Results for Big Data

Glassdoor’s Suggestion On The Best Cloud Computing Companies – Where You Can Find The Highest Levels Of Satisfaction At Work In 2018

October 25, 2018
Glassdoor (One Of The World’s Biggest Job and Recruiting Sites) To Partner With Cloud Investor Battery Ventures (A Global Investment Firm) for Second Year to Determine and Reveal Top Highest-Rated Private and Highest-Rated Public Cloud-Computing Companies – Where You Can Find The Highest Levels Of Satisfaction At Work In 2018.

With cloud computing on the rise, exceptional cloud growth, and cloud computing companies among the hottest in tech, the most common question among employers is “Which publicly and privately held companies are really the best to work for in 2018? You might be wondering: “Is workplaces’ culture and employee happiness in the current, ultra-competitive tech economy crucial?” Very important! The higher levels of satisfaction at work, the better results of their work will be shown.

With some cloud companies are growing much faster than others in the cloud computing market, it takes time to identify top best cloud computing companies and CEOs to work for this year. This list uncovering the Highest Rated Public and Private Cloud Computing Companies to Work For in 2018 represent those where employees’ satisfaction at work is reported at its highest. In this article, I will tell you more about the most excellent companies.

Glassdoor’s Suggestion On The Best Cloud Computing Companies In 2018


While some researchers consider Amazon Web Services the best cloud computing company, Glassdoor says that title actually belongs to Mendix - a low-code software platform founded in the Netherlands in 2005 - in the PaaS and hosted private cloud segment.

As the fastest and easiest high-productivity platform as a service (PaaS) with its membership in the Cloud Foundry Foundation to create and continuously improve multi-channel applications at scale, Mendix promises business and IT to have such a pleasant time working together. Also, the speed at which they can realize value is superior.


 As a multi-tenant cloud PaaS platform, Reltio supplies highly customized products and services to help with machine learning across all industries. The super modern data management platform of Reltio solves the hardest data management problems for any industry use case. Regardless of the size of the enterprise, Reltio’s products promise to use a wide variety of anonymized data to help them to grow faster, reduce the expense spent on IT, and remain scalable.


Zoho provides your entire business with more than just a single product or a tightly favorite apps’ integration. The cloud services will aid to cope with such business processes across your organization as 40+ integrated applications hosting and running, business intelligence, database management, manage all day-to-day activities, eCommerce hosting, boost sales, email hosting, ERP, step up productivity and collaboration, web content management, and website hosting.

Google Cloud Platform

A PaaS (Platform as a Service) platform, Google Cloud Platform – the most valuable brand in the world as of 2017 provides tools and services to build and deploy cloud-ready sample apps, extensions while still helping business users to minimize the operational costs. Also, Google Cloud Platform helps to increase their productivity by opening integration to connect any digital products regardless of who developed them and where they are.

Among 108 offering services and products, users are spoilt for choices to operate compute process, network (CDN, VPN), storage (Cloud Storage, Persistent Disk, Cloud Storage for Firebase, Cloud Filestore), management, security, data management (SQL, MySQL, PostgreSQL, and NoSQL) as well as analytics and machine learning (Apache Airflow), AI, Internet of Things (IoT), mobile, Blockchain, integrations and migration systems.

SAP Cloud Platform

SAP Cloud Platform
An in-memory data platform-as-a-service (PaaS) by SAP SE, SAP Cloud Platform allows connection and integration with other data and business processes in a secure cloud computing environment.

SAP HANA database management system - a strong brand that has received widespread recognition in the business and technology world rapidly became the #1 growing technology solution among all cloud computing companies including Asian Paints, Coca-cola, Accenture, Mercedes-AMG, eBay, Lenovo, Infosys, Colgate Palmolive, Sandisk, Unilever, and Cisco. Starting from its inception on October 16, 2012, and being deployed as an on-premise appliance or in the cloud on May 13, 2013, SAP HANA now has more than 815,000 active customers, who consider SAP HANA as their most popular cloud tools.

