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Why is Selenium Widely Used for Automation?

There are good reasons as to why selenium is a popular choice for automation, but what sets it apart is that it’s a complete suite, with different components for varying roles. Let’s look at what automation briefly is. What is Automation testing? In simple words, automation testing is a procedure carried out to ensure, that software runs fine and functions optimally. It’s a check that acts as a green signal for production and release. The tests run repeatedly and use scripted sequences which get further executed via test tools. Below listed are a couple of automation testing tools used often. 1. Selenium 2. Subject 7 3. TestProject 4. Parasoft 5. Avo assure 6. Cerberus testing 7. Watir 8. Telerik 9. Katalon 10. Kobiton 11. EggPlant 12. HP UFT 13. ZeuZ 14. QA wolf Start learning Manual Testing Here are some solid reasons why selenium is widely used 1. Multi-language support With support available in different languages, developers get the option of working in a programming language of their choice. The bindings to main programming languages including PHP, JavaScript, C#, Python, Ruby, Java, is a major benefit. 2. Testing on several browsers More browsers have been covered to expand the range of cross-browser automated testing including, Chrome, Yandex, Edge, Safari, IE, Firefox, Opera and others. Selenium automation framework provides the solution for executing a website’s cross-browser testing. 3. Numerous frameworks are supported Integration with multiple frameworks like ANT allows for source code compilation. Selenium can facilitate continuous delivery by integrating with Jenkins. Similarly, it can integrate with TestNG for reporting and app testing. 4. Community friendly Upgrades are available uniformly as support for selenium is moreover community-based. No special training is required. Being easy on budget makes it widely popular. 5. Maneuvering of keyboard and mouse Quite a significant feature, selenium has the ability to mimic the keyboard and mouse events as if they were initiated by a real user. The API comprises action classes needed to execute such events. Key presses, mouse clicks and holds, drag and drops, and item selection are some event examples that can be mimicked. 6. Testing on different devices Cases for automation testing on iPhones, Android devices or blackberry can now be written exclusively, which further help resolve issues of cross-device testing. 7. User-friendly implementation Selenium allows the users to script their customized extensions for the development of actions further manipulable, once the user acquires a more progressive level. Thus, easy implementation is a big plus for users. 8. No need for starting a server One of the major advantages of testing with selenium is that before testing, servers don’t require to start up. Commands get interpreted straight into web services, as they are received via HTTP request by the remote driver. These commands are further executed on the browser, which aids in sending the response. 9. Runs smoothly on Major OS 10. Automation testing selenium has many perks, including support for different operating systems (Mac, Windows, Linux, and Unix). The solution suite enables one to create a custom suite for testing to carry over any other platform, for instance, creating a test case on Windows for executing on Mac. 11. Being open-source This is the most welcome aspect about selenium is that it’s an open-source framework powered by the community providing QTP features and more without charging a penny. Users are free to download it from the official website with unlimited support readily available. 12. Usage of add-ons Compatibility testing in browsers is well-supported with scripts written in selenium framework. Multiple aspects of functionality testing are covered which makes it easier to run several testing cases. Customizable add-ons provide a bigger scope for application testing. 13. Code leveraging for speed testing Selenium facilitates speedy test cycles via development code leveraging. For instance, verifying the date tab recently updated in a database just requires the tester to pull out the data model the application has used without risking data flow automation. 14. More browser interaction Clicking front or back browser buttons is possible with selenium automation testing as no other free tools provide this level of advanced interactions. It allows testing applications that involve money transfers like fintech, also useful with banking applications that do not keep cache or cookies. Start learning Selenium Testing With so many plus points about it, selenium easily is the go-to choice in the automation testing scenario. No other open-source framework facilitates this much for practically nothing at all.

Why Full-Stack Development is Best to Upgrade Your Career?

