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You can’t dig into Big Data storage without first discussing Big Data in general. Big Data is a concept that any IT professional or knowledge worker understands almost by instinct, as the trend has been covered so extensively.

Data has been growing exponentially in recent years, yet much of it is locked in application and database siloes. If you could drill into all of that data, if you could share it, if you could cross-pollinate, say, a CRM system with information from your marketing analytics tools, your organization would benefit. Easier said than done.

That, essentially, is the Big Data challenge.

Arguably, the concept of Big Data entered the public imagination with the publication of Michael Lewis’ Moneyballin 2003. Of course, the term “Big Data” is nowhere to be found in the book, but that’s what the book was about – finding hidden patterns and insights within the reams of data collected during each and every major league baseball game.

One statistic that has been buried – well, buried isn’t right; ignored is more accurate – was about drafting college players over high school players. College players have a track record. They have statistics that can be measured, and they played against at least a half-decent level of competition:

“[Bill James] looked into the history of the draft and discovered that “college players are a better investment than high school players by a huge, huge, laughably huge margin.” The conventional wisdom of baseball insiders – that high school players were more likely to become superstars – was also demonstrably false. What James couldn’t understand was why baseball teams refused to acknowledge that fact.”

Pushing past gathering raw information and onto challenging preconceptions is at the heart of Big Data. So, too, is discovering truths that no one would have ever suspected before.

However, in order to gain these new insights and to challenge our misconceptions, we must find ways to access all of that data, hidden away in all of those proprietary applications and databases.

That’s not just a Big Data problem. It’s also a management problem, and it’s most certainly a storage problem.

Just how much data is out there? No one knows for sure, of course, but IBM’s Big Data estimates conclude that “each day we create 2.5 quintillion bytes of data.” The exponential growth of data means that 90 percent of the data that exists in the world today has been created in the last two years. “This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, e-commerce transaction records, and cell phone GPS coordinates, to name a few.”

To put the data explosion in context, consider this. Every minute of every day we create:

• More than 204 million email messages

• Over 2 million Google search queries

• 48 hours of new YouTube videos

• 684,000 bits of content shared on Facebook

• More than 100,000 tweets

• 3,600 new photos shared on Instagram

• Nearly 350 new WordPress blog posts

Source: Domo

This volume of data could not be saved, collected and stored were it not for the fact that data storage is so incredibly cheap. Today, everything from tablets to desktops is sold with ever bigger hard drives. Why would you bother deleting anything when it’s so cheap and easy to store it?

Between 2000 and today, the cost of storage has plummeted from about $9/GB to a mere $.08/GB, and as soon as I typed that low price point, you can bet that downward price pressure has already made those numbers obsolete.

If you are a highly paid knowledge worker, it’s probably cheaper to store data than delete it, since the productivity lost while purging old files may well cost your organization more than the storage costs — unless you have to find something lost in this data maze for, say, regulatory compliance.

Data is collected from everywhere, but where is it stored? That’s the crux of the problem. It’s stored everywhere, as well. Typically, these data repositories – “data silos”– are application specific.

Big Data storage, then, is as much about managing data as about storing it.

In Big Data storage management, we’re encountering a problem we’ve dealt with many times before.

We haven’t yet figured out a workable Dewey Decimal system for data. We’re moving in the right direction, with such tools as hyperlinks and wikis. But most data in enterprise applications, email servers and social networks is not structured for easy sharing to other applications.

1. Unstructured data. There are two types of data in storage, structured and unstructured data. Structured data has a high degree of organization, and is typically stored in a relational database that can be easily searched.

Unstructured data is, obviously, not structured in any meaningful way, including such things as photographs, videos, MP3 files, etc. Unstructured data is difficult to search and analyze.

2. I/O barriers. If you’re dealing with something like mapping genomes, gathering information from the Mars Rover or running sophisticated weather simulations, the transaction volumes of these data sets challenge traditional storage systems, which don’t have enough processing power to keep up with the huge number of I/O requests.

