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Hottest Trends in Big Data

Before we begin exploring the hot trends of big data, it is important to understand what big data truly represents. The Big data is the tremendous volumes of information produced from various industry spaces. Enormous information, for the most part, contains information accumulation, information examination, and information usage forms. As the years progressed, there’s been an adjustment in the enormous information examination patterns – organizations have swapped the monotonous departmental methodology with an information-based approach.

This has seen more noteworthy utilization of spry innovations alongside uplifted interest for cutting edge investigation. Remaining in front of the challenge currently expects organizations to send propelled information-driven investigation.

When it previously came into the image, enormous information was basically sent by greater organizations that could manage the cost of the innovation when it was costly. At present, the extent of big data has changed to the degree that undertakings both little and enormous depend on huge information for wise examination and business bits of knowledge.

This has brought about the development of enormous information sciences and technology at a truly quick pace. The most appropriate case of this development is the cloud which has let even private ventures exploit the most recent innovation and trends.

Hottest Trends in Big Data

BIG DATA ANALYSIS

Huge information investigation is the regularly mind-boggling procedure of analyzing enormous and differed informational collections, or colossal information, to reveal data, for example, concealed examples, obscure relationships, showcase patterns, and client inclinations – that can enable associations to settle on educated business choices.

On a wide scale, information examination advancements and technology procedures give a way to break down informational indexes and reach inferences about them which help associations settle on educated business choices. Business knowledge (BI) inquiries answer fundamental inquiries concerning business tasks and execution.

Huge information examination is a type of cutting edge investigation, which includes complex applications with components, for example, prescient models, factual calculations and consider the possibility that examination controlled by superior examination frameworks.

Big data investigation advancements and technology 

Unstructured and semi-organized information types regularly don’t fit well in conventional information distribution centers that depend on social databases situated to organized informational collections.

Further, information stockrooms will most likely be unable to deal with the preparing requests presented by sets of huge information that should be refreshed every now and again or even constantly, as on account of continuous information on stock exchanges, the online exercises of site guests or the exhibition of portable applications.

10 HOT TRENDS OF BIG DATA ANALYSIS FOR 2019

Quantum Computing

Industry insiders accept that the fate of technology has a place with the organization that fabricates the main quantum PC. Nothing unexpected that each technology mammoth including Microsoft, Intel, Google, and IBM, are dashing for the top spot in quantum registering. All in all, what’s the enormous draw with quantum registering?

It permits consistent encryption of information, climate expectation, answers for long-standing medicinal issues and afterward some more. Quantum registering permits genuine discussions among clients and associations. There’s likewise the guarantee of patched up money related displaying that enables associations to create quantum processing segments alongside applications and calculations.

Edge Computing

The idea of edge processing among other enormous information patterns didn’t simply develop yesterday. System execution gushing utilizes edge processing pretty consistently even today. To spare information on the nearby server near the information source, we rely upon the system transfer speed. That is made conceivable with edge registering. Edge registering stores information closer to the end clients and more remote from the storehouse arrangement with the handling happening either in the gadget or in the server farm. This strategy has been under development and has been in trend in 2019.

Open Sourcing

Individual small scale specialty engineers will constantly step up their game in 2019. That implies we will see increasingly more programming devices and free information become accessible on the cloud. This will massively profit little associations and new companies in 2019 and in the future. More dialects and stages like the GNU venture, R, will hoard the technology spotlight in the year to come. The open-source wave will enable little associations to eliminate costly custom improvement.

Data Quality Management (DQM)

The examination slants in information quality developed significantly this previous year. The advancement of business insight to break down and concentrate an incentive from the endless wellsprings of information that we accumulate at a high scale brought close by a lot of blunders and low-quality reports: the dissimilarity of information sources and information types added some greater multifaceted nature to the information coordination process.

