Data Analytics is on the Rise & Cloud is Driving it Crazy…

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Interesting Facts about Data

  • In 2020, 1.7 MB of data was created per second for every person on earth (Source: Tech Jury)
  • In 2022, 97 zettabytes (1 zettabyte = 1000 quintillion bytes) of data will be created, captured, copied, and consumed worldwide (Source: Statista).
  • Google Drive boasts 94.44% as the most used cloud storage service in the world, followed by Dropbox (62%) and OneDrive (39%). (Source: Fortune Business Insights)
  • It will take 181 million years to download all the data from the internet. (Source: Org)
  • 60% to 73% of data gets left out from analytics purposes (Source: Forrester)
  • Poor data quality costs organizations around 9.7 million USD every year (Source: Gartner).
  • Big Data is set to reach 273.4 billion USD by 2026 (Source: MarketsandMarkets).
  • Around 36% of the organizations consider big data vital to their business operations. (Source: Dresner)
  • Exploiting big data analytics in the retail industry, could boost profits for businesses by 60%. (Source: McKinsey)
  • 97.2% of the companies are investing into AI and big data. (Source: New Vantage Partners)

We belong to the world of instant gratification, with almost all the information we want right at our fingertips. How is this made possible? It is done through the demand and supply of digital data via complex cloud networks invisible to the naked eye, and sophisticated hardware that fits right into the palm of our hands. Data is no more the ‘new’ oil. It is ‘the essential oil’ which feeds the very fabric of our society to function day-to-day. 

Merely holding on to data doesn’t fetch results. While collecting data is important, it is also important for firms to forecast business outcomes by understanding customer behavior, identifying & tapping new opportunities, observing the market trends closely, etc. Data analytics is the primal reason behind the growth and success of countless firms.

In the course of their regular operations, businesses generate terabytes of data. The majority of this data, which comes from websites, social media, IT devices, and financial software, is now stored in the cloud. Cloud analytics software and tools are very effective in processing large data sets, producing insights in simply consumable formats, and creating insights from data in the cloud that is available on-demand, resulting in a better and more simplified user experience.

We shall discuss how cloud data analytics has revolutionized the way organizations work with data and how they make data-driven decisions. We shall also discuss the top cloud service providers – AWS, Azure, and GCP, and how they dominate the worldwide cloud data analytics market.

In this blog, we will cover:

  • What is (Big) Data?
  • What is Data Analytics?
  • How does Data Analytics work?
  • Why Data Analytics?
  • What are the different types of Data Analytics? 
  • Popular Tools for Data Analytics
  • Some use cases of Data Analytics 
  • Limitations of the Data Analytics Tools
  • Top Trends in Big Data Analytics in 2022 
  • Advantages of Cloud Data Analytics 
  • Getting Started with Cloud Data Analytics
  • Top Cloud Computing Solutions for Data Analytics
  • Comparison of different Cloud Platforms for Data Analytics
  • Conclusion

What is Data?

What is Data

Data can be anything, ranging from abstract concepts to real measurements, including statistics. Technically, data can be regarded as building blocks of factual information that can be used for reasoning, discussion, or calculation. 

In the world of computing, digital data, or simply – “data”, refers to information that has been converted into a format that is easy to move or process. Digital data can be measured, gathered, reported, analyzed, and also manipulated to create data visualizations such as graphs, tables, and images. 

Data can be stored in different file formats like ISAM and VSAM for the mainframe systems. Other formats include CSV for data storage, conversion, and processing. They are stored in the form of binary numbers or bits. 8 bits of data make a byte. And as the data continues to get collected, the units grow to megabytes, gigabytes, and so on.

Data can be divided into two categories: Primary and secondary data. Primary data is collected at the place of interest in databases like digital libraries, web pages, and web documents, whereas secondary data lies in some archived mediums including and not limited to, scientific journals and newspapers such as the National Geographic Journal or the Washington Post.

Why Data?

Why Data

The prevalence of the terms “data processing” and “electronic data processing,” came to encompass the full scope of what is now known as information technology, demonstrating the importance of data in business computers, early on. 

Data is a powerful asset in the hands of any business. It enables them to make data-driven decisions and create strategies for their business like identifying new opportunities. Data can be collected from various sources, such as social media, customer surveys, mobile applications, and more. This data can then be analyzed to find patterns and trends that will help businesses make smarter decisions. It can help a company answer a myriad of questions like “What are our customers doing on our website?” or “Should we spend more money on marketing?”