Their services cover mobile services, storage, database and storage, integration & orchestration, migration, developer tools, management tools, media services, digital experience, security, data management & analytics, machine learning, app integration, solution extensibility, customer engagement, SAP Cloud Platform Internet of Things (IoT), business productivity spheres.

Netskope Security Cloud

Netskope Security Cloud
Best known as “cloud-security platform for the enterprise,” the Netskope Security Cloud Platform offers the world’s largest organizations unmatched vantage points. Also, it provides fast and intuitive visibility into sanctioned or unsanctioned cloud experience for any individual and application over any web without sacrificing security. Thus, 100 percent of users on-premises, mobile, and remote as well as a lot of companies including Levi’s, ILM, Toyota, and nVidia can deliver superior digital experiences and successfully migrate billions of transactions across thousands of services.


Outsystems Cloud provides PaaS (Platform as a Service), all to accelerate business transformation without the hassle of managing physical software infrastructure. A perfect solution to build web applications like Outsystems is expected to get much more success in the cloud computing sphere in the near future.
Glassdoor’s Suggestion On The Best Cloud Computing Companies – Where You Can Find The Highest Levels Of Satisfaction At Work In 2018 Glassdoor’s Suggestion On The Best Cloud Computing Companies – Where You Can Find The Highest Levels Of Satisfaction At Work In 2018 Reviewed by thanhcongabc on October 25, 2018 Rating: 5

Top simplest ways for effective data analytics

July 25, 2018
Meta description: The skill namely data analytics maybe something extremely advanced and macro to various people. Here’re some simple ways to conquer this skill.


Good data analytics is the key to your success, especially in the increasingly developing world today. Therefore, improve this essential strategy will make you more competitive in increasing demands of the current labor market.

How often do you assess your data analytics? If you are still not confident enough to answer this question, it’s time to read this entire article provided with the simplest methods for effective data analytics.

If you are not good at data analytics, learn more about the proper training to help you improve your analytical skills right. Although good data analysis can’t be learned and enhanced overnight, it can be boosted faster with daily practicing.

We gave out top 7 habits to improve highly effective data analytics that can support your work, business, and life matters better. Let’s check it out!

How effective data analytics important to humans

Strength in the effective analytical skills will assist people in both your business and daily life. Regarding work, effective analytical skills will aid you to manage and execute your projects, get new ideas, and solve technical problems well.

If you have been spending years to find out the way for productive business without the little hope of success, take a look back at your data analytics. This skill was lowly appreciated and paid attention to in your life and work.

Think again to see if you did not invest properly in good data analytics or not. And, read through the lists of what an effective analytical skill will offer you as mentioned below.
Good analysis skills are extremely important to the human in:
  • Good information gathering and reporting 
  • Solutions for both simple and complicated  problems
  • Support the making of important decisions
  • Summary of big statistical data
  • Determine the trends of the issue
  • Streamline the workflow well 
  • Increase the project effectiveness
What decides the success of people does not depend on the emotions but the right decisions. The better the decision is, the closer you are to succeed. Moreover, people with good analytical skills are closer to success than others.

These successful individuals can’t be influenced by their emotions, passions, or external effects. Instead, they know how to apply their analytical skills to overcome the challenges, no matter how complex or critical the difficulties are.

Top simplest ways to practice effective data analytics  

It is obvious that the right decisions will create the positive results whether you are running a business yourself or simply trying to gain promotion at work. Having sharp analytical skills will make your plans change in a whole new way.

There are a number of effective tricks to increase the ability to analyze data in studying and business. Enhancing your analytical skills will create more opportunities for your future development.

That’s why you need to seek yourself more aggressive and active data analytics proactively. Here are some suggestions on how to improve your data analytics.

Start reading more books

It is time to begin your reading adventures today and read books as much as you can. You may wonder if the real effects of book reading to the improvement of your data analytics because it is an extremely simple trick to apply.

However, we’re sure that you will be amazed by the hidden miracles inside this method. By asking several questions about what you’ve learned when reading books, you are progressing your brain thinking more than usual.