Full-stack development is becoming a vital post as many professionals, as well as companies, realize its value. Thanks to its self-explanatory title, the full-stack developer skills do take time to hone and therefore promise heaps of profits. First, let’s look at what it means. What is meant by full-stack development? Full-stack development is a skill set owned by a developer that can work around a project's both front and back ends. It allows them to handle clients, servers, and databases. Different kinds of stacks are used depending on the requirements, some of these are listed below: 1. Ruby on Rails (PHP, SQLite, Ruby) 2. Lamp stack (MySQL, Linux, PHP, and Apache) 3. Mean stack (Angular JS, MongoDB, Node.js, Express) Why become a full-stack developer? There are plenty of reasons why being a full stack developer would benefit you in the long run mentioned here: 1. High recruitment There is a huge demand for the skills owned by a full-stack developer. The reason is simple, as they are expected to 1. Cover presentation aspects 2. Work on logic 3. Handle databases In the last 2 years, the demand has risen up to 20%, so has the full-stack developer salary in India alongside it, making them a lucrative prospect for current IT students. 2. Not hard to learn The nature of their job is to be an all-rounder, not a specialist. A student just needs enough motivation in the web development arena to learn multiple skills and get started. 3. Wide portfolio What’s more inspiring is that students don’t need to go to a university to land a job, companies are willing to accept capable candidates who are skilled with a wide-ranging portfolio. However, it’s best to learn by opting for a specialized full-stack developer course from a reputable institute. 4. No dependencies This is a job that doesn’t need much cooperation with others as far as the back and front end development are concerned. A full-stack developer's skills range widely: 1. HTML DOM 2. SASS 3. Node.js 4. C# 5. PHP 6. GitHub and Hit 7. JavaScript 8. CSS 5 9. Python 10. Java 11. Database storage 12. C++ 13. VCS 14. Web architecture 15. SOAP 16. .Net 17. REST 18. Competent design skills 19. Back end languages Tasks associated with updates, clients, integration are managed by full-stack developers. This significantly reduces the amount of time consumed in delays, as there is no dependency involved. Start learning Full-Stack Development 5. Diversified skills and credits Being an all-in-one solution that the companies seek, a full stack developer is given exclusive credit for handling a large chunk of the project by themselves. Due to their skills, all stages are taken care of, whenever a project is assigned, including UI/UX management. Start Learning Node 6. Flexibility plus control Working on both ends of a project provides them the flexibility to execute a project and apply various skills to produce a required outcome. Both creative and technical interfaces can be maneuvered by the full-stack developer, which results in having far greater control over the projects. 7. Excellent scope and high salary The full-stack developer salary is ironically similar to its designation name. Regardless of the starting point to someone having mid level experience of 5-8 years, they are expected to be paid handsome wages for what they bring to the table. In the coming years, companies will continue to look for a one-stop solution, which makes for an amazing scope in this field. Start Learning React How to become a full-stack developer? Apart from learning the skills, building a full-stack developer resume should be the priority in order to achieve this objective. Focus on the following while writing the resume, not to mention keep it updated: 1. Name and contact 2. Title (Mention the post you are interested in) 3. Education (Tell about the source of your learnings) 4. Skills (Pen the list of skills you have mastered) 5. Experience (Write down notable projects or companies you have worked with) Start Learning Angular The Verdict With full-stack developer jobs on the rise, there’s enough incentive for students to take to web development, learn and master skills through courses offered by institutes as well as online resources, making the most out of their careers as it sure is a rewarding path. These skills are not limited to geography, as students can expand their horizons globally, exploring numerous opportunities as they come.

Is Data Visualization with Power BI a Preferred Career Upgrade?