The data that a company has can also tell us if they are meeting its goals. For example, if a company wants to increase sales by 10% over the next year, they will need to know how much they sold last year in order to measure their progress.

Data is important because it is the foundation for a lot of decisions in life. It helps us make better decisions and understand what is happening in the world around us. Data can help us better understand our health, our environment, and even other people.

What is Big Data?

Over the last decade, the rise of the internet and smartphones has resulted in a surge in digital data creation. Large amounts of data that can’t be stored, processed, or analyzed using traditional methods are referred to as Big Data. Today, there are millions of data sources that deliver data in a timely manner. These data sources can be found all around the world. Social media platforms and networks are some of the most important data sources. Consider Facebook, which generates 4 petabytes (1 million gigabytes) of data per day (Source: Kinsta). Pictures, videos, texts, and other types of information are included in this data.

Data in the petabyte or larger range has been referred to as “big data.” The 5Vs are a simplified depiction of big data that includes velocity, volume, value, variety, and veracity. Big data-driven business models have evolved as web-based e-commerce has increased in popularity, recognizing data as a valuable asset in and of itself. As a result of these changes, there is now a greater emphasis on data privacy and social applications of data.

5 V's of Big Data

Big Data Challenges

Big Data Challenges

What is Data Analytics?

Data Analytics (DA) is the process of evaluating data sets in order to detect trends and develop conclusions about the information they contain. Data analytics is increasingly being done with the help of sophisticated tools and software. In commercial industries, data analytics technology and methodologies are frequently employed to help organizations make better business decisions. Data Analytics tools are also used by scientists and researchers to validate or reject scientific models, theories, and hypotheses.

Data analytics is a broad phrase that encompasses a wide range of applications, including basic business intelligence (BI), reporting, and online analytical processing (OLAP), as well as advanced analytics. In this way, it’s similar to business analytics, which is another umbrella word for data-analysis approaches. The distinction is that business analytics is focused on business applications, whereas data analytics is more general. However, this broad interpretation of the phrase isn’t universal: some individuals use data analytics to refer to advanced analytics alone while viewing business intelligence as a distinct category.

Businesses can use data analytics to boost revenue, improve operational efficiency, enhance marketing campaigns, and improve customer service. Organizations can also use analytics to adapt rapidly to emerging market trends and acquire a competitive advantage over their competitors. However, the ultimate purpose of data analytics is to improve business performance. Depending on the application, the data that is assessed can be historical records or new information that has been processed for real-time analytics. It can also come from a combination of internal and external data sources.

Today’s businesses depend on data insights. As shown in the below image, hence, if no data analytics, then…

What is Data Analytics?

How does Data Analytics work?

Data analytics is not just about analyzing data. It is also about collecting, integrating, and preparing data, as well as designing, testing, and refining analytical models to guarantee that they give reliable findings. Apart from data scientists and other data analysts, the analytics team also includes data engineers. They are responsible for designing data pipelines and helping with the preparation of datasets for analysis. 

The entire process begins with the collection of data. Data scientists first identify the kind of data they require for their analytics application. Then they compile it by themselves, or with data engineers and IT professionals. Data from varied sources may need to be merged using data integration techniques, converted into a common format, and then put into an analytics system such as a Hadoop cluster, NoSQL database, or data warehouse.

In other circumstances, the data collection process may entail extracting a relevant subset from a stream of data flowing into Hadoop, for example. The data is then migrated to a different partition in the system, where it could be analyzed independently, without affecting the overall data set.

Once the necessary data is in place, the following step is to identify and correct data quality issues that may compromise the accuracy of analytics applications. This includes doing data profiling and data cleansing procedures to guarantee that the data in a data set is consistent and that errors and duplicate entries are deleted. Additional data preparation work is completed in order to alter and organize the data in preparation for the planned analytics application. The data is then subjected to data governance regulations to ensure that it complies with industry standards and is effectively utilized.

A data scientist then uses data analytics tools using computer languages like Python, Scala, R, and SQL to create an analytical model. Usually, a partial data set is first used to assess the model’s correctness, after which it is altered and tested again as needed. This is referred to as “training” the model until it performs as expected. Finally, the model is run against the entire data set in production mode, which can be done once to address a specific information need or on an ongoing basis as the data is updated.