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Transformative Role Of Big Data Across Industries

We’ve all heard the buzzword “Big Data“ and frankly you maybe even a bit tired of hearing it. Although the term is too generic and often improperly used, it is not just a hype. It’s a quiet revolution. The age of data-driven management has already arrived and those that don’t adapt will be stomped out by competition. Let’s look at some of the industries which have already been transformed by the use of Big Data analytics.

Retail Industry

Creating a seamless user experience and managing multiple-channel customer interaction is essential. For example, a consumer might begin researching a product on a mobile app, purchase it online and pick it up at a store. Coordinating this multi-channel shopping interaction requires a business to effectively manage, integrate and understand this vast array of data coming at a non-stop pace. For example, you may figure out that certain video game is extremely popular but which of your customers order it online and which ones prefer to go to the store is a key question that can drive personalized marketing campaigns with a greater ROI. The following infographic from business and technology consulting firm Wipro explains further.

Supply Chain

Figuring out the shortest route from the distribution center to the store and having a balanced stock in each distribution center drives huge savings in operating costs. The Boston Consulting Group analyzes how big data is being used in supply chain management in the article “Making Big Data Work: Supply Chain Management“. One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from that article.

Banking & Insurance

In both banking and insurance sector the name of the game is Risk Management. A bank issues you a loan or a credit card and they make money on the interest rate. Besides obvious risk of you not paying of your debt there is another risk which is you paying off your debt prematurely and thus generating less revenue for the bank.

Predictive analytics has been in use since the 90’s to identify the interest rates thresholds which result in early payoff / reduced loan interest rate income for the banks. In the financial world a single transaction is the key building block of huge amounts of data that are then analyzed with predictive models and based on trending on massive scale allow for categorization of customer profiles that can predict risk associated with individual users. Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.

In the insurance world it also boils down to customer profiles – if the premium is too high (the offer is not a good fit to customer profile) they may switch to another insurance company. To contrast this, if you have a risky car driver your offering is costing your insurance company more in claims than it does in the insurance rate or premiums. Figuring out which customers are more risk-prone than others allows for custom tailored offers that mitigate the risk of losing a good customer or losing money on a bad customer. A good example of how technology is disrupting this field is the Snapshot device which transmits data about when customers drive, how often they drive, and how hard they brake.

It is not expensive and it is available now

According to the Accenture study the main reason why business owners aren’t implementing their Big Data ideas is the perception that it is very expensive. They would have been right 10 years ago. Not anymore.

Microsoft’s Power BI platform allows small and medium sized business owners to harvest the power of Big Data analytics without any technical expertise. Also, because it’s a platform it comes with insightful industry-specific BI tools – there’s no need to reinvent the wheel, you can start using the same reports that big players use, for a fraction of the cost. Using real-time business data, Power BI delivers crisp, clear dashboards that assist managers to comprehend where their business stands today, how it performed historically, and what can be done for future success.

Besides savings, on implementation costs (which can be tens or hundreds of thousands of dollars) your maintenance costs are virtually zero dollars. The Microsoft team not just keeps the platform running smooth, but improves and updates features as the market evolves, so you know that you will always get the latest industry-adopted reporting standards on your laptop, mobile or any other device anywhere you are.

Where Big Data And Physical Markets Meet

by Yaniv Vardi

The industrial revolution occurred in the 18th century, ushering in the industrial age, which continued through the 20th century.  On its heels, began the information age, which is ongoing, according to most experts.  While the industrial age focused on automation and mass manufacturing, the information age is based on today’s extensive communication infrastructure, which has enabled access to virtually endless information. 

The Evolution of Data

Historically, products were classified into tangible products and non-tangible services.  Marketing theories have narrowed this distinction (tangible and non-tangible goods), to what is known as the “goods and services continuum,” a model in which some products are an obvious combination of purely tangible goods and associated services. 