Another useful technique to enhance the ability of data analytics is to ask others questions and compare the writer’s views with your thoughts. Take note new ideas, thoughts, and achievements that you achieve after reading any book.

This active reading method will encourage your brain to acknowledge new information as well as allow you to think in several new ways.

Take a walk outside

You should better go out daily for a walk because this simple activity will help you improve your data analytical skills incredibly. Always remember to put your mind to the active state by observing carefully what you can see around.

And, think of things surrounding into detail too. Try to use your senses as much as possible and ask proper questions about what’s going on surround you.

How much information can you remember after a walk outside? Try to remember what you observed the previous day and rewrite them on your textbook the next day.

Practice Math-related games.

As much as we know, Math is the best subject for perfectly logical thinking and data analyzing skills. You should play some simple Math-related games such as Sudoku and logical, analytical games regularly.

Those fun game are good at speeding up the improvement of your data analytics.

Play games with the highly logical thinking skills

Add some games that require highly logical thinking skills on your favorite game list is also effective for better data analytics. Chess, Bridge card-game, and other brainstorming games with strategic moves are simple tricks for your mind.

Also, both Treasure hunts and Quizzes are extremely useful to increase your data analytical skill. These games are entertaining, interesting without any requirement to take too much effort for them.
Moreover, they help bring mild joy to the whole family with the great cohesion power if you play with your beloved family members.

Focus more on daily conversations. 

When talking to anyone face to face, you should ask him more questions and try to learn something brand new from them. New skills can be the best ways to deal with stress at work, how to develop a successful career or parenting experiences.

Asking questions regularly while talking is essential to keep you engaged actively in the conversation. You can also develop your better communication skills as well as making numerous new friends.


By improving and developing your data analytics, you will improve the completely different mind with new personal abilities. It is the direct way to add new knowledge, handle complex information, and give effective solutions for a successful career.

Thanks to data analytics’ benefits, we synthesize top simplest tricks to practice the skill for you. Remember to practice regularly because if you skip practicing regularly, failure may welcome you soon.
Top simplest ways for effective data analytics Top simplest ways for effective data analytics Reviewed by thanhcongabc on July 25, 2018 Rating: 5

Use data analytics to support the business success

July 17, 2018
Meta description: To support the business success with effective data analytics is a big question to all entrepreneurs. Our article will give you the exact answer.


Today, most of the businesses are aware of the importance of data analytics, nearly 90% of the business leaders think it will change their businesses in a right direction to success.

Most of the investors are purchasing Big Data projects to gain competitive advantages in developing customer relationships, redefining product development, and changing the way the business operation.

However, to make an effective investment in data analytics is not just easy like that. If your goal is to apply data analysis to your business, you should have a clear understanding of Big data and data analytics to make use of them.

After having a good understanding of these essential things, it’s time for you to apply them in running your business effectively. There are various tips that you wish you knew before to drive your company in the right way to gain the achievements.

Use data analytics to support the business success

Making use of data analytics is never an easy issue to the entrepreneurs, especially the leaders. Here are some suggested ways for you.

Always be ready to attract the customers 

The very first step in exploiting the effective data analytics to improve the customer loyalty is to identify the business model of your company. Two main formats include registered and non-registered members.

Membership who attended as the registered members can be easy to identify as a target customer group. For the unregistered business model, the entrepreneurs need to have the plans to identify the target customers among them regularly.

Ways to attract the loyal customers include customer services to such as offering special offers through phone care, phone applications, or sending discount offers.

Control or evaluate the customer satisfaction through customer surveys in the business websites, poles or questionnaires. Moreover, you should have long-term plans to reduce the obstacles for the customers in the easier purchasing process.

Analyze the suitability of the products or services with the needs of customers to understand what the customers are interested in [1]. This tip will help you find the right path to adjust and produce the amazing products that fit their needs and tastes.

Understand the decision-making process

While your ideas and emotions are easy to change, data analytics is the basis for empowering the leaders to make rational decisions. However, a business does not have to need data analytics to be perfect and successful.