Analytics software can even be set up to automatically trigger business actions in some instances. A financial services firm’s stock trading is an example. The final step of the data analytics process is the communication of the results generated by analytical models to top management and other end users. Charts and other infographics can be used to help people understand what they’re seeing. Data visualizations are usually integrated into BI dashboard systems that present data on a single screen and can be updated in real-time as new information becomes available.

Why Data Analytics?

Why is Data Analytics?

Big data analytics studies a lot of data to discover hidden patterns, correlations, and other variables. With today’s technology, you can analyze your data and get answers almost instantly – faster and more efficiently compared to traditional BI solutions. This helps organizations harness their data to identify new opportunities, leading to better strategies, increased profits, and happier customers. 

Some of the key reasons why data analytics is important to include:

  • Cost-cutting: When it comes to storing massive amounts of data, big data technologies like cloud-based analytics can significantly decrease costs (for example, a data lake). Additionally, big data analytics assists firms in identifying more efficient operational ways.
  • Faster and better decision making:  In-memory analytics, when combined with the ability to evaluate new sources of data, such as streaming data from IoT, allows businesses to analyze information quickly and make well-informed decisions.
  • Developing new products: Data Analytics allows organizations to gauge customer needs and satisfaction, empowering them to provide customers with exactly what they want, when they want it. It helps organizations build innovative new products to meet the changing needs of customers.
  • Targeted Marketing: Knowing what customers want ahead of time makes marketing campaigns more customer-focused. It allows businesses to tailor their advertisements to target a specific segment of their customer base. It also aids them in determining which customer segment would respond best to the campaign.
  • Operational Efficiency: Data analytics can also assist businesses in identifying fresh opportunities to optimize operations or raise profits. It helps in the detection of potential risks, alleviating the need to wait for them to arise before taking action. This enables businesses to determine which operations delivered the best overall outcomes under varied conditions, as well as discover which operational areas are prone to errors and need to be addressed.
  • Bounce Rates: The bounce rate of a website is a piece of information that each online business should be concerned about. Lower bounce rates suggest that clients who are interested in the company are serious about learning more about its services. Higher bounce rates, on the other hand, indicate that something has gone wrong. It means that neither Google nor any other search engine is able to determine the relevancy of a business website.

What are the different types of Data Analytics? 

Data Analytics is typically classified into 4 separate categories:

Different types of Data Analytics? 
  • Descriptive analytics: Descriptive analytics is a basic, high-level sort of analysis that accounts for what happened in the past. The two basic strategies used in data analysis are data aggregation and data mining—that is, the professional first gathers the data and presents it in a summarized format (the aggregation component), and then “processes” the data to find patterns. This helps with reporting – the company’s income, profit, and sales, among other things.

For example, the Dow Chemical Company examined historical data in order to improve facility use in its offices and laboratories. Using descriptive analytics, Dow was able to find the underutilized areas. As a result of the space consolidation, the company was able to save almost $4 million per year. This helps with reporting a company’s income, profit, and sales, among other things. It also helps with the social media metric tally.

  • Predictive analytics: It is a sort of advanced analytics that uses current and past data to forecast activity, behaviors, and trends. It uses data mining techniques, and artificial intelligence with machine learning to make predictions about the future. It is the most popular type of data analytics.

For example, PayPal evaluates what measures they need to exercise to protect its customers from fraudulent transactions. The company employs predictive analytics to construct an algorithm that detects fraudulent behaviors based on all historical payment data and user behavior data.

  • Diagnostic analytics: It is vital for corporate success to identify the underlying causes of trends and events. Diagnostic analytics is designed to aid firms in analyzing the data to understand probable causes driving trends and anomalies, such as a sudden loss in revenue, a change in client behavior, or an increase in expenses. It includes drill-down, data mining, and data recovery techniques. 

For example: A report from an e-commerce company details that sales have decreased, despite users adding items to their shopping carts. This could be due to a number of issues, including a form that failed to load, an excessively high shipping price, or a lack of payment options. Diagnostic analytics can help you find out what’s going on in this situation.

  • Prescriptive analytics: Prescriptive analytics is the process of analyzing data in order to determine the best course of action. This type of analysis generates ideas for the next steps by taking all things into consideration. Perspective analytics employs both descriptive and predictive analytics.