In today's data-driven age, every professional, whether IT or non-IT, has one common factor to deal with, and that is - data. So learning data science skills like data visualization is no longer a necessity for career up-gradation, it's essential.   What is Data Visualization? When you hear about data visualization, probably images of graphs and charts come to your mind. Yes, data visualization is just that - visual representation of data. However, the scope of Data Visualization goes far beyond that. An example: have you seen Hans Rosling explaining population growth with the help of plastic bins and illustrative props? If yes, then let me tell you that too is data visualization.  Why Data Visualization is important? Let's consider the Hans Rosling example once again. Growth of population and shift in demography are crucial elements on which the very existence of our species and planet rely. Also, these are extremely complex subjects. But some plastic bins and a few props make things crystal clear. That is the power of visualization. Can learning Data Visualization Upgrade your IT career? Keeping in mind the fourth industrial revolution, most of the fastest-growing careers in the tech sector did not exist a few years back. Today we have seen technologies like data science creating disruptions in the IT world, and you as a professional can't ignore the trend. And one of the most sought-after specializations in DS is DataViz.  According to job surveys- 1. Businesses are increasingly concerned about the skill gap, and employees are eager to switch to firms that provide greater skill development initiatives. 2. Data Visualization is one of the top 25 hottest skills that may get you employed, with data presentation being the essential skill set. 3. Data Visualization is a tool that small organizations are using to recognize the connected features and to gain important insights. It may be used to analyze information quicker in a graphical form or to identify trends. Companies of all sizes are seeking data visualization specialists these days. DataViz is in high demand. Visualization not only takes the power of big data into the mainstream but also serves as a bridge between technical and non-technical positions, allowing more professionals to transition into data-driven roles. Another reason it is in demand is that all types of businesses want data communicators who can utilize data to effectively depict and communicate findings to both tech and non-tech workers. There is a famous saying," a picture is worth a thousand words." According to a recent survey, the typical basic annual package for a Data Visualization professional in India is up to 13.5 lakh. Needless to say that Data Visualization is an ideal career upgrade for professionals in today's transitional era.   Is Power BI - the tool for Data Visualization? Tools, like any other technology, play crucial roles in data visualization. A couple of tools like Tableau and Power BI are dominating the DataViz market right now. Power BI is Microsoft's cloud-based business intelligence tool that enables you to visualize and analyze data more efficiently and with greater speed.  According to Gartner, Power BI is the best platform for analytics and business intelligence and a true market leader in the domain.  Start Learning Power BI Reasons that Power BI is preferred over Tableau: 1. Tableau doesn't support many features in free version, whereas Power BI offers all the features on free desktop version.  2. Power BI is an easy tool for learning. On the other hand, Tableau has a steeper learning curve.  3. Tableau is mainly suited to large organizations, but Power BI is suitable for organizations of all sizes-small, medium, large.  4. Power BI offers dashboard updates in real-time. No such feature is available in Tableau.  5. Tableau is more costly than Power BI.  6. Tableau does not support AI and ML. Power BI does. So your preferred career upgrade will be to learn Power BI as a data visualization tool.  Explore Business Analytics Program Final Words In a data-driven world, taking a course on data visualization will give you a much-needed boost in your career. And what better tool to do it than Power BI? So data visualization with Power BI is definitely a preferred career upgrade. 

How to Become a Competent Data Scientist in 2021?