An overview directed by the Business Application Research Center expressed the Data quality administration as the most significant pattern in 2019. It isn’t just critical to accumulate as much data conceivable, yet the quality and the setting where information is being utilized and deciphered fills in as the fundamental concentration for the eventual fate of business insight.

Artificial Intelligence

We are developing from static, aloof reports of things that have just happened to proactive investigation with live dashboards helping organizations to perceive what’s going on at consistently and give alarms when something isn’t the manner by which it ought to be. Our answer at datapine incorporates an AI calculation dependent on the most progressive neural systems, giving a high precision in abnormality recognition as it gains from chronicled patterns and examples. That way, any startling occasion will be advised and will send you an alarm.

We have likewise built up another component called Insights, additionally AI-based, that completely breaks down your dataset naturally without requiring an exertion on your end. You basically pick the information source you need to investigate and the segment/variable (for example, Revenue) that our choice emotionally supportive network programming should concentrate on.

At that point, estimations will be run and return to you with development/patterns/conjecture, esteem driver, key fragments relationships, oddities, and a consider the possibility that examination. That is an amazing time gain as what is normally taken care of by an information researcher will be performed by a device, furnishing business clients with access to top-notch bits of knowledge and a superior comprehension of their data, even without a solid IT foundation.

Connected Clouds

The pervasiveness of the cloud is the same old thing for anyone who keeps awake-to-date with BI patterns. In 2019 the cloud will proceed with its rule with an ever-increasing number of organizations moving towards it because of the expansion of cloud-put together apparatuses accessible with respect to the market. In addition, business people will figure out how to grasp the intensity of cloud investigation, where the greater part of the components – information sources, information models, preparing applications, registering power, expository models and information stockpiling – are situated in the cloud.

Booming IoT Networks

Like it’s experienced 2018, the Internet of Things (IoT) will keep on drifting through 2019, with yearly incomes arriving at path past $300 billion by 2020. The most recent research reports show that the IoT market will develop at a 28.5% CAGR. Associations will rely upon progressively organized information that focuses to accumulate data and increase more keen business experiences.

Unstructured or Dark Data

Dim information alludes to any information that is basically not a piece of business examination. These bundles of information originate from a large number of computerized organize tasks that are not used to accumulate bits of knowledge or decide. Since information and investigation are progressively increasing pieces of the everyday parts of our associations, there’s something that we as a whole should get it. Losing a chance to think about unexplored information is a big deal of potential security hazard.

Continuous Intelligence

Progressively, organizations need to work in a powerful situation and improve their choices continuously. With the assistance of nonstop information and the previously mentioned enlarged investigation, organizations will have the option to send what Gartner calls ‘Ceaseless insight’ that basically enables organizations to dissect approaching information settings progressively, by utilizing frictionless process duration to get constant business esteem from information and endorsing quick choices to improve results.

Basically, every client connection can help improve the following one. That is the intensity of persistent knowledge. It can perform continuous examination and recommend arrangements utilizing choice robotization and increased investigation.

Ceaseless knowledge will be in trend in the following year or two, with organizations receiving it to send better arrangements progressively. Regardless of what number of information sources the information streams in from, or how immense or complex it is, this cutting edge ML-driven methodology will enable organizations to quicken investigation and basic leadership.

Conversational Analytics

Normal language handling, a subset of AI and ML has quickly turned out to be ordinary and by 2020, half of every single expository inquiry will be created by voice. In 2020, additional clients will collaborate with chatbots and brilliant speakers like Alexa and Google Home.

Social occasion voice information and breaking down it will end up being a vital piece of each business’ information investigation technique. Investigation of conversational information can be dubious and the technology is as yet developing. Nonetheless, voice technology is particularly a typical piece of life today and conversational examination will see more extensive selection without a doubt.

Difference between Data Science and Big Data Analytics

What is Data Science?

Data science is a scientific methodology, to obtain actionable insights from large unprocessed data sets and structured data. It focuses on uncovering things that we do not know. It is a source of innovative solutions for our problems.

