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Even if your organization is compelled to become more data-driven, many don’t know how to transform themselves out of the use-your-gut mentality and into a data-first one.

The easiest way? Take shortcuts by refusing to reinvent the wheel and following the trails blazed by early adopters. Here are 5 cool Big Data apps, along with the use cases (and end users) that are helping to change the meaning of “business as usual.”

1. Big Data application: Roambi

How this Big Data app works: One thing often overlooked in the rush towards data-driven decision making is mobility. Increasingly mobile workforces need more ways to manipulate data from a smartphone that just basic business tools, which are so often stripped down for mobile. Mobile workers need the ability to access and analyze the same business data they use in the office in order to make smart, on-the-go decisions.

Roambi contends that it was founded to solve this very problem. Roambi’s goal is to reinvent the mobile business app to improve the productivity and decision-making of on-the-go employees. Roambi re-designs the way people interact with, share, and present data from a completely mobile perspective.

Use case of note: The Phoenix Suns.  In addition to their goal of consistently performing at an elite level on the court, the Phoenix Suns are making big strides off the court through the use of analytics, which they use to help drive strategy for both business and basketball decisions.

While considered by some in the NBA as a small business in terms of the infrastructure and processes in place, in the past three years, the Suns organization has invested significant resources in not only organizing the data they accumulate, but in also guaranteeing the accuracy of that data and ensuring that it is being used by all decision makers across the organization.

Whether it’s an off-site meeting or a long road trip, as is the nature with any professional sports team, a majority of their work is done away from the office. The organization’s ownership was looking for a way to make their critical business data available wherever their decision makers were located.

As the Suns began taking steps to become more mobile, there was a healthy amount of skepticism that a mobile solution could be found that was both valuable and, more importantly, easy enough for end users (most of whom don’t have a very technical background) in the organization to adopt.

That changed when the Suns adopted Roambi. The Suns started using Roambi Analytics with their front office, organizing and visualizing key player scouting information all in one place, as well as making this information available in real time.

After the success of the initial rollout, the Suns decided to expand their use of Roambi to their back office. On the business side, the Suns optimized their operations by providing KPIs across sales and marketing, reporting on everything from ticket sales to game summary reports to in-stadium promotions to customer buying behavior to inventory – all via mobile devices, so executives were all working off of same set of numbers and were able to make critical business decisions in a moment’s notice.

2. Big Data application: Esri ArcGIS

How this Big Data app works: Esri ArcGIS, as the name implies, is a Geographic Information System (GIS) that makes it easy to create data-driven maps and visualizations.

Use case of note (in this case it is more of a partnership): In mid-July at the Esri User Conference, the company radically updated its Urban Observatory project. Developed in partnership with Richard Saul Wurman and Radical Media and originally launched last year, the Urban Observatory helps cities use the common language of maps to understand patterns in diverse datasets.

I attended the Esri UC last week and spent plenty of time playing with (and before that standing in line to get access to) the Urban Observatory exhibit, an interactive exhibit that makes it easy to compare and contrast data from cities worldwide, all on a touch screen.

At least half of the world’s population is currently living in urbanized areas. The Global Health Observatory (GHO) projects that by 2050, 7 out of 10 people will live in a city. This year, nearly 60 cities are part of the Urban Observatory.

Participation in Urban Observatory is open to every city around the globe. Any city that has data its officials would like to share is eligible to be included. In February 2024, Urban Observatory will go on permanent display in the Smithsonian Institution.

3. Big Data Application: Cloudera Enterprise

Use case of note: Cloudera has a ton of customers, but Wells Fargo and home automation company Vivent are two to pay attention to.  Wells Fargo has used Cloudera Enterprise to build an enterprise data hub.

Vivent says that it has acquired more than 800,000 customers using a variety of third-party smart-enabled devices – roughly 20-30 sensors per home. Many of those devices come in the form of thermostats, smart appliances, video cameras, window and door sensors, and smoke and carbon monoxide detectors. Without a central internal repository to gather and analyze the data generated from each sensor, Vivent was previously limited in its ability to innovate and to add higher intelligence to its security offerings.