Until recently, information was considered too abstract a commodity to be classified as either a good or a service. Even intangible services were thought to require some physical presence, whether in their delivery or effect, to actualize their utility. Platonic information – or data as we so commonly refer to it today – was generally reserved for matters of education, statecraft or religious studies. 

Yet, as businesses and technologies evolved, the data produced on sales, profit margins and trends began to influence corporate decisions, bringing information to the enterprise. While this general connection became clear, the specific connection between any specific parcel of information and its impact on business decisions remained as nebulous as ever.

Still, confident in the belief that within the knot of data there was somewhere a thread connecting information to decision, prospectors became convinced of incredible latent value. What the California Gold Rush was physically, the Silicon Valley Data Rush was virtually. The treasure was there, simply waiting to be mined.

The Data Rush was further enabled by the proliferation of connectivity, giving birth to the “always on” culture, which, when combined with social networks, GPS, digitization, online searches and ecommerce transactions, has created a mass of information, commonly coined “Big Data.”

Big Data has become so big and so pervasive that it’s spun an entirely new economic market. Many argue that while Wall Street rushes to confer enormous valuations upon Big Data enterprises, the information they collect has no inherent value. (If you think back to Facebook’s IPO, consider the massive disparity in analyst valuations.) 

With the rise of information as a product, it’s worth asking “Are we witnessing a fundamental rearrangement of the global economy? Is data replacing physical goods and services as the premier engine of economic growth?”

While some may disagree, I respond uncompromisingly in the negative. The value of the data economy must come in its potential to enhance conventional markets, even if it’s a long and windy road from A to Z. Any value claimed beyond this, I contend, is nothing more than hot air – a bubble pumped up on animal spirits and undisciplined speculation.

Mark my words, the real engine of tomorrow’s global economy will be where Big Data and physical markets meet.

Enter the Internet of Things

Today, we are beginning to understand the incredible value that can be realized by coupling highly contextualized data with existing products and processes. The Internet of Things (IoT) – wherein traditionally non-responsive objects become dynamic interfaces constantly collecting, communicating and adjusting to data – has opened the path for organizations to zero in on the hidden points of micro-friction in their processes and thus improve efficiencies.

The Internet of Things is the paradigm of the type of value-generating convergence of Big Data and physical markets to which I refer. At the heart of the Internet of Things, are (weight, temperature, energy, etcetera) sensors and increasingly agile, quick, and sophisticated data processing techniques and tools.

Increasingly, every human and machine act is being catalogued and examined for any and all useful revelations. Consider transactional data, which provides customer insights and purchasing trends, or social data taken from social media. These datasets are being leveraged to evermore successful effect by enterprises looking to create real value throughout their operations – from internal efficiencies through commercialization and marketing strategies.

Deloitte has highlighted key trends in analytics that will influence the business world in the coming years, in what they coin “the next evolution”.  The growth of IoT will similarly have a high impact on businesses in the coming years, affecting consumer products and business models.

Aggregation of data and data analysis will facilitate the creation of new products, markets and services. Analytics will expand across all facets of enterprise, with businesses increasingly investing in Big Data infrastructure and technologies. Such data-driven insights will support decision-making processes.

What we’re witnessing is not the replacement of physical markets with digital markets, but the perfection of physical markets through digital markets.

According to BI Insider, while there were 10 billion devices connected to the internet as of 2024, the volume of connected devices will grow to reach 34 billion by 2023. Fueling this bonanza is the nearly $6 trillion expected to be spent on IoT solutions through 2023.

Big Data Explodes

So how big exactly is this data?

According to IBM, we create 2.5 quintillion (a quintillion has 18 zeros) bytes of data each day. Sales of Big Data and business analytics applications, tools, and services reached $122 billion in 2024 and are projected to increase over 50% to reach $187 billion by 2023, according to IDC. 

Services related revenues are projected to account for over half of this market, followed by software and business analytics. The manufacturing industry will be the largest consumer of Big Data and associated technologies, accounting for close to $23 billion of the aforementioned Big Data sales.