It is extremely crucial to learn to ask the correct questions and then, get into the problems well to find the right solutions that can be supported by effective data analytics.

The simplest way to identify the right problems and questions is to identify the most important decisions of a leader. Based on the available data, you can analyze the wrong operation in the company to adjust the new plans for your business.

Understand the wrong strategies in business can help an entrepreneur create the more careful plans for the next projects and a more suitable rational business operation.

Never complicate the data analysis

A talented data scientist will not handle every business problems. They know clearly how to choose and focus only on the main issues that influence greatly on the value of the business and think of the effective solutions for that problems.

Think simply on the data analytics to support the successful activities of a business. The key point is to simplify the analysis to make those data usable and provide full advantages for you to plan for the right way of the business management.

Find the relevant factors

To solve a problem of a business, it is essential to find out the dependent variables to solve the problem and analyze the independent variables.

Independent variables are not always clear while doing any researches for data analyzing. The initial requirements of a data analysis project can cause the misidentification of the business goals or business metrics to be evaluated well.

For example, if the directors asked you to increase the number of customers, you should understand that the leaders want to increase the total revenue. And, the rate of return is the main indicator that the director cares about.

Therefore, to find the important index that needs for the data analysis, a discussion with all the related people of the project should be organized. During the discussion, try to identify the right things concerned by the business leaders.

Set suitable standards

Set the standards that help to show team effectiveness and the goals for the whole team to work on. To do this, you need to collect the old data, internal and external impacts to find out an accurate standard.

First, you need to search for the current benchmark of the peer companies to compare yours with that one. Combine it with the information of your business resources to get an overview of your business effectiveness to others.

Thanks to that activities, you can set the most suitable standards that fit your company.

Make plans for periodic reports

Once you set up the measurable standards and objectives for your company, you need to plan your implementation for periodic reports to achieve the final goals. This activity supports the leaders in accessing the working effectiveness of individuals.

Periodic meetings allow you to have the right adjustment for the plans to help increase the efficiency of your work operation. Each team or department in the company with high effectiveness will boost the business success faster

Monthly reports illustrate the whole performance of all departments not only helps all the staffs use the data more in their work but also uses data analytics more wisely to increase the team effectiveness.


Using data analytics to aim at success for your business is not very easy, but you can learn to improve it gradually. We hope that you can have the best methods to keep your business going on the right track with our suggestions.
Use data analytics to support the business success Use data analytics to support the business success Reviewed by thanhcongabc on July 17, 2018 Rating: 5

All You Need To Know About Big Data Analytics

July 11, 2018
In the modern life, the big data analytics’ significant impacts and benefits are undeniable. If you’ve had no idea on what big data analytics is, please keep reading our article to stay updated.


Big data analytics investigates a huge amount of data to reveal the hidden correlations, insights, and patterns. With the fast-paced development of technology, it’s easy as a piece of cake to analyze your data and find out the answers from it. Understanding big data analytics thoroughly will be a big advantage as it could help you to develop your business faster than ever. If you want to grasp more useful information about big data analytics, this article is written for you. So keep calm and read our article.

Big data analytics’ history and evolution

The concept of big data has been known for years; all organizations understand clearly that if they could collect all the data that pours into their businesses, they could analyze the data and get considerable value from it and drive profits. In the 1950s, decades before anyone expresses the term “big data,” enterprises were making use of basic analytics to discover trends and insights.

The new advantages that big data brings to the table are speed and effectiveness. While in the past, a business gathered, analyzed and gave out information that could be beneficial and used for future decisions, nowadays, business could pinpoint insights for prompt decisions. The strong capability to work faster gives companies a severe competition that they didn’t have before.

Why is big data analytics pivotal?

Big data analytics allows enterprises to exploit their data and make use of it to create new opportunities. Hence, it leads to more effective operations, smarter business moves, more satisfied customers and of course, higher profits. In the recent report of Tom Davenport – IIA Director of Research, so-called Big Data in Big Companies, interviews around 50 enterprises to comprehend how they make use of big data. And here is what he found:

Cost reduction

Regarding storing big amounts of data, big data technologies like Hadoop and cloud-based analytics create a considerable cost advantage. Also, they could detect more effective ways of doing business.