For example, knowing when a home is occupied or vacant is important to security – but when tied into the HVAC system (which tends to be the largest contributor to a home’s energy bill and carbon emissions), you can add a layer of energy cost savings by cooling or heating a home based on occupancy. Similarly, by adding geo-location into the equation, you can begin to adjust temperature changes to a home based on the proximity to an owner’s arrival, for instance, when the owner has a connected vehicle. Studies have shown that consumers could see 20 to 30 percent energy savings by turning off HVAC systems when residents are away or sleeping.

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Top 5 Concerns For Big Data In 2023

In today’s world where the IT sector is booming, data is an aspect of research that is flourishing the most, yet is facing setbacks in terms of the value it offers to the people. The quantity of data that is produced every day, at every minute by people and machines makes it extremely difficult to save, analyze, manage and finally utilize it. Hence, the development and evolution of various kinds of tools for data analysis have helped immensely with the handling of customer data. According to research, almost 90% of  big data was produced in just the last couple of years. Apps are being developed and used to improve the service levels, utility and customer support, etc. The

Different Sources of Data

It is anyway a challenge to manage the large number of sources that produce data, let alone dealing with the amount and volume of data and speed at which it is being produced. The data originates from the organization’s internal sources like marketing, finance, etc. and the external sources like social media. This, in turn, makes the data extremely diverse and voluminous. It is anyway difficult to manage and optimize the use of this produced data irrespective of the expensive tools and varied methods and processes.  

Quality of Data Storage

With the fast-growing pace of various companies and organizations, the growth of the produced quantity of data is rapidly increasing too. It is hence becoming a huge challenge to store this data. Multiple options called data lakes or warehouses are being used to gather, store and process huge amounts of data that is unstructured, in its original format. The challenge nonetheless occurs when data lakes or warehouses try to merge this unstructured data from dissimilar sources. This is when the error occurs. Missing data, Inconsistent or unstructured data, logic conflicts, duplicates, etc. are all results of poor quality of data storage.  

Improved Quality of Data Analysis

The large sum of data produced by companies and organizations are used to come to the best probable solutions, hence obviously the data that they use must be correct and accurate in all probability, otherwise as a result, wrong decisions would be taken, which would ultimately snowball into being harmful to the future working and success of the company. This dependency on the data analysis makes it extremely important to maintain the quality of the analysis. It needs a lot of resources and people with the proper talent and proficiency in order to make sure that the information that is provided by the data produced is accurate. This process is, however, an expensive affair and is immensely time-consuming.  

People who Comprehend Big Data Analysis

It is extremely important to analyze the data that is being produced in huge amounts in order to make complete use of it. Hence, the need for data analysts and scientists arises, for the storage and optimum use of quality data. It is also important for a data scientist to have the required skills that are as varied as the job is. But, the number of people pursuing the job of a data scientist is very less as compared to the amount of data that is being produced every day. This is another major challenge that is faced by most organizations.  

Privacy and Security of the Big Data

Why Is Java Important For Big Data?

Big data refers to extremely large and complex data sets that traditional data processing software and tools are not capable of handling. These data sets may come from a variety of sources, such as social media, sensors, and transactional systems, and can include structured, semi-structured, and unstructured data.

The three key characteristics of big data are volume, velocity, and variety. Volume refers to a large amount of data, velocity refers to the speed at which the data is generated and processed, and variety refers to the different types and formats of data. The goal of big data is to extract meaningful insights and knowledge from these data sets that can be used for a variety of purposes, such as business intelligence, scientific research, and fraud detection.

Why is Java needed for Big Data?

Java and Big Data have a fairly close relationship and data scientists along with programmers are investing in learning Java due to its high adeptness in Big Data.

Java is a widely-used programming language that has a large ecosystem of libraries and frameworks that can be used for big data processing. Additionally, Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data. Furthermore, many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.

Here are some key points we should investigate where Java’s importance can be mentioned cut-shortly;

Performance and Scalability

Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data.

Java APIs

Many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.


Java is platform-independent, meaning that the same Java code can run on different operating systems and hardware architectures without modification.

Support and Community

Java has a large and active community of developers, which means that there is a wealth of resources, documentation, and support available for working with the language.

Prime Reasons Why Data Scientists Should Know Java

Java is a popular language for big data scientists because it is highly scalable and can handle large amounts of data with ease. Data science has heavy requirements, and being the top 3 listed programming languages Java can meet the requirements easily. With active Java Virtual Machines around the globe and the capability to scale Machine Learning applications, Java offers scalability to Data science development.

