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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache Ambaripache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache StormProcessing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
Apache Spark Vs Apache Storm - Head To Head Comparison
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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache Ambaripache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache StormProcessing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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Apache Spark Vs Apache Storm - Head To Head Compar...

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Agile Coach (ICP ACC): All You Need To Know

Want to shape your career as an Agile coach? ICP-ACC certification enables you to acquire the required skills to serve an Agile team in a better way. But how do these skills help you? These skills enable you to increase your focus on the objectives and business goals of an organisation as well as create a healthy environment for collaboration and problem resolution. In this article, we will see how to be an Agile Coach, roles and responsibilities, Agile Coach skills, and how the role is valuable for the organizations. But, firstly let us take a look at the definition of an Agile Coach.What is an Agile Coach?An Agile Coach is an individual who exhibits his/her years of experience in implementing Agile methodology in the projects and sharing that experience with a project team. Agile Coach trains project teams on Agile software development process and guide teams throughout the implementation process. The ultimate aim behind the Agile Coach role is to equip Agile teams with the right knowledge, tools, principles, and practices so that the teams can follow Agile in a full manner. As an Agile coach, you serve as a mentor to the Agile team while facilitating agile practices and thinking to empower the teams to reach their goals through improved team practices. Further, being an Agile coach you’ll be able to encourage a new culture of agile-lean thinking while bringing in a positive change to reflect an agile attitude by introducing some change in the organisational culture as well as redefining the work paradigm.Roles and Responsibilities of an Agile CoachWhen an organization wants to develop something new on the project, an Agile Coach is the best person to guide and to come up with some bright ideas that will enhance product development in Agile. The roles and responsibilities of an Agile coach are:Being a non-Scrum team member, Agile Coach mentors and coaches the teams on Agile methodologyAgile Coach is partly trainer and partly consultant, more specifically he/she is a Guide to the team membersBeing an individual outside the organization, Agile Coach helps the team members to implement their training knowledge into reality. Agile Coach is an Agile expert who provides objective guidance on the projectAn Agile Coach runs any kind of Agile project (of changing size and complexities) successfully    The Agile Coach is responsible for implementing the Agile techniques in varied cultures and environmentsWorking as a guide, the Agile Coach helps the team in Agile adoption and challenge the present environment How do you become an Agile Coach?To become an Agile Coach, an individual needs an expert-level of understanding of Lean-Agile practices, strong skills of team facilitation, and professional coaching and mentoring skills. The steps to becoming an Agile Coach are as follows:Get CSM® certification from any registered education provider (REP) with Scrum AllianceAfter achieving CSM®  certification, you get at least 2-3 years of working experience as a Scrum Master, which will make eligible (in terms of knowledge) to become an Agile Coach You need to get Agile Coach training- ICP-ACC certification training to become an Agile Coach What is ICP-ACC (ICAgile Certified Professional in Agile Coaching) certification?ICP-ACC certification is a knowledge-based Agile coaching certification. The main focus of this certification is to set the Agile mindset, roles, and responsibilities of an Agile coach. After achieving this certification, a candidate can easily differentiate among facilitating, mentoring, professional coaching and teaching. Also, the candidates will learn all the skills like team collaboration and conflict resolution that will lead to form a safe environment in an organization. The key focus of this certification is to develop an understanding of the professional coaching skill set as well as the value of honing these skills in order to serve individuals in an agile team.At the end of the ICP-ACC certification, you’ll be able to differentiate between and among:FacilitatingMentoringTeachingProfessional coachingWhat is ICAgile?ICAgile, established in 2010, is a globally renowned certification and accreditation body that enables organizations to configure learning experiences that develop an Agile mindset and empower to achieve sustainable agility in the organizations. It is not a training provider. The approach of ICAgile is not just limited to Agile methodology, it lets people add more flavors to make real sense out of it. ICAgile is a platform where Agile thought leaders around the world collaborate and develop learning programs that take people to achieve mastery in Agile. This accreditation body work with the course providers to certify new or existing courses against the comprehensive and demonstrated Learning Outcomes given at ICAgile. The courses provided at ICAgile meet the most elevated amount of guidelines created by overall Agile thought leaders and the learning roadmap gives a clear way to the professionals and organizations who want to begin or continue their Agile journey.    