Faster and better decision making

With the fast speed of in-memory analytics and Hadoop, came along with the capability of analyzing new data sources, enterprises are capable of analyzing information instantly and make decisions based on what they got from the data analytics.

New products and services

With the capability to discover customer demands and evaluate customer satisfaction through analytics comes the power to provide customers with exactly what they want. Davenport also points out that thanks to big data analytics, more businesses are generating new products that meet the customers’ needs and requirements.

Big data analytics in the modern world

The majority of companies have big data. Also, they understand the huge need for collecting that data and take advantage of it.

High – performance analytics help you do things that you had never thought about before as the data volumes were just too big. For example, you could get well-timed insights to make decisions about wink opportunities, get accurate and detailed answers for difficult – to – solve problems and find out new growth opportunities.

If you are looking for an analytic solution for your business, I highly recommend In-Memory Analytics which is from SAS Event Stream Processing to SAS Visual Analytics. In – memory will help you get instant insights from your data. Plus, it is used for different types of businesses:

Travel and hospitality

Keeping customers satisfied and happy plays an important role in all fields, especially travel and hotel industry. However, measuring the customer satisfaction index promptly is such a challenging task. Big data analytics give travel and hospitality businesses the capability of collecting customer data, applying analytics to identify the potential problems instantly and have timely actions.


Big data provides the healthcare industries various benefits. Analyzing and extracting deep insights into health plans, patient records, insurance information and tons of other information will be easier than ever with the support of In-memory Analytics. That is the reason why big data analytics technology is extremely vital to the stable development of health care’s organizations. By analyzing quickly loads of information in both structured and unstructured, healthcare suppliers could offer lifesaving diagnoses and appropriate treatment options immediately.


Tightening the budget without decreasing quality and productivity seems to be the most challenging obstacles for all government agencies. This is particularly trouble with law enforcement agencies which are working hard to reduce the crime rate. That is the reason why a big number of government agencies make use of big data analytics. The technologies would reorganize and update the operations while providing the agencies with a more holistic observation and perspective on criminal activity.


Customer service has developed in the past years as the shoppers expect the retailers to comprehend exactly what and when they want and need it. And of course, with the strong support of big data analytics, the retailers could meet all those demands in just short time. With analyzed numerous amounts of data from buying habits, customer loyalty programs, and other sources, retailers could not only grasp the deep insights of their customers but also predicts upcoming trends and recommend new products, hence, boost profitability.

Currently, there are a lot of organizations using SAS to support their businesses, such as Royal Bank of Scotland. In fact, SAS has changed completely the way every enterprise did their business and helped them to grow sustainably.


On the whole, big data analytics is highly important for all fields nowadays. With the solid support of big data analytics, we could shorten our process, create significant values and drive improvements. Hopefully, after reading this article, you could have grasped some background knowledge on big data analytics. Keep calm and learning, I strongly believe that you will become an excellent analyst in the near future. Wish you all the best.
All You Need To Know About Big Data Analytics All You Need To Know About Big Data Analytics Reviewed by thanhcongabc on July 11, 2018 Rating: 5

5 Wonderful Ways to Be Revolutionized In Database Field 2018

July 03, 2018
“Keep up with the latest trend of 2018 regarding database!”

Database has proven to be very effective and beneficial for financial Institutions around for years and few would dispute the benefits it brings to organizations that have more data stored than ever before. There’s a reason why the market size of database is predicted to almost certainly break past the $40 billion mark in 2018. It is strong, thriving, and constantly evolving.

1. Cognitive technologies – the real opportunities for business are on the rise

A powerful, open, and connected tool set to increasingly do tasks that once required humans, boost analytical capabilities, increase automation opportunities, and enhance the investment decision-making process, cognitive technologies, in the eyes of many leaders, are the most disruptive forces on the horizon.