It uses a variety of models and means of extracting and processing information. It analyses data on the concept of mathematics and statistics with the help of automated tools. Cleanse data, find data connections, analyse, and predict potential trends. Manipulate, identify disconnected data points, and explore the probabilities and combinations.

It encourages us to try distinct ways to analyze information. Capture data, program it and solve specific problems with data science. It provides a new perspective towards data, enhances usability to provide insights. Data science can support accurate business decisions and tackle big data.

Data scientists use programming languages like SQL, Python, Java, R, and Scala for multiple analytical functions. They write algorithms, build statistical and predictive models to analyze data.

What is Big Data Analytics?

What is Big Data Analytics?

Big Data effectively processes enormous data, extensive information, and complex data that traditional applications cannot attempt. Big data consists of a variety of structured and unstructured data. Introduce cost-effective and latest forms of information to enable enhanced business insights. It can highlight market trends, customer preferences, customer behavior, and buying patterns

Data analytics can help in the organization’s goals by measuring the current and past events and plans form future events. It performs statistical analysis to create a meaningful presentation of data by connecting patterns to strategize business. It eases immediate improvements, problem-solving, and respond to specific concern area. Data analysts require knowledge of Pig, Hive, R, SQL, and Python.

Data Analytics needs well-defined data sets to address particular problematic areas of business. For better results, the data analysts need to have technical expertise and knowledge of mathematics and statistics. Data mining, database management, data analysis, and skills to convey the quantitative results achieved from data.

Data Analysis has important role in Data Science; it performs a variety of tasks such as collecting and organizing data. It assists in presenting the data in charts, graphs, comparative tables, and build relational databases for organizations.

Data analysis and data analytics sound similar, data analysis includes everything a data analyst practices compiling and analyzing data. Whereas data analytics is a subsection of data analysis, it uses technical tools and data analysis techniques to achieve business objectives.

What is the Difference between Data Science and Big Data Analytics?

Data Science is an integral part of Artificial Intelligence, Machine Learning, Search Engine Engineering, and Corporate Analytics. Big Data Analytics is widely used to find actionable items in fields such as healthcare, gaming, and travel industries.

With a greater scope of data, science helps in data mining for varied and unique fields. Big Data analysis mainly focuses on processing large data. Simplification of the differentiation, data science provides thought for questions you should ask and big data analytics helps in discovering answers to questions.

Data science lays a strong foundation by initiating a focus on future trends, improves observations of data movements, and provides potential insights. Big data analytics provides the path for practical application of actionable insights.

Data analytics examines large data sets and data scientists create algorithms, work on creating new models for prediction.

Are there any Similarities between Data Science and Big Data Analytics?

Are there any similarities between Data Science and Big Data analytics?

Similarity, the interconnectivity of Data Science and Big Data Analytics brings wonderful results to benefit organizations. Their dependency can affect the overall quality of action strategy and consequences based on those actions. Companies never apply both Data Science and Big Data Analytics together in every situation yet are useful for different purposes. It can help companies in the technological change they are about to have. Both can help companies to understand the data better.

The relationship between them can have a positive impact on the company.

  • In 2019, the big data market likely to grow by 20% and the big data analytics market headed towards the target of $103 billion by 2023.
  • Worldwide the companies in various sectors using big data technology are telecommunications 94.5%, insurance 83%, advertising 77%, financial services 70%, healthcare 63%, and technology 57.5%.
  • Nearly, 81% of data scientists analyze data of non-IT industries.
  • About 90% of enterprise analytics stated that data and analytics are key elements of initiatives taken by their organization towards digital transformation.
  • Data-driven organizations have 23 times more chances of customer acquisition, and 6 times more likely to retain the customer.
  • Businesses are motivated to get more insights, proves the 30% per year growth in insight-driven organizations.
  • By 2020, we can expect 2.7 million job listings for data science and data analytics.