The immenseness of the data produced daily creates challenges, as enterprises and organizations scramble to translate the data into value and data-driven business models. Data scientists and analysts are in such demand that analysts are warning of talent gaps in the near future. A similar demand is projected for managers who know how make data-driven decisions on processes and strategies.

Harness the Power of IoT and Big Data

The Big Data created and stored in an enterprise is unstructured. Rapid analytics are required in order to create the practical insights, which can improve margins and efficiencies. Platforms such as the open source Hadoop or IBM’s Watson offer data processing and analytical tools, which can identify trends, predict behaviors, detect patterns and enhance responsiveness – forging new opportunities for businesses, and improving relationships with customers.  

Similarly, IoT-enabled operations analytics platforms identify trends in operations and manufacturing, enabling companies to improve their efficiencies, more accurately manage controls, better track inventory and ultimately pad their bottom lines. Intelligent energy monitoring and analysis, for example, can detect anomalies and automatically generates actionable energy insights to reduce consumption and machine downtime while eliminating failures altogether.

New Economic Model

Put simply, Big Data and physical markets meet through the Internet of Things, and this convergence drives profit. Integration of data-driven decisions and processes as part of an enterprise’s physical operations creates remarkable value via improved efficiencies, increased productivity, and novel product offerings.

While physical markets aren’t going anywhere and the rise of the data economy does not signal a new world order, there can be no doubt that the Data Rush has altered the face of the commercial landscape forever, for the better.

Author Bio:

Yaniv Vardi is the CEO of Panoramic Power, a leader in device level energy monitoring and performance optimization.

Big Brother’s Watching � And Saving You Big Money

The temptation to goof off or fudge time sheets and gas mileage is apparently irresistible, at least to some field workers. And even the best employees often end up estimating their hours long after the fact, introducing costly errors. 

At R&J Construction Inc., a renovation firm in Danville, Calif., keeping track of where workers were and what they were doing became a major issue. To get the situation under control, R&J earlier this year started using an innovative Web-based time-tracking solution from Automatic Data Processing Inc. (ADP) and Xora Inc.

Workers now carry cell phones equipped with GPS (Global Positioning System) receivers that track and record where they go then and transmit their whereabouts and movements over the cellular network to a Xora computer. Employees also use software on the phones to record how they’re spending their time.

R&J specializes in high-end remodeling work in Danville and nearby towns and cities in the San Francisco Bay area. With 53 employees, most of them working at client sites all day, the company earns about $10-million in revenues annually.

The main time-tracking problem was that employees didn’t bother recording their hours until they came in to the office on Monday morning for the weekly staff meeting. And then they recorded them the old-fashioned way – on paper time sheets.

“They were trying to do it based on memory, and they were making mistakes,” said Wiens. “Then we had people in the office processing those time cards [keying the data into a computer]. That was more opportunity for error. So we decided to automate.”

The first idea was to implement the Pay eXpert Internet payroll service from ADP, a company Wiens had worked with in the past and trusted. It was ADP that suggested bringing Xora into the project. “We didn’t even know they had such a thing [as the Xora GPS service],” she said. “But since we already had the Nextel phones, it all kind of fit.”

Getting an accurate record of how workers spend their time is critical for a company like R&J for a couple of reasons. The firm delivers time-and-materials bills to clients, detailing what work was done on a project and when. If workers mistakenly or deliberately say they were working at a client’s home when they were not, and the client knows they were not, it hurts relations with the client. It also potentially the firm’s reputation in the market. 

There were a few occasions when employees made mistakes or deliberately falsified time sheets and were found out by clients who checked their bills, Wiens admitted. “This company was built solely on reputation,” she said. “We work in a small [market area]. Everything comes from, ‘You did work for this person and we love what you did, so we want you to work for us.’ Anything negative out there can be detrimental to our business”

The problem goes deeper, though. If workers incorrectly report that it took 70 hours to do work the firm’s estimators figured would – and actually did – only take 60 hours, estimators are likely in future to issue higher bids on similar projects to cover the expected labor costs. “And that makes us less competitive,” Wiens pointed out.