For example, prescriptive analytics can be used to increase an airline’s profit margins. This type of data is utilized to create an algorithm that adjusts airline tickets automatically based on a variety of criteria such as client demand, weather, destination, holiday seasons, and oil prices.

Data Analytics types with Complexity 

Top 10 Big Data Applications in 2022

  • Medical Care: One issue that many hospitals have to confront is adapting to economic pressures while treating as many patients as possible, and finding ways to improve the quality of medical care. Data Analytics can be used to improve and track treatment, patient flow, and equipment utilization in hospitals, using machine and instrument information.
  • Accounting: The EMI plans didn’t just appear out of nowhere. Such schemes are significant products of Data Analytics. Data analytics, which plays a key role in accounting, is used to gain knowledge about significant insights in finance and the various types of modifications that can be made to increase productivity and deal with risks that crop up. Data analytics are used by bookkeepers to build strong relationships with a variety of business leaders. As a result, data analytics encourages the gathering of information to improve finance. Bookkeepers get to optimize their financial decisions by better analyzing gains and earnings.
  • Risk Detection and Exploitation: One of the basic applications of data analytics derived from the world of finance is this. Countless businesses have had dreadful debt experiences and have quite literally become fed up with it. Many used data analytics to save themselves from calamities by gathering past data at the time their consumers requested credit. This encouraged banks to devise a strategy for dividing and acquiring data from their clients’ profiles, recent expenditures, and other vital information that had been made available to them. This made it simpler for them to assess and determine whether there was a risk of clients defaulting.
Top 10 Big Data Applications in 2022
  • Digital marketing: Digital marketing has also been transformed by data analytics. Most advertisement sources presently use data analytics and data algorithms, including digital billboards in cities and banners on websites. It’s one of the reasons why digital ads earn higher click-through rates than traditional ads. 
  • Planned cities: City planning is one of the few areas where data analytics hasn’t taken off yet. While many city planners are skipping on using data analytics to their advantage, it is leading to ill-planned cities that get clogged with traffic. Data analytics could aid in improving accessibility and reducing congestion in the city. Eventually, the planning process will become more efficient. Using data analytics and modeling, it will be easier to predict the outcome of constructing any structure and plan accordingly.
  • Personalized in-store experience: The use of data analytics can offer a new perspective on customer behavior. To make personalized recommendations and connect with customers better, retailers often provide their sales executives with point-of-sale devices. Retailers will be able to highlight merchandise efforts that cater to their customer’s tastes and budgets by understanding customer preferences. Retailers gain a competitive advantage when their customers receive highly personalized services.
  • Inventory Management: Data Analytics helps in predicting the future demand and thereby reduces the amount of stock that is needed. Supply Chain Optimization is achieved by using data analytics and predictive modeling to optimize the logistics flow of goods, services, and information. Vendor Management can be optimized by making strategic decisions on how to manage vendors, such as selection, pricing, incentives, and negotiation tactics.

Popular Data Analytics Tools

Popular Data Analytics Tools

Limitations of Data Analytics Tools

Limitations of Data Analytics Tools

Top Trends in Big Data Analytics in 2022 

Top Trends in Big Data Analytics in 2022 

Cloud Data Analytics

With all of the advantages that big data analytics has to offer, much of their potential is wasted because employees do not have timely and reliable access to the data. According to Gartner, 85 percent of Fortune 500 firms aren’t getting the most out of their big data analytics because data isn’t easily accessible, allowing them to miss out on possibilities to better engage with and meet customer demands. Data analysis becomes more available as analysis goes to cloud drives because company employees may access company information remotely from any location, liberating them from being tethered to local networks and therefore making data more accessible.

Similar to on-premises data analytics, cloud data analytics is simply the process of using analytic algorithms in the cloud to analyze data in a private or public cloud in order to deliver the desired result. Cloud analytics entails the use of scalable cloud computing in conjunction with advanced analytic software to uncover patterns in data and drive new insights.

Cloud Analytics is often associated with Artificial Intelligence, Machine Learning, and Deep Learning. Organizations of all sizes can swiftly make data-driven decisions to improve the efficiency of their products and services by leveraging AI and other analytics technologies. The cloud is a critical platform for quickly testing ideas via proofs of concept (POCs) and providing a rich software environment for developing AI applications and training DL models. AI is increasingly being applied to address essential corporate goals such as automating corporate operations, giving cognitive insights through data analysis, and connecting with customers through natural language processing (NLP) in a variety of industry verticals. The next level of ML, which is DL, is capable of learning from enormous amounts of data in order to imitate the human brain’s pattern recognition abilities (for example, images, speech, and text).