Data scientist is one of the highly trending designations, IT professionals aspire to transit towards. Not to mention the hefty salary package it comes with, aside from that there’s a certain roadmap one needs to follow to strengthen their chances at being selected. To become a competent data scientist in 2021, these skills are prerequisites or data scientist qualifications that need to be honed beforehand. 1. Statistics 2. Python/R (Programming language for coding, and analysis) 3. SQL (Structured Query Language for transforming databases and saving programming time with the use of concise commands) 4. Machine learning and Artificial Intelligence (Algorithms and data usage to mimic how humans learn. Concepts include time series, reinforcement learning, outlier detection, supervised and adversarial learning, computer vision) Enrolling in a data scientist course would assist the aspirants to not only gain technical knowledge but also get certificates upon completion of their syllabus. Technical knowledge for becoming a Data scientist 1. Calculus & Linear algebra  (for building machine learning models, plotting and finding function values) 2. Statistics (for insights related to actionable data, plus exploring and understanding data precisely. Checking data anomalies) 3. Probability (for doing further analysis and estimation, example: identifying variable relationships and dependencies, future trends prediction) 4. Data wrangling (presenting data to businesses for taking action on) 5. Programming language (Julia, Python, R, TensorFlow, Scala, SQL) 6. Data visualization (via pie charts, bar charts, scatter plots) 7. Data management (via Oracle, MySQL) 8. Soft skills (improving interpersonal and communication skills for taking project responsibilities) 9. Business acumen (gaining proper knowledge about the concerned sector, understanding business issues to be resolved) The right kind of intuition to understand the problem and building solutions creatively is a must. Some more Data Science skills to master 1. Data cleaning 2. Data ingestion 3. Data analysis (Exploratory) 4. Data pre-processing 5. Decision making 6. Identifying data patterns Common Data Science Interview Questions you must have answers to While cracking the interview stage shouldn’t scare you, nonetheless, you should brush up on the basics, as in with data scientist interview questions, you could be asked to explain these concepts or interrogated about some of them. 1. Standard deviation and variance 2. Bayesian statistics 3. Mean and median 4. Correlation and covariance 5. Hypothesis testing 6. P-value 7. Selection bias 8. Confusion matrix 9. Format data (Long and wide) 10. Confidence interval & point estimates 11. Probability-based prediction questions 12. Regularization 13. Cluster sampling 14. Cross-validation 15. Decision trees 16. Collaborative filtering 17. Ensemble learning 18. Weights initialization on network 19. Hyperparameters Keep in mind these are random questions from different components of data science including Statistics and machine learning that can come your way. Having the answers handy would boost your impression of the interviewer and maximize your chances at the selection. Start Learning Data Science Different aspects of data science The data scientist career path involves: 1. Technical facets 2. Non-Technical aspects Both of which are equally important to make you the go-to candidate. You cannot rule out the ability to explain concepts in laymen’s language to clients. In 2021, you will require to master both of these to progress further in your quest to becoming a data scientist. Start Learning Python Programming Get a grip on these 1. Machine learning algorithms (Applying and implementing in the program) 2. Cloud platforms (example: Azure, Google, AWS for the deployment of models) 3. Apache spark (Computation framework for carrying out analytics to distributing company from data intake) 4. Logistic regression & decision trees (Solving problems on the basis of organizational outcomes) 5. Unstructured data (Non-streamlined data that doesn’t fit within database tables, manipulation of this provides insights for decision-making) Stand out with your Data Science resume To be noticed fairly easily, it’s important to consider keeping your data scientist resume up to date. There are some key components you will need to ensure. These include: 1. Contact (give correct information) 2. Education (mention educational qualifications) 3. Skills (list the  concepts you have mastered) 4. Work experience or projects (write down any relevant industry exposure) 5. Proper formatting (keep it brief, accurate, and bulleted) 6. Personal hobbies (share a little personal about your likes and inspirations) Conclusion Learning everything by yourself, all alone can seem like a daunting task, but opting for a course that provides data scientist certification will smoothen your IT educational & professional journey. Furthermore, the institute will introduce or encourage you to join data science communities online would keep you afloat with the latest trends in this industry. Must Read Blogs:-  What is Machine Learning & How is it making our world a better place? Top 4 Data Science Projects that will get you Job in 2021 Coronavirus: How AI & Data Science can help to Fight the Pandemic