Gone are the days when the computing systems only capture, move, and store unstructured data -- without understanding it: Cognitive solutions in 2018 will not only understand different types of data such as lab values in a structured database or the text of a scientific publication but drive huge transaction volumes that are hard to achieve otherwise.

Artificial intelligence-based systems trained to understand technical, industry-specific content, cognitive technologies extend the power of information technology to tasks traditionally performed by humans. And by using the advanced reasoning, predictive modeling, and machine learning techniques to advance research faster, they can enable organizations to break prevailing trade-offs between speed, cost, and quality.

2. There will be more growth in prescriptive analytics

Prescriptive analytics involves predicting the spending habits of each customer by analyzing consumers’ interaction with an online retailer when they do “site search”– transactions, web browsing, social media activity, interests, demographics, transforming it into meaningful trends, and thus, improving your customer relationship.

Applied to numerous industries and other facets of business, database is basically a roadmap to better business. Combining database with predictive analytics can be a huge benefit to any organization. Your business can connect the dots and uncover trends in your sales and customer behavior and you will be able to make really quick strategic decisions with the data you have.

For example: Predictive Analytics enable IBM’s business leaders to increase profitability, prevent fraud, and even measure the social media impact of marketing campaigns. And Microsoft — by integrating predictive analysis into their sales process, they are getting more accurate sales predictions from top down.

3. Machines Learning Speed Up Remediation

One of the main limitations of being human is simply our own bodies—and brains. But the days when people did almost everything manually are long gone. We are living in the world where a lot of human-like tasks are performed by machines. As computing power, data collection, and storage capabilities increase, Machine learning is being applied more broadly across industries and applications than ever before. And in the future, humans are expected to augment ourselves with computers and enhance many of our own natural abilities.

With the ability to generalize knowledge from data to perform tasks that human beings do naturally on a daily basis, ML, which is now being used to measure social sentiment and gauge a stock’s value before earnings reports come out, is probably the method people are most excited about right now.

4. Artificial intelligence (AI) will improve cybersecurity

Anyone running a business and the cybersecurity industry itself are not happy with the increasingly common cyber-attacks and cyber-hacks. But with the integration of AI into security systems can stem the growing and evolving cybersecurity risk facing global businesses.

As AI can process and analyze unlabeled data captured to understand new trends and details without any need for human supervision, when it comes to cybersecurity, it can quickly identify and analyze new exploits and weaknesses to help mitigate further attacks.

5. IoT - the next technological revolution will have a big impact on database

As digital and mobile technologies become part of daily life, the Internet of Things (IoT) – the emerging third wave in the development of the internet has been a major influence on the database landscape and will become a large part of database analysis in 2018.

According to Gartner – Global IT Research and Advisory Firm (excerpted from Forecast: The Internet of Things, Worldwide, 2013, published December of 2013): The Internet of Things will include 26 billion units installed and the revenue generated from IoT products and services will be exceeded $300 billion by 2020.

By generating an unprecedented amount of data, IoT is claimed to come to change the society we live in, as well as create entirely new business opportunities for companies by both academic researches and consulting firms. Both consumers and businesses alike have benefited from sensor-based analytics. An example: Customers can take advantage of a system that plays their favorite TV program as soon as they enter the room while UPS (United Parcel Service of America) - one of the largest and most successful logistics and shipping companies in the world uses sensors in its vehicles to improve delivery performance and cut costs.

With database being the axis of all important decisions made in every business, various Industries have experienced a big boom. How has database affected your business?
5 Wonderful Ways to Be Revolutionized In Database Field 2018 5 Wonderful Ways to Be Revolutionized In Database Field 2018 Reviewed by thanhcongabc on July 03, 2018 Rating: 5

How to choose the right NoSQL database

June 16, 2018
“Wondering how to choose the best NoSQL database, we’ve got you covered”

These days, NoSQL databases become a good choice for big data and analytics projects because of working effectively with large sets of distributed data. In this article, we will give you deeper insight about solutions like MongoDB, Elasticsearch, OrientDB, Hadoop and Cassandra.