Cloud analytics tools and software are especially effective at processing large data sets, delivering insights in easily digestible formats on demand, resulting in a better and more simplified user experience.

Advantages of Cloud Data Analytics

Cloud data analytics opens up several doors for organizations. The following image shows some of the most impactful benefits it has to offer: 

Advantages of Cloud Data Analytics
  • Data Consolidation: It’s practically impossible to gain a unified perspective of big data collected from various, different sources across the enterprise. Cloud analytics combines all of a company’s data sources to create a more comprehensive picture. All stakeholders may quickly access this data in one place, regardless of their physical location (or the data’s location), to acquire more accurate insights and make better business decisions in real-time.
  • Sharing and collaboration: Silos of big data in individual departments, such as Finance or Human Resources, have an impact on the entire company. A cloud analytics system can better incorporate data from various sections of the company, subject to role-based access rules that can be configured, resulting in improved communication and decision-making.
  • Scalability: When workloads and data volumes increase significantly, administrators using on-premise platforms must purchase and install new hardware to meet the increased demand – a service model that frequently leads to overprovisioning and extra costs if demand drops in the future. Organizations can scale up to meet demand surges by bringing more instances online (or removing them when demand drops) and paying only for what they use using cloud analytics services.
  • Cost-cutting: In addition to the expenditures of varying hardware requirements, on-premise platforms necessitate periodic upgrades and migrations, which usually result in system downtime, which has a negative impact on business continuity. On-premise analytics also necessitates specialized skill sets that some firms lack or cannot afford. Organizations can avoid purchasing and supporting additional gear by using cloud analytics, and they can also take use of service providers’ in-house expertise.
  • Security: Security monitoring is typically only one of several responsibilities for an organization’s IT team, but it is a full-time responsibility for cloud hosts. Cloud analytics companies also utilize strong encryption to protect data as it travels over networks. However, the fact that the data is held off-site may be the most significant security benefit they provide – Insider threat actors, such as current and former workers who take confidential or proprietary information with them when they leave the organization, were found to be responsible for 34% of all breaches, according to a recent report.

Getting Started with Cloud Data Analytics

All cloud analytics platforms have something different to offer relatively. Hence it is best to first identify your company’s specific needs before purchasing any cloud analytics service. Some of the key factors to keep in mind include:

  • Scalability: A great cloud analytics platform would be able to address your evolving business needs and support its growth. Many services come with flexible pricing options allowing organizations to pay only for the resources they use.
  • Security: While almost all cloud services encrypt data when it flows across a network, many do not protect data while it is in storage. Look for a platform that encrypts data both “in transit” and “at rest”.
  • Real-time integration: Your cloud analytics platform should interact in real-time with the rest of your organization’s systems, allowing you to keep up to date with minimal effort.
  • Analytics features: Each company follows its own unique business metrics. Select a platform capable of calculating your organization’s metrics.
  • Responsive interface: A bulk of users run data analysis on mobile devices nowadays. So whatever the device be, make sure the platform performs well across the devices the user is comfortable operating with.

Many organizations today increasingly resort to multi-cloud (using multiple cloud providers) and hybrid cloud (combination of private and public cloud infrastructures) approaches to meet the changing business needs. However, this also comes with concerns over data compliance, data governance, and security.

Top Cloud Computing Solutions for Data Analytics

The world of business software is always changing, and cloud-based business intelligence solutions are no exception. The best cloud analytics systems, particularly those that can scale quickly to accommodate a company’s evolving business models, are rapidly replacing on-premise reporting solutions, and it’s easy to see why. Cloud solutions enable easy-to-customize user interfaces, prototyping tools, and self-service BI that legacy systems lack.

Companies of all sizes are faced with the difficulty of scaling their business models while maintaining responsive and efficient operations. As a result, cloud-based analytics solutions are beginning to bridge the gap between older systems’ capabilities and what modern businesses require to compete and grow.

Top 3 Cloud Platforms for Data Analytics

Top platforms like AWS, Microsoft Azure, and Google Cloud Platform all offer essentially the same basic features in terms of configurable computation, storage, and networking. They all have the features of a public cloud, such as self-service and rapid provisioning, auto-scaling, as well as security, compliance, and identity management.