Benefits & Uses of learning Python coding

Will you be interested in being a part of the 4th generation technologies and advance your career to a level that will secure your future? If your answer is yes, then learn the programming language called Python. Python has created a storm in the IT world in recent times. Albeit, learning this language will be nothing short of an adventure for you. Still, you may like to know the benefits and uses of Python before taking a final call on this. Benefits of Learning Python Coding According to StackOverflow, Python is the fastest-growing programming language in the world. You will find countries with larger economies are extensively using this programming language. So obviously there are clear benefits of learning this technology. High earning Potential  The demand for data science positions will see a rise of 1581% in the coming years. Python is the most sought-after programming language for data science today. So if you can land in such a profile, you will have a plethora of job opportunities unfolding before you. Python is the second most compensated coding language, according to Indeed. The salary of a Python developer ranges from $118,626 to $162,223 per annum. So once you are an expert in this language high salaries are bound to follow you.  Ease of Learning Python is a high-level programming language with a syntax that reads like English. Therefore, it is much easier to learn the language compare to most other computer languages.  Flexibility Python is not only easy to learn but also extremely flexible. There are about 125,000 third-party libraries available in Python, you to utilize the language for everything from machines to web development to even bioscience. It is also recommended for data science since its data-focused libraries, like pandas, NumPy, and matplotlib, can store, process, and display data. It's so useful that sometimes it is called the "Swiss Army Knife" of programming languages. You can also import external libraries if you find some libraries not available within the Python package by using pip which is a Python package manager.  Open-Source Language Python is a language approved by OSI and comes under an open-source license. So it is free for use and distribution. You have the flexibility of downloading the source code, change it according to your purpose, and create your Python version. This is a helpful feature when companies want the language customized for a specific function and use their versions for development.  Python is Portable  You must have seen that many languages like C/C++ need few changes before you can execute those programs on different platforms. But this is not the case with Python. Python will let you write only once and execute wherever you want.  Python Boosts productivity Python is a highly productive programming language. Python's simplicity allows developers to concentrate on the task at hand. They don't need to spend a lot of time learning the language syntaxes or behavior. You write less and accomplish more. Python is Secured Python is a secured language for programming. You have to thank a Python security project called OWSAP for that. OWSAP helps developers create a hardened version that gives them the highest level of security in a high-risk environment.  Start Learning Python Programming Uses of Python Python is a programming language that is being widely used across all verticals. So data science is not the only option you have you are done with learning this language. The fields that you can choose includes : Other Python Career Options-- 1. Data Visualization 2. Gaming 3. Business Analytics 4. Scientific and Mathematical Computing 5. Finance and Trading 6. System Automation   Must Read:- Top 5 Jobs in Trend After Lockdown Conclusion Python is popular for its ease of learning and wide acceptance among various industries. So take the joy ride of learning Python today and enjoy both a fun-filled and fulfilled career.