The company is already seeing some improvements in its competitiveness because it has a better handle now on how long it really takes workers to do jobs. Managers are also analyzing the data to figure out how long it takes particular workers to do particular tasks so they can put together the best team to work on a project given the type of work involved.

The really dramatic savings came from an initially unplanned source. Until it implemented the ADP/Xora system, R&J issued workers with gas credit cards they could use to fill their trucks to get them from site to site. The trouble is, some were abusing the company’s trust, using the cards to fill their spouse’s cars or even their boats.

Now the company pays employees a per-mile rate based on the actual travel they do in the course of their work – which it can calculate precisely from the GPS data Xora captures. The new system is saving the company a whopping $6,000 a month in travel costs. After several months of using the new system, mileage costs have stabilized and are quite consistent, Wiens said. So the savings should be repeated each year.

R&J expects additional soft benefits. The data from the Xora system will help it prove to clients that it actually performed work it claims in its detailed bills, eliminating potentially relationship-damaging contention. Employees also won’t be able to dispute the paid hours they worked because the evidence from the Xora data will be incontestable.

And the company is using the system to ensure it’s in compliance with government labor regulations that require employees to be given a lunch break if they work longer than a five-hour shift. It sends messages to employees on their cell phones reminding them they need to take a break, and monitors data from the Xora software to make sure they actually log off work for the required period.

The fact that both the Xora and ADP services are Web based is another boon. It means that if Wiens, who is responsible for monitoring the Xora position- and time-tracking data, is at home on a day off and her boss needs information about where a particular worker is, she can easily log in to the system from her home computer and get it. “I love that it’s Web-based,” she said.

The system isn’t perfect, though. When one employee didn’t want the company to find out that he was taking an unscheduled side trip to get lunch while on an errand, he left his phone at the work site so it would look like he was still there. Unfortunately for him, he was spotted at a McDonalds drive-through.

“It comes down to hiring the right people,” Wiens said. “If someone wants to steal from you or be lazy, which is the equivalent of stealing, short of permanently attaching [the phone] to their person, there’s not much you can do about it.” Unless, as in this case, you get lucky.

There was some resistance to the new system. “They’re guys,” Wiens quipped. “Of course they resisted change.” Some objected to “the whole big brother is watching thing.” But those who had nothing to hide embraced it, she said, and it’s so easy to use that nobody had any trouble learning the new procedures.

R&J purchased an already-integrated suite of services from ADP and Xora. It works very smoothly, and the odd time it doesn’t, or when the company wants to explore adding new functionality, Xora and ADP are very responsive, Wiens said. “Both companies have been absolutely great.”

and online publications since the 1980s.

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5 Ways Big Data Analytics Can Help Your Business

More and more businesses are embracing the concept of big data versus treating it like just another buzz-phrase.

Once heralded as “the next big thing,” adoption of big data analytics is at an all-time high with no signs of slowing down anytime soon. With big data and business analytics software projected to reach nearly $200 billion in revenue by 2023, it’s clear that the business world’s decision to bet on data has paid off so far.

So, what’s the catalyst for such rapid adoption in the first place?

After all, not all data is created equal and the need for massive numbers varies from company to company. According to a 2023 big data survey conducted by NewVantage, the top reasons for big data initiatives include decreasing expenses, exploring innovation opportunities and launching new products and services:

Although big data has uncovered new opportunities for businesses to reel in revenue, it’s also created a slew of challenges for marketers.

According to analytics firm SAS, the most common problems presented by big data to marketers are three-fold:

Determining which pieces of data to gather: with so many moving pieces of any business, it’s natural for marketers to find themselves in a situation where they’re drowning in a sea of numbers

Picking between analytics tools and platforms: more data means more tools, which means more picking and choosing on behalf of marketers already saddled with time and budget constraints

Turning data into action: while it’s easier than ever to acquire mounds of data at a moment’s notice, the act of spinning that data into gold is easier said than done

Does that mean that all hope is lost for marketers looking to benefit from big data?