Applications and Benefits of Data Science and Big Data Analytics:

Tremendous benefits of data science are noticeable with the number of industries involved in technological developments. Data science is a driving force for business improvement and expansion.

  • Agriculture: Surprisingly data science bias-free thus can benefit even sectors that were not data-driven. It is a reliable source for suggestive actions for water frequency or quantity manure required, soil suitable crops, the precise amount of seeds needed, etc. Big data analytics can be of great assistance to farmers in yield prediction, crop failure symptoms due to weather changes, food safety, and spoilage prevention and much more. Companies can rest assured of crop quality, precautions taken by farmers during harvesting and packaging, and on delivery possibilities.
  • Aviation & Travel: Data science can help in reducing operating costs, maximizing bookings, and improving profits. Technology can help flyers in taking decisions of routes, connecting flights, and seats before booking. This is the service industry, for better performance in various areas; companies adopt data science. Big data analytics can enhance customer experience through information shared by the company. Users can find travel discounts, delays, customized packages, open tickets, and personalized air and other travel recommendations, etc. Companies can get statistical and predictive analysis about the selective area such as profits against a particular marketing campaign. Social media activities and its positive impact or rates of conversion are some of the insights that can help in cost reduction.
  • Customer Acquisition: The complete process is of high importance and creates high value for businesses. Data Science can help identify business opportunities, amend marketing strategies, and design marketing campaigns. Redefining strategies, redesigning campaigns and re-targeting audiences is possible with data science. Big data analytics highlights the pain and profit points for business. Identify the best possible method for customer acquisition and improve on the basis of data analysis. Return on Investments, profitability, and other important business ratios presented by big data analytics in the simplest form. Big Data of the telecommunications industry can help in getting new subscribers, retaining existing customers, approaching current subscribers to serve based on their priorities, frequency of recharge, package preferences, and use of internet packs, etc.
  • Education: Implementing data science in this sector can help in the student admission process; take calculated decisions, check enrollment rates, dropouts from institutes, etc. Big data analysis can compare the current and past year’s student data, issues in process or course wise predictions of student performance, etc. Colleges and educational institutes can perform various analyses using the data and plan the changes required. Big data analytics can evaluate students for admissions in other courses based on their eligibility, preferences, or inclination.
  • Healthcare: Data science collects data from various applications, wearable gear, and patient data by monitoring constantly. It helps in preventing potential health problems. The pharma research and new medicine coding are eased with data science. It can predict illness, frequent hospitalizations. Hospitals can use it for new cases, to diagnose patients accurately, and take quick decisions and save lives. Big data analytics can help in cost reduction on treatments, treat maximum patients, improve medical services and the estimations needed to serve better with exciting machines.
  • Internet Search: The search engines use data science to write effective algorithms to deliver the accurate results of search queries in milliseconds. Big data analytics can recommend users on their search, product, or services, or show preference based results. Search engines have frequent visitors, their view history, specific requirements, and many preferences. The speedy suggestions can save time and increases the chances of someone clicking the links. Even digital advertisements have strong data science algorithms and they are effective than traditional methods of advertising. The user experience and profitability of companies improve with the help of big data analysis.
  • Financial Services: Banking, insurance, and financial institutes have to deal with huge data and the complexity, data science efficiently deals with. Big data analytics allows us to focus on relevant data from the loads of massive data that influence customer analytics. It helps in operational issues identification, fraud prevention and improved recommendations for customers.

Now with the scope of data science and big data analytics, we can find why customers are loyal or why they leave you. Find what works in your favor and against you. Know more about customer expectations and if you can meet them. Find more of such indications are available at varied data points that lie on websites, e-commerce sites, mobile apps, and social media interactions.

Data Science and Big Data Analytics consider facts thus it empowers us to plan, face competition and perform better. We can proactively respond to requests and anticipate the needs of our customers. Deliver relevant products with no anticipations but data-supported predictions. Link innovation in product and service with a set of customer expectations and new demands that generate with time and technology.