Widely-used big Data Frameworks Large Developer Community

Java has a large developer community, which means that there is a wealth of resources available online for learning and troubleshooting. This makes it easy for big data scientists to find answers to questions and learn new skills, which can help them quickly and effectively solve problems that arise during data science development.


Java is platform-independent and can run on a variety of operating systems and architectures, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.


In short, Java is a powerful and versatile language that is well-suited for big data development, thanks to its scalability, wide use of big data frameworks, large developer community, portability, and familiarity in the industry. It is a language that big data scientists should consider learning to excel in the field.


In conclusion, Java is a powerful and versatile language that is well-suited for big data development. Its scalability, ability to handle multithreading and efficient memory management makes it an excellent choice for handling large amounts of data.

Additionally, Java is the primary language for many popular big data frameworks, such as Hadoop and Spark, which provide pre-built functionality for common big data tasks. The large developer community also means that there is a wealth of resources available online for learning and troubleshooting. Furthermore, Java is platform-independent, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.

Machine Learning (Ml) Business Use Cases

As machine learning (ML) technology improves and uses cases grow, more companies are employing ML to optimize their operations through data.

As a branch of artificial intelligence (AI), ML is helping companies to make data-based predictions and decisions based at scale.

Here are some examples across the globe of how organizations in various industries are working with vendors to implement machine learning solutions:

See more: Machine Learning Market

The AES Corporation is a power generation and distribution company. They generate and sell power used for utilities and industrial work.

They rely on Google Cloud on their road to making renewable energy more efficient. AES uses Google AutoML Vision to review images of wind turbine blades and analyze their maintenance needs.

“On a typical inspection, we’re coming back with 30,000 images,” says Nicholas Osborn, part of the Global AI/ML Project Management Office at AES.

“We’ve built a great ML solution using Google Cloud’s tools and platform. With the AutoML Vision tool, we’ve trained it to detect damage. We’re able to eliminate approximately half of the images from needing human review.”

Industry: Electric power generation and distribution

Machine learning product: Google Cloud AutoML Vision


Reduced image review time by approximately 50%

Helped reduce prices of renewable energy

More time to invest in identifying wind turbine damage and mending it

Watch the full AES on Google Cloud AutoML Vision case study here.

AIMMO Enterprise is a South Korean web-based platform for self-managing data labeling projects. Their services can be used for autonomous driving, robotics, smart factories, and logistics.

They were able to boost work efficiency and productivity by establishing an MLOps pipeline using the Azure Machine Learning Studio.

“With Azure ML, AIMMO has experienced significant cost savings and increased business efficiency,” says SeungHyun Kim, chief technical officer at AIMMO.

“By leveraging the Azure ML pipeline, we were able to build the entire cycle of AIMMO MLOps workflow quickly and flexibly.”

Industry: Professional services

Machine learning product: Microsoft Azure Machine Learning Studio


Improved efficiency and reduced costs

Helped build AIMMO’s entire MLOps workflow

Makes it easier to deploy batch interface pipelines

Works as an all-in-one MLOps solution to process data in 2D and 3D

Read the full AIMMO on Microsoft Azure Machine Learning Studio case study here.

See more: Key Machine Learning (ML) Trends

Bayer AG is a multinational pharmaceutical and life sciences company based in Germany. One of their specializations is in producing insecticides, fungicides, and herbicides for agricultural purposes.

To help farmers monitor their crops, they created their Digital Yellow Trap: an Internet of Things (IoT) device that alerts farmers of pests using image recognition.

The IoT device is powered using AWS’ SageMaker, a fully managed service that allows developers to build, train, and deploy machine learning models at scale.

“We’ve been using Amazon SageMaker for quite some time, and it’s become one of our crucial services for AI development,” says Dr. Alexander Roth, head of engineering at the Crop Protection Innovation Lab, Bayer AG. 

“AWS is constantly improving its services, so we always get new updates.”

Industry: Agriculture and pharmaceuticals

Machine learning product: AWS SageMaker


Reduced Bayer lab’s architecture costs by 94%

Can be scaled to accommodate for fluctuating demand

Able to handle tens of thousands of requests per second

Community-based, early warning system for pests

Read the full Bayer AG on AWS SageMaker case study here.