Absolutely not.

After all, data-driven marketing has become the norm of today’s businesses. Rather than trust assumptions or gut feelings, modern marketers are making decisions by the numbers available to them. In fact, spending on data-driven marketing was up over 60% between 2024 and 2024.

1. Better Analytics = Better Design

As noted in the NewVantage survey, some of the greatest value of big data comes in the form of decreased expenses and faster launch of new products and services. This is being played out in the design world, where data is helping machines learn how to create sophisticated branding elements.

Your logo is the anchor of your brand, but getting one created can be a costly and lengthy affair:

Do it yourself, and you risk missing key elements that designers have been trained to understand.

Online platforms like Tailor Brands are eliminating the need for expensive designers and creative teams, getting brands up and running quickly and inexpensively. They’ve discovered how to take a user’s subjective input about their brand, and apply that to the huge amounts of data collected through their user base to provide machine-generate designs in minutes.

The system makes artistic decisions around colors, typefaces and layout based on design best practices and user feedback, essentially providing access to a massive database of design knowledge. Because their system is set up to continuously learn from all user input, they are able to spot design trends and preferences too, continually improving results.

All of this means brands no longer face the expense of working with logo design teams and can get out there and start marketing in record time.

2. Perfectly Timed Content

Speaking of time, marketers today face some major pain points in regard to content. That is, squeezing the most out of each and every piece we publish is much easier said than done.

Fortunately, analytics can play a major role when it comes to timing and content distribution.

Consider how Growbots’ email marketing platform optimizes send times based on engagement and the peak activity of email subscribers based on data from over one million cold campaigns.

The results of their analysis are nothing to scoff at, either. According to Growbots, email delivery optimization has the potential to nearly double the conversion rate of any given campaign.

Collecting data on followers and subscribers ultimately teaches marketers the best window to reach them, time after time.

This same logic can be applied to the world of social media, too. That’s why solutions such as social scheduling tool Sprout Social created its “ViralPost” platform which automatically schedules tweets and posts in conjunction with the online activity of relevant influencers. This sort of scheduling clues us into both the power of data and automation for today’s marketers.

Big data often reminds us of a rather obvious detail of any given marketing strategy: we can’t be everywhere at once. With these tools on deck, however, the task of marketing around the clock actually becomes a reality.

3. Boosting Sales

Given the cost and legwork involved with leveraging big data, there should be a financial incentive for hopping on the bandwagon, right?

Luckily, there is.

Take the world of ecommerce, for example, where a keen attention to analytics could potentially make or break a business. As noted by Dataconomy, big data has huge implications for sales as it applies to…

Optimized pricing: by tracking purchases and trends in real-time, brands can ultimately identify patterns that result in higher profits (something that 30% of businesses fail to do year after year)

Demand: big data analytics can forecast needs for inventory and essentially prevent the need for a business to ever be out of stock

Predicting trends: keeping a close eye on industry data provides opportunities to determine which products are buzzing with consumers and what’s falling flat.

For marketers making digital sales, even the most minor details uncovered via analytics could result in major profits or losses. Again, the information gleaned by big data often represents points that many marketers wouldn’t think twice about until they were aware of where they might be going wrong.

4. Conversion Optimization

Yet the degree to which big data analytics can help accomplish these goals may be less obvious.

Bear in mind that 48% of big data is attributed to customer analytics, meaning that drilling deep to understand customer behavior should be a matter of “when” not “if”.

The rise of big data is a stern reminder for marketers to take a data-driven approach to conversion optimization. With variables such as headline and CTA copy to color scheme and imagery, there’s plenty to consider on any page of your site or store.

The more data you have to assess the behavior of your traffic, the better.