Top 10 Machine Learning Frameworks {Complete Guide}

There has been exponential growth in the field of Machine Learning/Artificial Intelligence and it has created a boom in the technology field. According to the reports, it has created millions of jobs and has led to the evolution of many Machine Learning frameworks. Today we will look into some of these most popularly used Machine Learning Frameworks. What is Machine Learning? Machine Learning is a part of Artificial Intelligence, consisting of algorithms that work on outputs generated from previous experiences. The more input parameters (more experiences) they get, the better the results can be expected. The uniqueness of these algorithms is that they don’t require human intervention. But, the human brain is always required to understand which machine learning algorithm will fit best for a particular situation. Data sets (inputs) are divided into training and testing sets. Machine learning algorithms work on the training data sets in order to build models for prediction and decision making. Some applications of machine learning are computer vision, collaborative filtering, natural language processing, spam filtering, etc. What is a Machine Learning Framework? ML models can be easily developed with the help of Machine Learning frameworks, without knowing the ML Algorithm. Python is the most widely used language in machine learning and so most of the ML frameworks are built for programming in Python language.  Machine Learning with R Programming is also widely used in Data Science fields.  Read More:- IS PYTHON ENOUGH FOR MACHINE LEARNING 10 Machine Learning Frameworks widely used are:- Tensorflow Keras Scikit-learn Theano Amazon SageMaker Spark ML Microsoft Cognitive Toolkit (CNTK) H20 Caffe Torch Tensorflow Google’s Tensorflow is the most popularly used framework for machine learning/deep learning. It is an open-source platform for machine learning. ML Applications can be easily built and deployed using Tensorflow. It contains a broad range of multiple libraries, tools and community resources that greatly enriches the developers' experience and makes the development easy. Keras Keras is a neural network library built on top of TensorFlow to make Machine Learning modeling easier. It simplifies some of the coding steps, like offering all-in-one models, and can use the same Keras code to run on a CPU or a GPU. Keras is a tool designed for human beings, not machines. Its features and working like load balancing, steady and easy APIs, minimizing the user inputs to execute use cases and providing simple, clear and actionable error messages greatly benefits the developer's coding experience. Scikit-Learn Scikit-Learn is the most popular and frequently in-use ML library. It features various algorithms that are designed to work efficiently with the Numpy and Scipy. These algorithms comprise regression, classification and clustering including k-means, random forests, gradient boosting, DBSCAN, SVM (Support Vector Machine).  Theano Theano is a Python library built on top of Numpy Library. It is primarily used to evaluate multi-dimensional arrays and expressions that require mathematical manipulation. Mathematical expressions are compiled to execute properly on CPU/GPU architectures and are presented in a Numpy-Esque syntax. Amazon SageMaker Amazon SageMaker was released on 29 November 2017 that provides an integrated development environment for machine learning models. AWS provides this Machine Learning service for applications such as Computer Vision, Collaborative Filtering, Image and Video Analytics, Forecasting, Text Analytics, etc. You can choose Amazon SageMaker to build, train, and deploy machine learning models on the cloud. All this can be automated using Amazon SageMaker Autopilot which has capabilities to automate machine learning models. Amazon SageMaker allows you to create ML algorithms from scratch as it is connected to Tensorflow and Apache MXNet. Spark ML Lib Apache Spark provides an interface to programmers for complete clusters. It is a widely popularly used open-source cluster-computing framework. Spark Core is the base of the Apache Spark. It provides in-memory computation to increase the speed and also allows the parallel processing of big data. Spark SQL works more efficiently and easily to optimize the structured data set. It is the distributed framework that works on structured data processing. Spark Streaming is one of the widely used live streaming & high scalable processing that ensures fault-tolerant solutions. It works on dividing the live dataset into multiple small batches before processing. Spark MLib is Spark's machine learning library which has very advanced algorithms, highly scalable and high-speed functionalities. It consists of algorithms like clustering, regression, classification, dimensionality reduction, collaborative filtering.  Read more:- BIG DATA ANALYTICS USING MACHINE LEARNING, SPARK IS HOTTEST JOB MARKET THIS YEAR Microsoft Cognitive Toolkit (CNTK) The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit developed by Microsoft Research. With the help of CNTK, users can easily combine models like recurrent neural networks (RNNs), convolutional neural networks (CNNs) and DNNs. It has explained neural networks as a series of computational steps. Automatic parallelization and differentiation across servers and GPUs can be implemented with the help of stochastic gradient descent (SGD, error backpropagation) learning. Multiple GPUs and servers are used to provide parallelization across the backend. H20 H20 is a decision-making artificial intelligence tool that provides business-oriented insights to the users. It is an open-source machine learning platform used for fraud analytics, healthcare, risk analytics, modeling, insurance analytics, financial analytics, and customer intelligence.  Read Our Blog:- WHAT IS NATURAL LANGUAGE PROCESSING? INTRO TO NLP IN MACHINE LEARNING Caffe Caffe is provided by the Berkeley Vision and Learning Center (BVLC) and by network donors. Caffe Framework is used by Google’s DeepDream. This popular learning structure is a BSD-authorized C++ library with Python Interface. It is made with the best quality and high speed. Torch Torch framework provides support for ML algorithms to GPUs first. It is built on an easy and fast scripting language LuaJIT and an underlying C/CUDA implementation, which makes it easy to use and efficient. The goal of this framework is to have maximum flexibility and speed in building your ML algorithms. If you want to Learn ML then you can Join Gyansetu's Machine Learning Training Course. 

