data now comes in many forms, including structured, semi-structured, and unstructured data (e.g., text, images, videos). Traditional data warehouses are optimized for structured data, but big data technologies can handle a wider variety of data types.

• Cost of storage of data is decreasing and the Scalability and capacity of data storage are increasing

Traditional data warehouses are often expensive to scale due to their reliance on proprietary hardware and software. In contrast, big data solutions leverage commodity hardware and open-source software, making them more cost-effective and easier to scale horizontally. Cloud computing has further facilitated the scalability and cost-efficiency of big data solutions. Cloud providers offer scalable storage and processing power that can be adjusted based on demand.


• Advanced Analytics is made possible with the help of advanced tools and scalability and processing speed offered by cloud computing

The rise of advanced analytics, including machine learning and artificial intelligence, has necessitated the use of big data platforms that can efficiently process and analyze large datasets. These technologies require substantial computational power and flexible data processing capabilities that traditional data warehouses cannot provide. Big data platforms also support complex analytics and data mining, enabling organizations to derive deeper insights and predictive analytics.


• Real-time data Processing is essential in preventing fraud and facilitating online operations

Big data technologies enable real-time data processing and analytics, which are crucial for applications such as fraud detection, real-time recommendations, and dynamic pricing. Traditional data warehouses, typically designed for batch processing, are not well-suited for these use cases. Banking, financial services, and telecom sectors are the largest beneficiaries of real-time data processing and data analytics.


• The concept of Data Lakes has emerged which requires sophisticated tools for storage and analysis

The concept of data lakes has emerged as a key component of big data architecture. Data lakes allow organizations to store raw data in its native format until it is needed. This flexibility contrasts with the rigid schema requirements of traditional data warehouses and allows for more agile and exploratory data analysis. Sometimes, structured and unstructured data together. Requires a complex understanding of data analytics tools. Some of the leading cloud computing platforms such as Amazon, AWS, Microsoft, Azure, and Google cloud platforms are bundled with these advanced data handling and analytical capabilities.


• Integration and Interoperability of data have always been a challenge in data analytics, which is being solved by cloud computing platforms

Big data platforms often integrate more easily with various data sources and tools, providing better interoperability across different systems and datasets. This integration capability is essential for organizations that need to consolidate data from disparate sources.


• Industry Adoptions and Use Cases of big data analytics are increasing at a very fast and rapid pace

Many industries, such as banking, insurance, healthcare, finance, retail, and telecommunications, have adopted big data technologies to address specific use cases like personalized marketing, risk management, patient care optimization, and network performance monitoring. Overall, the shift from traditional data warehousing to big data solutions has been driven by the need to handle larger, faster, and more diverse datasets, along with the demand for real-time processing, advanced analytics, and cost-effective scalability. This transition has enabled organizations to harness the full potential of their data for strategic decision-making and innovation.

Benefits, which Cloud Computing platforms are offering for big data analytics

Using cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) for big data analytics offers numerous benefits and addresses several challenges faced by traditional on-premises solutions. Here are some key benefits and challenges these platforms solve:

• Cloud computing platforms can easily help you scale up or scale down at any time without worrying about large capital expenditure and offer Flexibility in management and operations.
Cloud computing platforms provide on-demand scalability, allowing businesses to easily scale their storage and computing resources up or down based on their needs. This elasticity is crucial for handling varying data loads without the need for significant upfront investment in infrastructure. Cloud computing services offer a wide range of tools and services that can be easily integrated and customized to meet specific analytics requirements.


• Cloud computing platforms help reduce the cost of ownership of big data analytics and bring efficiency
Cloud providers use a pay-as-you-go pricing model, which means businesses only pay for the resources they use. This reduces the need for large capital expenditures on hardware and software. Cloud computing platforms manage the underlying infrastructure, reducing the need for in-house IT maintenance and support.

• Cloud computing platforms bring in advanced Analytics and Machine Learning capabilities and algorithms to big data analytics

Cloud computing platforms provide a suite of advanced analytics tools and services, such as AWS Glue, Azure Synapse Analytics, and Google Big Query. These tools simplify data processing and analysis. Cloud computing Platforms like AWS SageMaker, Azure Machine Learning, and Google AI Platform offer integrated machine learning services that make it easier to build, train, and deploy machine learning models.


• Cloud computing platforms help in easy data Integration and Management of the extraction transformation and loading process
Services like AWS Lake Formation, Azure Data Lake, and Google Cloud Storage allow organizations to create data lakes and warehouses that can store vast amounts of structured and unstructured data. Cloud platforms offer tools for Extract, Transform, and Load (ETL) processes and data pipelines, such as AWS Data Pipeline, Azure Data Factory, and Google Cloud Dataflow, facilitating seamless data integration from various sources.


• Cloud computing platforms help in global Accessibility and Collaboration amongst multicounty and multi-location, large teams spread across the world

Cloud computing platform providers have data centers around the world, ensuring high availability and low latency for users regardless of their location. Cloud storage and computing platforms enable real-time collaboration and data sharing among teams across different geographies.

• Cloud computing platforms offer better Security and Compliance of data storage as per increased regulatory compliances in a truly

Cloud platforms offer robust security features, including encryption, identity and access management, and compliance certifications, ensuring data protection and regulatory compliance. Regular security updates and patches are automatically applied by the cloud provider, reducing the risk of vulnerabilities.