5. Promoting Personalization

 With so much emphasis on metrics in regard to big data, it’s easy to forget the people and relationships behind those same numbers.

The concept of big data creating more personalized experiences may seem like an oxymoron but just take for example how chatbots are being used to boost customer satisfaction.

For example, the more a fashion chatbot for a brand like H&M “talks” to a customer, the more it learns about their preference in terms of products. The bot is then able to come up with personalized product recommendations as a result:

While marketers aren’t expected to rely on robots, they are expected to regularly gather data from customers in pursuit of a more personalized experience.

Even beyond the world of bots, Amazon’s recommendation engine is a prime example of personalized recommendations via data collection. Considering that lack of personalization annoys nearly three-quarters of all consumers, the key is for marketers to deliver relevant recommendations only.

And although personalization is considered a must-do, 39% of marketers note that a “lack of data” is their biggest challenge toward making it happen.

Therefore marketers looking to get closer to their customers should learn more about them sooner rather than later. Through the power of big data analytics, that crucial personal connection is more than possible.

Breaking Down the Benefits of Big Data

13 Big Data Analytics Tools & Software (2023 Update)

Big Data Analytics software is widely used in providing meaningful analysis of a large set of data. These software analytical tools help in finding current market trends, customer preferences, and other information. A large amount of data is very difficult to process in traditional databases. So that’s why to use big data tools and manage the huge data size very easily.

Following is a handpicked list of Best Big Data Analytics Tools, with popular features and latest download links. The list contains both open-source(free) and commercial(paid) software.

Top Big Data Tools (Big Data Analytics Tools): Free & Paid

Zoho Analytics is a tool that provides visual analysis and dashboarding. It allows you to connect multiple data sources, including business applications, databases, cloud drives, and more.


Offers visual analysis and dashboarding.

It helps you to analyze data in depth.

Provides collaborative review and analysis.

You can embed reports to websites, applications, blogs, and more.

Support: Live Chat, Email, Online Form

Free Trial: 15-Day Free Trial (No credit card required)

Visit Zoho Analytics

15-Day Free Trial (No credit card required)

Atlas.ti is all-in-one research software. This big data analytic tool gives you all-in-one access to the entire range of platforms. You can use it for qualitative data analysis and mixed methods research in academic, market, and user experience research.


You can export information on each source of data.

It offers an integrated way of working with your data.

Allows you to rename a Code in the Margin Area

Helps you to handle projects that contain thousands of documents and coded data segments.

Supported platforms: Mac, Windows, Web, Mobile App

Support: Live Chat, Email

Free Trial: Use free up to 5 day within a 90-days period

5-Day Free Trial


Reliable analytics with an industry-leading SLA

It offers enterprise-grade security and monitoring

Protect data assets and extend on-premises security and governance controls to the cloud

High-productivity platform for developers and scientists

Integration with leading productivity applications

Deploy Hadoop in the cloud without purchasing new hardware or paying other up-front costs

Support: Email, Online Form

Free Trial: Free $200 credit to use in 30 days

Free $200 credit to use in 30 days

4) Skytree

Skytree is one of the best big data analytics tools that empowers data scientists to build more accurate models faster. It offers accurate predictive machine learning models that are easy to use.


Highly Scalable Algorithms

Artificial Intelligence for Data Scientists

It allows data scientists to visualize and understand the logic behind ML decisions

Skytree via the easy-to-adopt GUI or programmatically in Java

Model Interpretability

It is designed to solve robust predictive problems with data preparation capabilities

Programmatic and GUI Access

Support: Via Phone, Letter, Email

Free Trial: Not Available

5) Talend

Talend is a big data analytics software that simplifies and automates big data integration. Its graphical wizard generates native code. It also allows big data integration, master data management and checks data quality.