1. MongoDB

Famous for being the most prevalent NoSQL database management system (DBMS), MongoDB is document-oriented and coded in C++. Invented to support high volumes of data, MongoDB carries on a logic of horizontal scalability with sharding and assists to implement a MapReduce system.

One of noticeable features of MongoDB in its 3rd version is that it allows to conduct  advanced research such as geospatial, faceted search, do research on some text as well as define the language, ignore “stop words” (“and”, “or”, “the”…in English for example). Besides, documents are stored in BSON (Binary + JSON) on computer, resulting in some disk space and a better performance.
Only accessing it through the protocol because of no API REST interface is a main downside of using this method. However, to narrow the gap, some external projects give a measure acting as the interface between an API REST and the protocol on the other side. It is possible for  Full-text search yet not in depth. It is can be inconvenience for users because of the lack of  some functionalities such as “ More like this” which is to help users to search for related documents.

2. Elasticsearch

Elasticsearch is another Another well-known cloud-based NoSQL database programmed in Java using Lucene. It is of plugins and tools that you have to pay for.

Elasticsearch has the ability  to implement complicated search on high volumes of data. Horizontal scalability becomes more effortless  since  you merely need to establish a new service. The invention of Elasticsearch has intention to prompt a ‘no SPOF’ (no Single Point Of Failure) engine -i.e. in a cluster of several Elasticsearch.  The data would be kept and the service would continue to work in case that a node would turn off. Without matching a schema,  users can store flat documents like JSON objects.

Put it in another way, using Elasticsearch as a main database system  is not good because  it’s a search engine but a database. It takes users some time before the data would be ready to work. Unlike MongoDB, Elasticsearch will do two queries to handle several documents.

3. OrientDB

Released in 2010 and a 2.0 version in 2015, OrientDB, open source and free for any use, emphasizes on graph-document.

It has no any leader nor any election between nodes from the cluster so OrientDB . In order to be more tolerant towards node failure without interrupting the service or data loss, the data is copied exactly and shared between the various nodes. By being scalable, OrientDB has set up some clusters at the class level to be more efficient. This enables you to search in the User class to find back all the users or to search in one of the clusters to limit the number of results. Besides, OrientDB helps you find quickly relationships with a native function especially when using a social network to find and suggest to users the friends of friends at different stages.

In spite of promises OrientDB made, we do only find few user feedback from a production with a large amount of data. The community isn’t quite big around this tool, which can be quite frightening if a problem might occur.

4. Hadoop & Hive

Hadoop is a Java framework helping some tools from the same ecosystem connect onto it. Thanks to MapReduce jobs, Hadoop abstracts the fact that the load is handed out and run as if the data was stored on one disk. And, Hive- a Java software, will connect itself onto Hadoop and run queries close to SQL syntax
How to Choose a Cloud Database Provider Correctly
How To Choose A Good Cloud Database
NoSQL Database In The Modern Technology
In the process of working, Hadoop aims to analyse a enormous volume of data shared through some servers. Take it as an example. To retrieving all the tweets with a particular hashtag to analyse the level of satisfaction towards a brand is one of useful functions of it.
One of downsides of this solution is that SQL queries are compiled in MapReduc job for a small-sized data or this tool is not suitable for many servers. Due to not being a search engine, Hive does not undertakes a ‘full-text’ search or faceted search.

5. Cassandra

Invented by Facebook and released in 2008, Cassandra is column-oriented and open source. It is the preference’ s big companies: eBay, Netflix, Github, Spotify, Instagram…

One of great functions of Cassandra is that it can help a strong scalability and to guarantee a high availability. The power is enhanced in proportion to nodes added as users add a Cassandra node within a cluster. Put it in another way, no need to worry about adding a node as it can be the case with other DBMS.

The schema is supposed to be specified in advance since the system remains column-oriented. In duration of retrieval, it is harsh. Also, Retrieval is not exhaustive, no like, no ‘full-text’ or faceted search.