The American Cancer Society is a nonprofit dedicated to eliminating cancer. They operate in more than 250 regional offices all over the U.S.

They’re using Google Cloud ML Engine to identify novel patterns in digital pathology images. The aim is to improve breast cancer detection accuracy and reduce the overall diagnosis timeline.

“By leveraging Cloud ML Engine to analyze cancer images, we’re gaining more understanding of the complexity of breast tumor tissues and how known risk factors lead to certain patterns,” says Mia M. Gaudet, scientific director of epidemiology research at the American Cancer Society.

“Applying digital image analysis to human pathology may reveal new insights into the biology of breast cancer, and Google Cloud makes it easier.”

Industry: Nonprofit and medical research

Machine learning Product: Google Cloud ML Engine


Enhances speed and accuracy of image analysis by removing human limitations

Aids in improving patients’ quality of life and life expectancy

Protects tissue samples by backing up image data to the cloud

Read the full American Cancer Society on Google Cloud ML Engine case study here.

“The new model assesses intersections by risk, not by crashes,” says David Slack-Smith, manager of data and intelligence at the Road Safety Commission of Western Australia.

“Taking out the variability and analyzing by risk is a fundamental shift in how we look at this problem and make recommendations to reduce risk.”

Industry: Government and transportation

Machine learning product: SAS Viya


Data engineering and visualization time reduced by 80%

An estimated 25% reduction in vehicle crashes

Straightforward and efficient data sharing

Flexibility of data with various coding languages

Read the full Road Safety Commission on SAS Viya case study here.

See more: Top Performing Artificial Intelligence Companies

Use Array Formulas With Google Forms Data To Automate Calculations

In this article, you’ll see how to use Array Formulas with Google Forms data to automatically calculate running metrics on your data.

Have you ever tried to use a formula in the column adjacent to your form responses to do calculations? You’ve copied it to the bottom of your sheet, maybe even included an IF statement for the blank rows, and now you want it to auto-calculate whenever new responses come in.

Sadly, this approach doesn’t work.

When a response is collected through the form it adds a new row under your existing data, and any formulas in adjacent columns get bumped down a row rather than being calculated. Bummer!

However, this is a perfect use case for an Array Formula. (If you’ve never heard of an array formula before, check out: How do array formulas work in Google Sheets.)

In the example above, I’ve set up a simple Google Form, which asks a user to submit a single number between 1 and 100. The form responses are collected in columns A and B of a Google Sheet (timestamp and number respectively).

The other columns contain Array Formulas with Google Forms data to calculate various metrics e.g. running totals, %, average, etc. (all made-up for the purposes of this example).

Using Array Formulas with Google Forms data, we create a single formula in the top row of Sheet, which will automatically perform calculations on any new rows of response data from the Google Form.

Note: in general, and especially if your forms are complex, you should consider keeping the response data in its own sheet and doing any data analysis in a separate sheet.

How to use Array Formulas with Google Forms data What’s the formula?

Array Cumulative SUM: To get the total of all values in column B, enter this formula in the top row (e.g. cell C2):






It uses an IF function to check whether column B is blank or not, and displays a sum only for non-blank rows.

Array % of TOTAL: To calculate the % of values in column B, enter this formula in the top row (e.g. cell D2):








Array Average: To calculate the average of all values in column B, enter this formula in the top row (e.g. cell E2):






Array IF: To create categories for values in column B, enter this formula in the top row (e.g. cell F2):

All of these will expand to fill out the entire column, displaying values for any rows that have numbers in column B. They will auto-update when new data arrives through the Google Form.

Can I see an example worksheet?

Yes, here you go.

Here’s the link to the Google Form so you can see the formulas auto-update.

How does this formula work?

Let’s run through how the first of these array formula examples, the SUM example, works.

The way to think of it is that in the first row, we effectively have this formula:






This regular formula checks if cell B2 is blank or not.

If it’s blank then the ISBLANK formula returns TRUE and our IF formula outputs a blank cell.

However, if cell B2 has a number in it (from the Form), then we put the total of column B into cell C2. The syntax SUM(B2:B) ensures that we include ALL numbers in column B into our total calculation.