Accelerate time to value for big data projects

Simplify ETL & ELT for big data

Talend Big Data Platform simplifies using MapReduce and Spark by generating native code

Smarter data quality with machine learning and natural language processing

Agile DevOps to speed up big data projects

Streamline all the DevOps processes

Support: Live Chat, Email, Online Form

Free Trial: Free Basic Version

6) Splice Machine

Splice Machine is one of the best big data analytics tools. Their architecture is portable across public clouds such as AWS, Azure, and Google.


It is a big data analytics software that can dynamically scale from a few to thousands of nodes to enable applications at every scale

The Splice Machine optimizer automatically evaluates every query to the distributed HBase regions

Reduce management, deploy faster, and reduce risk

Consume fast streaming data, develop, test and deploy machine learning models

Support: Email, Online Form

Free Trial: Community Free Version

7) Spark

Apache Spark is one of the powerful open source big data analytics tools. It offers over 80 high-level operators that make it easy to build parallel apps. It is one of the open source data analytics tools used at a wide range of organizations to process large datasets.


It helps to run an application in Hadoop cluster, up to 100 times faster in memory, and ten times faster on disk

It is one of the open source data analytics tools that offers lighting Fast Processing

Support for Sophisticated Analytics

Ability to Integrate with Hadoop and Existing Hadoop Data

It is one of the open source big data analytics tools that provides built-in APIs in Java, Scala, or Python

Support: Email

Free Trial: Free Version

8) Plotly

Plotly is one of the big data analysis tools that lets users create charts and dashboards to share online.


Easily turn any data into eye-catching and informative graphics

It provides audited industries with fine-grained information on data provenance

Plotly offers unlimited public file hosting through its free community plan

Support: Email, Online Form

Free Trial: Free Basic Version

9) Apache SAMOA

Apache SAMOA is a big data analytics tool. It is one of the big data analysis tools which enables development of new ML algorithms. It provides a collection of distributed algorithms for common data mining and machine learning tasks.


Pluggable architecture that allows it to run on several DSPEs

Support: Email

Free Trial: Free

10) Elasticsearch

Elasticsearch is a JSON-based Big data search and analytics engine. It is a distributed, RESTful search and analytics engine for solving numbers of use cases. It is one of the big data analysis tools that offers horizontal scalability, maximum reliability, and easy management.


It allows combine many types of searches such as structured, unstructured, geo, metric, etc

Intuitive APIs for monitoring and management give complete visibility and control

It uses standard RESTful APIs and JSON. It also builds and maintains clients in many languages like Java, Python, NET, and Groovy

Real-time search and analytics features to work big data by using the Elasticsearch-Hadoop

It gives an enhanced experience with security, monitoring, reporting, and machine learning features

11) R-Programming

R is a language for statistical computing and graphics. It also used for big data analysis. It provides a wide variety of statistical tests.


Effective data handling and storage facility,

It provides a suite of operators for calculations on arrays, in particular, matrices,

It provides coherent, integrated collection of big data tools for data analysis

It provides graphical facilities for data analysis which display either on-screen or on hardcopy

12) IBM SPSS Modeler


Discover insights and solve problems faster by analyzing structured and unstructured data

It has data analysis systems that use an intuitive interface for everyone to learn

You can select from on-premises, cloud and hybrid deployment options

It is a big data analytics software that quickly chooses the best performing algorithm based on model performance

FAQ ✅ What is Big Data?

Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.

❓ What is Big Data Tools?

The tools that are used to store and analyze a large number of data sets and processing these complex data are known as big data tools. A large amount of data is very difficult to process in traditional databases. So that’s why we can use big data tools and manage our huge size of data very easily.

✅ Which are the Best Big Data Analytics Tools?

Here are some of the Best Big Data Analytics Tools:

Zoho Analytics


Microsoft HDInsight



Splice Machine


🏅 Which factors should you consider while selecting a Big Data Tool?

You should consider the following factors before selecting a big data tool

License Cost, if applicable.

Quality of Customer support.

The cost involved in training employees on the tool.

Hardware/Software requirements of the big data tool.

Support and Update policy of the big data tool vendor.

Reviews of the company.

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