5 Best Big Data Tools You Must Know

Data – a four-lettered word that makes the world go round. As per research conducted by DOMO, “Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.”   An increase in the number of users of the internet and influx of data has also made things simpler for businesses. An economic environment is made up of transactions between consumers and businesses. Similarly, a business organization is nothing without its human resource. Interaction between these resources is streamlined and made simpler with the help of data analysis. As rightly said by Atul Butte in Stanford, “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.” If these sentences do not make any sense to you, let us tell you how data can help businesses – With the help of collected data of consumers, your business can redesign its marketing strategies to achieve better results. Through data, your business can hire better resources with the help of online tools. Data can help your business predict future trends and modify your plans as per them. Data can also help in personalizing the experience of your consumers, thereby, increasing their satisfaction. However, the question which arises now is that if there’s a sea of data being generated every minute, how does a business reach a decision with its help? This is where Big Data comes in. WHAT IS BIG DATA? In simple words, big data is data generated from various sources and through different formats. Thanks to the amount of information available on internet, the accumulated data is growing exponentially every day.  Big data is primarily defined through Vs. Let us tell you what they are – VELOCITY: Data flows in higher velocity and speed than in earlier times.  The number of internet users have helped in accelerating the pace. VARIETY: From pictures, videos to information and numbers, the type of data pouring in through various channels is varied as it can get VOLUME: The amount of data generated every minute is growing in leaps and bound. As per statistics, the average amount of data generated every minute is US alone is 2,657,700 gigabytes. As data has been multiplied hundreds of times, its analysis has become even more difficult. Data comes in all kinds of forms – structured and unstructured.  To make this data useful by drawing information and insights, we need systems which are much more advanced than traditional databases. This is where Big Data tools and analytics come in. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – (Geoffrey Moore, author and consultant) In this article, we will walk you through some of the best big data tools to look out for in 2019 TOP BIG DATA TOOL YOU MUST KNOW HADOOP: If there’s a discussion about big data, it is incomplete without a mention of Hadoop. Literally, Hadoop and big data are inseparable. Even in 2019, Hadoop remains significant and relevant in the world of Big Data analytics. SO, WHAT IS HADOOP? Hadoop is an open-source framework that helps in storing data and tackling big data in an efficient way. It first came into existence in early 2000s; around the same time as Google.  It began as a search engine indexing tool and grew more technical with features like storing and processing data. Over time, hadoop has become synonymous with big data analytics and still remains important. It is low cost and has easy accessibility. It has four components – Hadoop Common:  basic utilities for every kind of case. Hadoop Distributed File System (HDFS): a database for storing data in a simple manner Hadoop MapReduce:  Helps in processing and simplifying a large set of data by filtering and analysis. Hadoop YARN: helps in resource management and scheduling. Even though, it is as old as the term “Big Data”, it still remains the backbone of it. Thanks to its affordability, open libraries and scalability, Hadoop still has a growing scope in 2019. Learning Hadoop will give you a solid base and understanding of Big Data. It will also help you in learning other technologies like Apache Spark etc. APACHE SPARK: Developed in 2009 at UC Berkeley, Apache Spark is one of the most popular open-source data processing engines with APIs in varied forms like Java, Python, SQL and R. Apache Spark was developed to provide speed, ease of use and sophisticated analysis. In an article in Forbes, Apache Spark was called Taylor Swift of Big data as it has been around for a while but grabbed eyeballs only around 2015. In the past few years, Apache Spark has gained a lot of admirers mainly because of the following reasons – It uses electronic memory rather than completely relying on hard disk which makes it 10 times faster. Even though it doesn’t have its own database, it can easily be integrated with any system like HDFS, MongoDB and Amazon’s S3 system. It is the most preferred framework for Machine Learning. Machine Learning is the present and the future of technology. With the help of Spark Streaming, data can be analyzed in real-time. Start Learning Apache Spark Apache Spark was developed as an improvisation of Hadoop and is still flourishing. It is now considered as one of the key and mature tools for Big Data Analytics. Even with introductions of new technology, Apache Spark continuous to rule the Big Data ecosystem. APACHE CASSANDRA: The software world runs on scalability. And, Apache Cassandra is a highly scalable, no SQL, open-source framework. Open-sourced in 2008 by Facebook, Apache Cassandra provides certain advantages that no other relational database or SQL can give.  Some of these advantages are – – DECENTRALIZATION: Apache Cassandra doesn’t have a master-slave architecture. Every cluster is identical eliminating a single point of failure or network bottlenecks. – HIGHLY ELASTIC: Its unique decentralized architecture makes adding new nodes easy, thus, enables it to handle a large amount of data across channels. – LINEAR SCALABILITY: Scaling is easier and simpler as no single node is interdependent on each other, and adding a new node can help you scale as much as you would like. Apache Cassandra has been helping big shots like Apple, Spotify, Instagram, ebay etc. Its ability to handle multiple concurrent users without affecting the performance makes it the first choice of many organizations. In 2019, Apache Cassandra will only continue to grow as more and more people realize its benefits. MongoDB: Just like Apache Cassandra, Mongo DB is another NoSQL database. With its high flexibility, cost-effectiveness and open-source libraries, Mongo DB is the fastest growing technology. It is simple, dynamic and object-oriented. What makes it different from the traditional databases is its document store model in which data is stored as a document rather than in columns of a traditional database. Some of the advantages it offers are – Because of its rich document-based data system in the form of BSON etc, a large variety of data like integer, string, array can easily be stored. – Its infrastructure is cloud-based making it highly flexible. – It uses dynamic schemas which allows data to be set up quickly. This helps in saving cost and time – It helps in the real-time analysis of data. Mongo DB is highly preferred for e-commerce websites, social networking sites and content management.  All of these are the need of the hour in 2019. As the most important part of MEAN Stack, it is the most preferred framework by startups. Moreover, bigger companies too are adopting it quickly. APACHE SAMOA : Coming back to the Apache family, Apache SAMOA is one of the most popular big data tools especially for the graphical databases.   SAMOA stands for Scalable Advanced Massive Online Analysis and really, the abbreviation explains it all. Apache SAMOA is an open-source platform which has a collection of the distributed streaming algorithm for data mining and machine learning tasks such as – -Regression, -Clustering -Classification -Program Abstraction It has a pluggable architecture which allows it to run on many distributed stream processing engines like – – Apache Storm – Apache S4 – Apache Flink Apache Samoa is the most preferred framework for Machine learning as it facilitates the development of new machine learning algorithms without interfering with the complexity of underlying distributed stream processing. Some major reasons why it is preferred are – With its pluggable and reusable architecture, deployment becomes easy. – There’s no downtime. – Once a program is completed, it can be run everywhere. – Simple process. CONCLUSION: Big Data is the biggest trend of 2019 and of the coming future. An understanding and proficiency in big data help in carving a bankable career path for yourself. Our suggestion would be to begin your journey in Big Data with Hadoop and Spark. These two technologies are a pioneer in the world of Big Data and will always remain the basis of analytics. For more information, feel free to contact us and check our training program.