Customers can use either cloud to build a mobile app or even create a high-performance computing environment, depending on their needs. All three vendors have launched services and tools targeted at cutting-edge technology areas like the Internet of Things (IoT) and serverless computing (Lambda for AWS, Functions with Azure, and Google), while customers can use either cloud to build a mobile app or even create a high-performance computing environment, depending on their needs.

It can be arduous to choose the appropriate cloud platform provider. All the above-mentioned well-known platforms provide us with a vast array of products and services. However, figuring out which one will align with your requirements is difficult yet crucial.

The roles of these cloud providers in Big Data Analytics are as follows:

  • AWS: AWS provides a comprehensive set of analytic tools and services that meet the data analytic requirements of organizations of all sizes and help them to redefine their business through data. AWS Elastic MapReduce (EMR), a managed Hadoop, Spark, and PrestoDB solution, lies at the core of Amazon’s analytics solutions. EMR manages the setup of an underlying EC2 cluster and integrates with a number of AWS services, such as S3 and DynamoDB. Data Pipeline is a data orchestration tool that allows you to transport, copy, transform, and enrich data. It is in charge of pipeline activity scheduling, orchestration, and monitoring. Kinesis Streams, an AWS service that can analyze hundreds of data streams per second, enables real-time stream data analytics.
AWS Services for Data Analytics
  • Azure: Microsoft Azure has a plethora of services for evaluating large amounts of data. One of the most efficient approaches is to store data in Azure Data Lake Storage Gen2 and then process it with Spark on Azure Databricks. Azure Synapse Analytics is a complete data analytics platform service which brings together Azure Data Warehouse, Azure Data Lake, Azure Data Factory, Spark and Power BI. It enables you to select the languages and frameworks that best suit your abilities and requirements. Azure Data Factory is still available as a separate service for building data processing pipelines. The data factory can read data from a variety of Azure and third-party data sources, as well as connect to and consume on-premise data using Data Management Gateway. Azure provides Stream Analytics, Data Lake Analytics, and Data Lake storage for real-time data analytics and storage.
Azure Services for Data Analytics
  • GCP: The Google Cloud Platform (GCP) offers a variety of analytics tools, all of which are created with distinct data analytics and management capabilities. It offers a number of services for storing and analyzing large amounts of data. BigQuery, a high-performance SQL-compatible engine that can execute analysis on very massive data volumes in seconds, is perhaps the most significant. Cloud Dataproc is Google’s fully managed Hadoop and Spark offering. Cloud Storage also provides an HDFS-compliant connector that may be used to store data that has to exist after the cluster has been shut down. Cloud Dataflow can be used to create data processing pipelines. Cloud Dataflow, unlike GCP and AWS, provides a fully programmable framework, available for Java and Python, and a distributed computing platform. It supports both batch and streaming workers.
GCP Services for Data Analytics

Comparison of different Cloud Platforms for Data Analytics

Type of ServiceAWSAzureGCP
Data DiscoveryAmazon AthenaAzure Data Lake AnalyticsGoogle Big Query
Big Data ProcessingAWS EMRHDInsightCloud Dataproc
Data OrchestrationAWS Data PipelineAzure Data FactoryCloud Composer
Data WarehouseRedshiftSQL Data WarehouseGoogle Big Query
Streaming ProcessAWS KinesisAzure Event HubsCloud Dataflow
Data VisualizationQuicksightPowerBIGoogle Data Portal
Machine LearningAWS SagemakerML StudioGoogle Datalab
Data CatalogAWS GlueAzure Data CatalogCloud Data Catalog
StorageS3Azure Blob StorageGoogle Cloud Storage
PricingHourly cost for each node + underlying EC2 InstanceSynapse Commit Units(SCUs) underlying compute engine + vCPU per minute


In this blog, we explored how data gets captured, processed, and used to strategize business functions to meet customer needs. We now also know the importance of analyzing data, which data analytics tools are popular, how the cloud data analytics ecosystem functions, and what the top trends are in big data analytics. With the rise of Web3.0, we are yet to see the technologies which shall dominate the realm of data analytics. We will come up with more exhaustive topics with use cases in our upcoming blogs. Stay tuned to keep getting all updates about our upcoming new blogs on AWS and relevant technologies.


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