To sum up

Each tool is given to deal with issues arising on specific projects. Combining NoSQL DBMS and/or also add a SQL solution like MySQL or PostgreSQL would be the best solution.  From all given information mentioned  above,  you will select the best fit for the desired task.
How to choose the right NoSQL database How to choose the right NoSQL database Reviewed by thanhcongabc on June 16, 2018 Rating: 5

How Do Big Data And Machine Learning Help Companies?

May 20, 2018
What do you know about big data and machine learning? How can they help us? Keep reading my today article.

It is undeniable that in our modern technology, machine learning, and big data have gradually been an indispensable part. In the near future, I think that these above Artificial Intelligence products will dominate all structures in technology. Of course, when machine learning cooperates with big data, it will bring surprising effects in tackling different complicated issues. My today article will help you insight into this field.

Background of big data and machine learning

There is no doubt that combining big data and machine learning is very important in the modern technology. They also bring a lot of opportunities for businessmen.

In fact, the term “machine learning” emerged in IT from the 1990s, however, it has just shown hidden potentials when being used in some applications.

At the meantime, big data is a new product of Artificial Intelligence from 2013. According to a recent report, 90% of the global data was produced by big data in 2015 and 2016.

So, the question is why combining big data and machine learning can produce beneficial outcomes. I will show you better understandings about this issue in the following part.

Why combining big data and machine learning can bring great benefits

1. Facilitating customer segmentation

Determining a group of distinct individuals among those who have common similarities is a normal task in business. To some extent, this seems to be an essential step for all companies and corporations.

It comes as a no surprise that machine learning is very good at implementing this task. High level of accuracy of algorithms in machine learning can ensure the ability to indicate clearly similarities and differences.

Thus, your companies can take advantage of the above power of combining machine learning and big data. To fulfill this, you need to take some crucial steps:

First of all, you should determine what advantages the combination of machine learning and big data can bring to your business. If you are sure that, this implementation can produce fruitful results, it is time for you to focus more on data analytics.

Of course, machine learning does not help to solve all problems exactly but it can build segmentation infrastructure.

2. Targeting feasible and effective

It is also obvious that when you find the way to determine distinct needs for applying the combination of big data and machine learning, you will sooner or later certainly have some good results for your companies.

In some cases, you should spend time for researching into customers’ intention as well. This will help you collect more information and realistic experiences. Big data and machine learning can do this task well.

The way that Google utilizes the combination of big data and machine learning is a very striking example. Thanks to this implementation, Google can handle all complex issues with algorithms.
Why Is Your Business Data Treated With Such Little Regard?
Big Data Analytics In The Global Market
Big Data Tools Apache Spark And Azure
Ads are also specific targets of big data and machine learning. For instance, Netflix and Pixar have invested effectively in this filed and had a large amount of revenue.

3. Fostering predicative analysis

After collecting and finding information related to customer’s preferred choices and behavior, it is also necessary for companies and corporations to have possible predictions. This can ensure that the combination of big data and machine learning can produce fruitful results.

To make these anticipations feasible, you also need to apply different kinds of algorithms effectively.

4. Providing Foundations for Risk Analysis and Regulation

In many situations, big data creates chances for machine learning to analyze and synthesize quickly. The American Express used this method in finding fraud cases. They took advantage of big data and machine learning in analyzing both previous and present events.

It is highly recommended that your company or corporation can utilize this combination to cut down cases of financial fraud or deficit. IBM is a good example of this action. This organization makes use of big data and machine learning to predict financial risks and manage all revenues.


General speaking, big data and machine learning are two key elements in the modern technology. They will certainly play a superior role in the near future. Of course, if companies and corporations take advantage of these IT products, they can avoid risks and gain more benefits. There are four main reasons why combining big data and machine learning can bring great advantages for us. I do hope that my today article will provide a helpful source for you in this field.
How Do Big Data And Machine Learning Help Companies? How Do Big Data And Machine Learning Help Companies? Reviewed by thanhcongabc on May 20, 2018 Rating: 5
Powered by Blogger.