Best 10 uses of MS Excel in Daily life

In the quick & progressing lifestyle of people, there is utmost demand for shortcuts & advanced methods to understand and resolve the daily problems. Moreover, there are multiple tasks use of Excel that we do in our daily lives such as calculating monthly expenses, budgeting & goal setting, students tacking their syllabus and various other things that most people do in casual ways rather than following simple tools that provide concrete shapes. Here are the Best 10 uses of MS Excel in our daily lives Use of Excel for Students & Teachers:    Teachers can make the best use of table styles, charts, shapes, data tools, and various formulas to educate students in the classrooms. Whereas students can enhance their learning skills to solve basic and logical statistical & mathematical problems in excel. Use of Excel for Goal Setting & Planning: Goal setting & planning are the kinds of repeated tasks carried on each day. From business owners to students to housekeepers each individual is involved with the process of goal setting and planning. The goal-setting and planning process involves white paper, time, and the immense pressure of calculations, but with the use of MS Excel, this process has become efficient, quick, easy, and environmentally friendly. Must Read:- 5 REASONS WHY MICROSOFT EXCEL IS IMPORTANT FOR ANALYTICAL CAREER Use of excel for Entrepreneurs & Business Owners:   A large chunk of the millennial population across the world is aspiring to become an entrepreneur which requires not only efficient planning but also analysis of team performance, work progress, business progress & payout detail. Whether a new business or an established business, each can derive benefits from using excel. The data can be stored, analyzed and sophisticated presented on excel sheets using multiple tables, pivot tables, data highlighters, sorters, sheet and cells organizers among others. Use of Excel for Housewives: Housewives are known to be the best keepers of monthly expenses & saving money. And thus excel can assist housewives to manage their daily house expenses which can track down the spending habits of each member of the house. Furthermore, housewives using Excel can be beneficial for them to make their kids learn basic excel skills. Must Read:- TOP 5 MOST IN DEMAND IT JOB SKILLS YOU NEED FOR THE FUTURE  Use of Excel for Career Development: Career Development revolves around career management. The tasks such as learning management, time management, work, and life management and goal-focused habits are important which can be effectively practiced on MS excel. Use of excel for Monthly Expenses Report: Based on monthly expense data entered in the excel sheet, the user can create a comprehensive monthly expenses report which can highlight the top expenditure segments and study the pattern of expenditure & required savings pattern to be followed to reach the desired goal. Start Learning Advanced Excel VBA  Use of excel for Online Access: Another important use of MS Excel is that the files can be accessed online from any part of the world anytime and anywhere. It provides the convenience of accessing the excel files over mobile phones even if there is the unavailability of laptops & lets you exercise the work easily without any problem. Use of excel for Developing Future Strategies:  The data relating to future strategies such as investments and major expenses anticipated at future date can be entered in the form of charts and graphs so it can lead to identify the trends and compare each possibility. Using MS Excel, the trend lines can be expressed through graphs and charts to forecast the future value of money invested. Use of Excel for Create a Calendar or Schedule:  Whether it is family-based planning for a weekly, monthly or yearly calendar or a personal appointment daily planner or a schedule for managing bill payments, homework, favorite sports team’s games, and many more, excel can make it easy to compile, filter, search, organize and simplify large amounts of data. Use of Excel for Event & Project Planning:  MS Excel is being widely used in the planning of a large work project or holiday or wedding party, where it can keep track of different tasks, efforts, and deadlines, and to analyze the schedules of collaborators in the planning of the event with other participants and make use of excel sheet as a central database of all relevant information required by all members to execute the project or event. The more a person gets deeper in learning the wide benefits of excel, the more they tend to develop smarter ways to apply MS excel in daily life. It is better to provide some time on learning & getting hands-on excel rather than doing things in unnecessary and less productive ways. Other than the above-mentioned benefits, various other uses of excel can be developed as per the individual requirements.   

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