Services can be personalized and respond in real-time for faster service. Optimize and improve operational efficiency and productivity by using various techniques for analytics for continuous change and growth. Risk mitigation and fraud prevention provides added security.

Data Science increases abilities to understand the customers and their decision-making patterns. Big Data analysis helps in anticipating the potential that lies in the future based on current data and its predictions.

Conclusion:

Modern businesses generate huge data and taking actions based on valuable insights is extremely unavoidable in order to remain in the competition. By 2021, organizations using big data analysis will be in a position to take a share of $1.8 trillion than the ones less informed. We can look into the data relevancy, using before its stale, reduce the customer experience gaps and deliver in real-time if we are committed to using interweave technology with business. Being a data-driven organization is an intelligent choice.

Big Data Analytics Tools

Big Data is a large collection of data sets that are complex enough to process using traditional applications. The variety, volume, and complexity adds to the challenges of managing and processing big data. Mostly the data created is unstructured and thus more difficult to understand and use it extensively. We need to structure the data and store it to categorize for better analysis as the data can size up to Terabytes.

Data generated by digital technologies are acquired from user data on mobile apps, social media platforms, interactive and e-commerce sites, or online shopping sites. Big Data can be in various forms such as text, audio, video, and images. The importance of data established from the facts as its creation itself is multiplying rapidly. Data is junk if the information is not usable, its proper channelization along with a purpose attached to it.
Data at your fingertips eases and optimizes the business performance with the capability of dealing with situations that need severe decisions.

Interesting Statistics of Big Data:

What is Big Data Analytics?

Big data analytics is a complex process to examine large and varied data sets that have unique patterns. It introduces the productive use of data.
It accelerates data processing with the help of programs for data analytics. Advanced algorithms and artificial intelligence contribute to transforming the data into valuable insights. You can focus on market trends, find correlations, product performance, do research, find operational gaps, and know about customer preferences.
Big Data analytics accompanied by data analytics technologies make the analysis reliable. It consists of what-if analysis, predictive analysis, and statistical representation. Big data analytics helps organizations in improving products, processes, and decision-making.

The importance of big data analytics and its tools for Organizations:

  1. Improving product and service quality
  2. Enhanced operational efficiency
  3. Attracting new customers
  4. Finding new opportunities
  5. Launch new products/ services
  6. Track transactions and detect fraudulent transactions
  7. Effective marketing
  8. Good customer service
  9. Draw competitive advantages
  10. Reduced customer retention expenses
  11. Decreases overall expenses
  12. Establish a data-driven culture
  13. Corrective measures and actions based on predictions
Insights by Big Data Analytics

For Technical Teams:

  1. Accelerate deployment capabilities
  2. Investigate bottlenecks in the system
  3. Create huge data processing systems
  4. Find better and unpredicted relationships between the variables
  5. Monitor situation with real-time analysis even during development
  6. Spot patterns to recommend and convert to chart
  7. Extract maximum benefit from the big data analytics tools
  8. Architect highly scalable distributed systems
  9. Create significant and self-explanatory data reports
  10. Use complex technological tools to simplify the data for users

Data produced by industries whether, automobile, manufacturing, healthcare, travel is industry-specific. This industry data helps in discovering coverage and sales patterns and customer trends. It can check the quality of interaction, the impact of gaps in delivery and make decisions based on data.

Various analytical processes commonly used are data mining, predictive analysis, artificial intelligence, machine learning, and deep learning. The capability of companies and customer experience improves when we combine Big Data to Machine Learning and Artificial Intelligence.

Big Data Analytics Processes

Predictions of Big Data Analytics:

  1. In 2019, the big data market is positioned to grow by 20%
  2. Revenues of Worldwide Big Data market for software and services are likely to reach $274.3 billion by 2022.
  3. The big data analytics market may reach $103 billion by 2023
  4. By 2020, individuals will generate 1.7 megabytes in a second
  5. 97.2% of organizations are investing in big data and AI
  6. Approximately, 45 % of companies run at least some big data workloads on the cloud.
  7. Forbes thinks we may need an analysis of more than 150 trillion gigabytes of data by 2025.
  8. As reported by Statista and Wikibon Big Data applications and analytic’s projected growth is $19.4 billion in 2026 and Professional Services in Big Data market worldwide is projected to grow to $21.3 billion by 2026.