Now consider the next row, where our formula effectively becomes:






It’s identical except we’re checking row 3, so whether B3 is blank or not, and outputting the IF result into cell C3.

Finally, we turn it into an array formula by putting a range into the IF ISBLANK test, and wrapping with the ArrayFormula syntax:






You only enter this formula once, into cell C2 (or whatever your top row is) and it will auto-fill the whole column.

Whenever a form response is added to the Sheet, a new number appears in column B and that cell is no longer blank. Hence the array formula updates to display the SUM value into the adjacent cell in column C.

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Big Data Protection In The Age Of Machine Learning

The concept of machine learning has been around for decades, primarily in academia. Along the way it has taken various forms and adopted various terminologies, including pattern recognition, artificial intelligence, knowledge management, computational statistics, etc.

Regardless of terminology, machine learning enables computers to learn on their own without being explicitly programmed for specific tasks. Through the use of algorithms, computers are able to read sample input data, build models and make predictions and decisions based on new data. This concept is particularly powerful when the set of input data is highly variable and static programming instructions cannot handle such scenarios.

In recent years, the proliferation of digital information through social media, the Internet of Things (IoT) and e-commerce, combined with accessibility to economical compute power, has enabled machine learning to move into the mainstream. Machine learning is now commonly used across various industries including finance, retail, healthcare and automotive. Inefficient tasks once performed using human input or static programs have now been replaced by machine learning algorithms.

Here are a few examples:

Prior to the use of machine learning, fraud detection involved following a set of complex rules as well as following a checklist of risk factors to detect potential security threats. But with the growth in the volume of transactions and the number of security threats, this method of fraud detection did not scale. The finance industry is now using machine learning to identify unusual activity and anomalies and reporting those to the security teams. PayPal is also using machine learning to compare millions of transactions to identify fraudulent and money laundering activity.

Without machine learning, recommendations on product purchases and which movies to watch were mainly by word of mouth. Companies like Amazon and Netflix changed that by adopting machine learning to make recommendation to their customers based on data they had collected from other similar users. Using machine learning to recommend movies and products is now fairly common. Intelligent machine learning algorithms analyze your profile and activity against the millions of other users they have in their database and recommend products that you are likely to buy or movies that you may be interested in watching.

For all its increased popularity and use, machine learning still hasn’t yet made its way into any part of data protection, and that is being acutely felt in big data. Specifically, backup and recovery for NoSQL databases (Cassandra, Couchbase, etc.), Hadoop, and emerging data warehouse technologies (HPE Vertica, Impala, Tez, etc.) is a very manual process with a lot of human interaction and input. It is quite a paradox that these big data platforms are used for machine learning while the underlying data protection processes supporting these platforms rely on human intervention and input.

For example, an organization may have a defined recovery point objective (RPO) and recovery time objective (RTO) for a big data application. Based on those objectives, an IT or DevOps engineer determines the schedule and frequency for backing up application data. If the RPO is 24 hours, the engineer may decide to perform backups once per day starting at 11:00 p.m.

While this logically makes sense, the answer is not as simple as that, especially in a big data environment. the big data environments are often very dynamic and unpredictable. These systems may be unusually busy at 11:00 p.m., loading new data or running nightly reports and making that time least optimal for scheduling a backup.

Why can’t the data protection application recommend the best time to schedule a backup task to meet the recovery point objective?

Another common example of inefficiency in data protection relates to storing backup data. Typically, techniques such as compression and de-duplication are applied to backup data to reduce the backup storage footprint. The algorithms used for these techniques are static and follow the same mechanism independent of the type of data being dealt with. Given that big data platforms use many different compressed and uncompressed file formats (Record Columnar (RC), Optimized Row Columnar (ORC), Parquet, Avro, etc.), a static algorithm for deduplication and compression does not yield the best results.

Why can’t the data management application learn and adopt the best deduplication and compression techniques for each of the file formats?

Machine learning certainly could aid in optimizing a company’s data protection processes for big data. All pertinent data needs to be collected and analyzed dynamically using machine learning algorithms. Only then will we be able to do efficient, machine-driven data protection for big data. The question is not if but when!

By Jay Desai, VP, product management, Talena, Inc.

Photo courtesy of Shutterstock.

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