Why should an individual take up an ICP-ACC certification?Getting an ICP-ACC certification enables you to become an Agile Coach and with the rising demand for implementation of Agile methodology across various organisations, you end up having a promising career in the field. The following reasons trigger the organisations to hunt for Agile coaches to train their Agile teams:When it comes to an organisation looking to implement agile methods in their existing workflow, a high-quality Agile training program can offer a fantastic jump start to it.An Agile Coach encourages teams to adopt, scale-up, and thrive on Agile methods by communicating a vision on the need for Agile methodology. For a company transitioning to Agile, an Agile Coach plays the role of a trainer by offering rigorous training and hand-holding for the teams.Also, an Agile Coach plays the role of a mentor to speed up with Agile to accelerate adoption, identify, and plug knowledge gaps by deploying the right set of activities.The role of an Agile Coach further includes the responsibility of ensuring continual improvement through regular monitoring to gauge the organisational progress brought by Agile adoption.Who needs an Agile Coach?Agile teams need an environment to grow. Such an environment should comprise of respect, trust, and mutual interest. This brings in the requirement of an Agile Coach who can coach and mentor various Agile roles or a Scrum Master who has acquired the required coaching skills.Apart from the technical and business expertise, the Agile Coaches are expected to be equipped with certain unique behavioral skills like self-awareness, self-management, active listening, powerful questioning, etc. This has lead to a high demand for Agile Coaches in all the organisations across every industry around the globe. The learning objectives of ICP-ACC certificationThe major focus of the Learning Objectives (LOs) of Agile Coaching Track is on the competencies required for self, individuals, team, and program level impact. On taking up an ICP ACC certification training, you’ll learn the following:Learn to develop the Agile mindsetLearn about the roles and responsibilities of an Agile CoachLearn to mentor Agile roles and transitionsUnderstand the team dynamics and coach the team to get motivated and become self-awareLearn to handle the conflict and dysfunctions within the teamLearn team building and collaborationLearn to define the coaching contract and maintain neutralityLearn to identify and address issuesLearn to break down impediments for team successUnderstand the pillars of agility of an organisation while learning to identify systematic challengesLearn to create your personal coaching improvement backlog.Now coming to the prerequisites, you don’t need to meet any specific prerequisites to take up ICP-ACC training. But it is an added advantage for you to have a basic understanding of Agile and some work experience in an Agile team.Who can take up the ICP-ACC training?ICAgile is the certifying body for ICP-ACC certification and according to it, this certification is best pursued after Agile Team Facilitation (ICP-ATF). However, this certification can be taken up by:Agile coachesAspiring coaches with a passion for servant leadership and a desire to learn and practice facilitation, professional coaching, teaching, and mentoring in service of Agile teams.ScrumMastersIteration ManagersAgile Project ManagersWhat lies beyond ICP-ACC training?Equipping yourself with ICP-ACC training can help you to land upon a high salary job. Yes, you’ve heard it right! An Agile coach earns an average annual salary of $1448,698 in the US. Moreover, you get an opportunity to work with the top organisations around the globe, like, PepsiCo, Nissan Motors, Accenture, Wipro, and the list goes on. The following chart will give you a better understanding of the salary of Agile coaches across the globe: CountrySalary of Agile Coach (per annum)IndiaINR 21,70,944US$148,698CanadaC$101,142AustraliaAU$152,353On a concluding noteWith the frequently evolving industry trends, the demand for seasoned Agile Coach is on the rise. They are really looked up to as a change-maker who can really make a difference in an organisation’s Agile journey. This article comprises of all the information that you need to get ICP-ACC certified to embark on your journey to becoming an Agile Coach. All the best!
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Agile Coach (ICP ACC): All You Need To Know