Big Data Processing:

Identify Big Data with its high volume, velocity, and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis.

Big Data Processing

Data processing challenges are high according to the Kaggle’s survey on the State of Data Science and Machine Learning, more than 16000 data professionals from over 171 countries. The concerns shared by these professionals voted for selected factors.

  1. Low-quality Data – 35.9%
  2. Lack of data science talent in organizations – 30.2%
  3. Lack of domain expert input – 14.2%
  4. Lack of clarity in handling data – 22.1%
  5. Company politics & lack of support – 27%
  6. Unavailability of difficulty to access data – 22%
  7. These are some common issues and can easily eat away your efforts of shifting to the latest technology.
  8. Today we have affordable and solution centered tools for big data analytics for SML companies.

Big Data Tools:

Selecting big data tools to meet the business requirement. These tools have analytic capabilities for predictive mining, neural networks, and path and link analysis. They even let you import or export data making it easy to connect and create a big data repository. The big data tool creates a visual presentation of data and encourages teamwork with insightful predictions.

Big Data Tools

Microsoft HDInsight:

Azure HDInsight is a Spark and Hadoop service on the cloud. Apache Hadoop powers this Big Data solution of Microsoft; it is an open-source analytics service in the cloud for enterprises.

Pros:

  • High availability of low cost
  • Live analytics of social media
  • On-demand job execution using Azure Data Factory
  • Reliable analytics along with industry-leading SLA
  • Deployment of Hadoop on a cloud without purchasing new hardware or paying any other charges

Cons:

  • Azure has Microsoft features that need time to understand
  • Errors on loading large volume of data
  • Quite expensive to run MapReduce jobs on the cloud
  • Azure logs are barely useful in addressing issues

Pricing: Get Quote

Verdict: Microsoft HDInsight protects the data assets. It provides enterprise-grade security for on-premises and has authority controls on a cloud. It is a high productivity platform for developers and data scientists.

Cloudera:

Distribution for Hadoop: Cloudera offers the best open-source data platform; it aims at enterprise quality deployments of that technology.

Pros:

  • Easy to use and implement
  • Cloudera Manager brings excellent management capabilities
  • Enables management of clusters and not just individual servers
  • Easy to install on virtual machines
  • Installation from local repositories

Cons:

  • Data Ingestion should be simpler
  • It may crash in executing a long job
  • Complicating UI features need updates
  • Data science workbench can be improved
  • Improvement in cluster management tool needed

Pricing: Free, get quotes for annual subscriptions of data engineering, data science and many other services they offer.

Verdict: This tool is a very stable platform and keeps on continuously updated features. It can monitor and manage numerous Hadoop clusters from a single tool. You can collect huge data, process or distribute it.

Sisense:

This tool helps to make Big Data analysis easy for large organizations, especially with speedy implementation. Sisense works smoothly on the cloud and premises.

Pros:

  • Data Visualization via dashboard
  • Personalized dashboards
  • Interactive visualizations
  • Detect trends and patterns with Natural Language Detection
  • Export Data to various formats

Cons:

  • Frequent updates and release of new features, older versions are ignored
  • Per page data display limit should be increased
  • Data synchronization function is missing in the Salesforce connector
  • Customization of dashboards is a bit problematic
  • Operational metrics missing on dashboard

Pricing: The annual license model and custom pricing are available.

Verdict: It is a reliable business intelligence and big data analytics tool. It handles all your complex data efficiently and live data analysis helps in dealing with multiparty for product/ service enhancement. The pulse feature lets us select KPIs of our choice.