The business challenges that cloud computing platforms are solving for big data analytics
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• Frees up your IT Resources for better strategic projects: Cloud computing platforms handle the complex management of physical infrastructure, including servers, storage, and networking. This alleviates the burden on in-house IT teams and allows them to focus on more strategic initiatives.

• Faster query handling and reporting: Cloud computing platforms provide high-performance computing resources that can process large datasets quickly and efficiently. This addresses the challenge of slow data processing times often encountered with on-premises solutions.

• Avoiding downtime, due to non-availability of resources: Automated resource provisioning in the cloud ensures that the necessary computer and storage resources are available when needed, preventing bottlenecks and downtime.

• Integration challenge with different platforms, which do not talk to each other directly: Cloud computing platforms offer extensive APIs and integration capabilities, enabling seamless interoperability with various data sources, third-party tools, and enterprise systems. This resolves the challenge of siloed data and fragmented IT environments.

• Avoid business losses due to faster recovery of data in case of any disaster: Cloud computing platforms provide robust disaster recovery and backup solutions, ensuring business continuity and data integrity in case of failures or disasters. This eliminates the need for complex and costly on-premises disaster recovery setups.

Increased demand is also leading to a shortage of skills in big data analytics, and cloud computing platforms

The fields of cloud computing and big data analytics are rapidly growing, leading to a high demand for skilled professionals. However, this demand has outpaced the supply of qualified candidates, resulting in a skills shortage. Here are some insights into the skills shortage and the types of jobs available in these fields:
These cloud computing and big data analytics Skills are facing global Shortage and huge demand in global Corporation

• Proficiency in major cloud platforms such as AWS, Azure, and GCP is essential. Skills in cloud architecture, cloud security, and cloud migration are particularly in demand. Knowledge of big data frameworks and tools like Hadoop, Spark, Kafka, and NoSQL databases is crucial. Additionally, expertise in data warehousing solutions like Amazon Redshift, Google Big Query, and Azure Synapse Analytics is highly valued.

• Skills in data cleaning, transformation, and analysis using tools like SQL, Python, and R are necessary. Familiarity with data visualization tools such as Tableau, Power BI, and D3.js is also in demand. Proficiency in machine learning algorithms, frameworks (e.g., TensorFlow, Py Torch), and platforms (e.g., AWS Sage Maker, Azure ML, Google AI Platform) is increasingly sought after.

• Knowledge of cloud security best practices, identity and access management (IAM), encryption, and compliance frameworks (e.g., GDPR, HIPAA) is critical. Understanding data governance principles and practices, including data quality, metadata management, and data lineage, is important for ensuring data integrity and compliance.

• Skills in DevOps practices and tools such as CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code (Terraform, Ansible) are essential for efficient cloud operations. Proficiency in automating cloud infrastructure deployment, monitoring, and management using scripting languages and cloud-native automation tools.

How to address the Skills Shortage in the field of cloud computing and big data analytics

• Getting Certifications from AWS, Azure, GCP, and other relevant partners and IT training organizations such as Jetking can help bridge the skills gap. Programs like AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, and Google Professional Data Engineer are valuable.

• Conducting Intensive training programs and organizing boot camps, webinars & online courses focusing on cloud computing, big data technologies, and machine learning can rapidly upskill professionals.

• Companies should invest in training programs for their existing employees to build the necessary skills in cloud computing and big data analytics. Establishing mentorship programs and offering internships can help develop the next generation of skilled professionals.

• Partnerships between industry and academia can ensure that curricula are aligned with current industry needs, producing job-ready graduates. Engaging with professional members and developer associations and attending industry conferences can help professionals stay updated with the latest trends and technologies.

Conclusion with regards to cloud computing and big data analytics

Cloud computing platforms like AWS, Azure, and GCP provide significant advantages for big data analytics, including scalability, cost efficiency, advanced analytics capabilities, and enhanced security. They also address critical challenges related to infrastructure management, data processing speed, resource provisioning, and disaster recovery, making them an essential component of modern data analytics strategies. So, if you’re planning to get a better ROI from your data analytics projects, it is recommended to use the latest cloud computing and storage platforms with advanced analytical capabilities for your organization.

Also In conclusion, while there is a significant skills shortage in cloud computing and big data analytics, there are numerous opportunities for professionals with the right skills. Addressing this shortage requires a concerted effort from educational institutions, employers, and professionals themselves to invest in relevant training and certification programs. In case you wish to Upskill yourself or your team within your organization, Jetking offers a large number of programs in the area of cloud computing and big data analytics. Please refer to our all-programs page or speak to the Jetking admission counselor by dialing 07666830000

References for further study

1. IDC (International Data Corporation)

2. Statista

3. Domo's "Data Never Sleeps" Report

4. Gartner

5. Hootsuite and We Are Social’s Digital 2023 Report

6. Facebook Company Info

7. https://www.linkedin.com/pulse/cloud-computing-big-data-analytics-services-dinesh-thorat

8. https://datafortune.com/the-role-of-cloud-computing-in-big-data-analytics-services/

9. https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00301-w

10. https://www.geeksforgeeks.org/difference-between-cloud-computing-and-big-data-analytics/

11. https://www.chitkara.edu.in/blogs/understanding-big-data-analytics-in-cloud-computing-latest-trends-challenges/

12. https://www.redswitches.com/blog/big-data-analysis-in-cloud-computing/