Periscope Data:

This tool is available through Sisense and is a great combination of business intelligence and analytics to a single platform.
Its ability to handle unstructured data for predictive analysis uses Natural Language Processing in delivering better results. A powerful data engine is high speed and can analyze any size of complex data. Live dashboards enable faster sharing via e-mail and links; embedded in your website to keep everyone aligned with the work progress.

Pros:

  • Work-flow optimization
  • Instant data visualization
  • Data Cleansing
  • Customizable Templates
  • Git Integration

Cons:

  • Too many widgets on the dashboard consume time in re-arranging.
  • Filtering works differently, should be like Google Analytics.
  • Customization of charts and coding dashboards requires knowledge of SQL
  • Less clarity in display of results

Pricing: Free, get a customized quote.

Verdict: Periscope data is end-to-end big data analytics solutions. It has custom visualization, mapping capabilities, version control, and two-factor authentication and a lot more that you would not like to miss out on.

Zoho Analytics:

This tool lets you function independently without the IT team’s assistance. Zoho is easy to use; it has a drag and drop interface. Handle the data access and control its permissions for better data security.

Pros:

  • Pre-defined common reports
  • Reports scheduling and sharing
  • IP restriction and access restriction
  • Data Filtering
  • Real-time Analytics

Cons:

  • Zoho updates affect the analytics, as these updates are not well documented.
  • Customization of reports is time-consuming and a learning experience.
  • The cloud-based solution uses a randomizing URL, which can cause issues while creating ACLs through office firewalls.

Pricing: Free plan for two users, $875, $1750, $4000, and $15,250 monthly.

Verdict: Zoho Analytics allows us to create a comment thread in the application; this improves collaboration between managers and teams. We recommended Zoho for businesses that need ongoing communication and access data analytics at various levels.

Tableau Public:

This tool is flexible, powerful, intuitive, and adapts to your environment. It provides strong governance and security. The business intelligence (BI) used in the tool provides analytic solutions that empower businesses to generate meaningful insights. Data collection from various sources such as applications, spreadsheets, Google Analytics reduces data management solutions.

Pros:

  • Performance Metrics
  • Profitability Analysis
  • Visual Analytics
  • Data Visualization
  • Customize Charts

Cons:

  • Understanding the scope of this tool is time-consuming
  • Lack of clarity in using makes it difficult to use
  • Price is a concern for small organizations
  • Lack of understanding in users for the way this tool deals with data.
  • Not much flexible for numeric/ tabular reports

Pricing: Free & $70 per user per month.

Verdict: You can view dashboards in multiple devices like mobiles, laptops, and tablets. Features, functionality integration, and performance make it appealing. The live visual analytics and interactive dashboard is useful to the businesses for better communication for desired actions.

Rapidminer:

It is a cross-platform open-source big data tool, which offers an integrated environment for Data Science, ML, and Predictive Analytics. It is useful for data preparation and model deployment. It has several other products to build data mining processes and set predictive analysis as required by the business.

Pros:

  • Non-technical person can use this tool
  • Build accurate predictive models
  • Integrates well with APIs and cloud
  • Process change tracking
  • Schedule reports and set triggered notifications

Cons:

  • Not that great for image, audio and video data
  • Require Git Integration for version control
  • Modifying machine learning is challenging
  • Memory size it consumes is high
  • Programmed responses make it difficult to get problems solved

Pricing: Subscription $2,500, $5,000 & $10,000 User/Year.

Verdict: Huge organizations like Samsung, Hitachi, BMW, and many others use RapidMiner. The loads of data they handle indicate the reliability of this tool. Store streaming data in numerous databases and the tool allows multiple data management methods.

Conclusion:

The velocity and veracity that big data analytics tools offer make them a business necessity. Big data initiatives have an interesting success rate that shows how companies want to adopt new technology. Of course, some of them do succeed. The organizations using big data analytic tools benefited in lowering operational costs and establishing the data-driven culture.