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What is Power BI Used For - Practical Applications Of Power BI

Organizations deal with lots of data regularly. But in case you are not able to access or connect with that important data, you are not yielding anything. You are keeping your organizations away from getting the value. Microsoft Power BI will help you solve this problem with the help of a powerful business intelligence tool that mainly stresses on Visualization. Microsoft Power BI is a fundamental programming framework for organizations with huge amounts of disparate data developed during normal business operations. Power BI has numerous uses in building a software system. Let’s see the practical uses of Power BI below:Visualization = Inbuilt featureServer-Level Data ManagementAnalytics With Internal Software SystemsProvide complex data within software and appsStreamline Organizational ProcessesVisualize Details EasilyEnhance the marketingReal-Time look at the company’s financial performanceCreate Consistent Reporting Standards 1. Visualization = Inbuilt featureData Visualization is very crucial for human management in business systems. The computer programs are written in coding language whereas business people need to be able to visualize and understand the business data. Microsoft Power BI provides such tools that will let you visualize key data points accurately from various sources in a single dashboard. Power BI integration with Cortana can visualize data, interact with it in your OS, and search your data with Cortana’s powerful AI systems.2. Server-Level Data ManagementIn businesses, data comes from various sources. Most of those sources reach the server at some point. Power BI tools let you manage all the business-related data at the server level with the goal that you have more extensive and complete information systems than the data you collected from a program working on a few PCs. 3. Analytics With Internal Software SystemsYour business needs to access data from the complete software system to manage it. Power BI merges with any software platform used to manage the businesses. This consists of mail management,  social media platforms, accounting software, CRMs, and traditional data platforms like Azure and MySQL. Multiple dashboards for data management are integrated to form Integrated data management. If the businesses use integrated data management then the businesses need not use multiple dashboards for managing the data. 4. Provide complex data within software and appsPower BI takes input from various sources and provides that data in various contexts, including embedded within your own apps through API by Microsoft. Moreover, Power BI offers tools to manage businesses allowing app customization. It also provides a product value-add feature that is used to track and identify the key data sets regarding the product or service.        5. Streamline Organizational Processes The departments such as Sales, marketing, operations, HR, and so on in the organizations need their own data sets, KPIs, and their own data management systems. Power BI offers specific functionality to the businesses in the form of a data management system/app to streamline the processes from different departments. It let the businesses use the templates for intuitive dashboards and reporting systems instead of creating a system part-wise for your business. It gives your teams the capability to manage quality and efficiency without software customization. 6. Visualize Details EasilyPower BI helps businesses to check any kind of details to ensure that the business is running smoothly. While some enterprises track a small amount of inventory, some businesses track sales calls (made by the sales team members). These data sets are designed with Power BI algorithm and delivered in a readable and easily understandable format.7. Enhance the marketingBusinesses spend intensely on online marketing to attract customers. But the huge mass is failing to convert while researching the solutions to their uniques problems online. In this case, Power BI helps you create a chart to track the user behavior all through their online visit. 8. Real-Time look at the company’s financial performanceFinancial issues have an enduring effect on your organization, particularly if it's totally all of a sudden. Microsoft Power BI gives you understanding into the performance of the organizations at numerous levels. With Power BI, you can gaze into teams productivity, top-selling products, the income created by a specific division and numerous different perspectives. With Power BI, you will be able to get immediate attention on sudden financial drops and will be able to fix the issues before it turns into a huge concern.   9. Create Consistent Reporting StandardsEvery organization relies on applications from accounting to sales for their day-to-day job duties. You will not always get a reporting feature in these programs and if you get, the format varies from app to app. Using Power BI, you can pull the data and generate reports to provide you organizational standards. It is less hectic for the managers and takes less time to get the data if it is presented in the same format and style each time.Use-cases of Power BIThere is no doubt that Big Data has big potential today. Most of the companies using Power BI have started enjoying the real benefits such as higher efficiency, improved performance, and more efficient use of their data. Here are some of the real-world examples of Power BI that will tell you how companies are reaping commercial success with Power BI.1. MediaComMediaCom is the world's most famous media agency and the largest cable television provider was in need of the module that can easily measure and represent the health of a large number of diversified data sets.  For this problem, they found the solution i.e. ‘health check’ which is able to capture every surface of a multi-platform media in one single score, including paid media effectiveness, earned media effectiveness, the ratio of earned-to-paid media, realized customer value, and longitudinal performance. The feature ‘health-check’ is built on Power BI that provides a campaign dashboard, a collaborative site where the account team can ask their queries to the customers. With the adoption of Power BI, the company yielded increased optimization checks for campaigns from weekly to daily basis.     2. Carnegie Mellon UniversityCarnegie Mellon University (CMU), a global research university known for its world-class studies was looking for some option that can optimize the power (energy) and operational efficiency in buildings around the globe.The university installed a PI system and integrated automation system of the whole building. Also, they installed lights, ventilation, air quality, weather, and security data sources.   At that point, they included Power BI for Office 365 to give custom-reporting abilities to all the constant information created by the different frameworks, furnishing employees with maps, visualizations, and dashboards indicating data, for example, building types, geographic areas, and power consumption. This data empowers employees to identify the environment, and show them to reduce energy usage. With Power BI installed, CMU identified 30% less energy consumption.3. HeathrowHeathrow Airport, a UK based travel, and transportation industry uses Power BI to make the travel of the passengers less stressful. This airport uses Power BI to visualize the real-time passenger’s traffic at the airport and enable employees to be prepared with the traffic changing conditions. With Power BI adoption, they will be able to connect with a wide scope of data sources with fewer efforts and use this data to run Heathrow airport smoothly than ever before.
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What is Power BI Used For - Practical Applications...

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Best ways to learn Apache Spark

If you ask any industry expert what language should you learn for Big Data? You will get an obvious reply to learn Apache Spark. Apache Spark is widely considered as the future of the Big Data industry. Since Apache Spark has stepped into Big data market, it has gained a lot of recognition for itself. Today, most of the cutting-edge companies like Apple, Facebook, Netflix, and Uber, etc. have deployed Spark at massive scale. In this blog post, we will understand why one should learn Apache Spark? And several ways to learn it. Apache Spark is a powerful open-source framework for the processing of large datasets. It is the most successful projects in the Apache software foundation. Apache Spark basically designed for fast computation, also which runs faster than Hadoop. Apache Spark can collectively process huge amount of data present in clusters over multiple nodes. The main feature of Apache Spark is its in-memory cluster computing that increases the processing speed of an application.Why You Should Learn Apache SparkApache Spark has become the most popular unified analytics engine for Big Data and Machine Learning. Enterprises are widely utilizing Spark which in turn is increasing demand for Apache Spark developers. Apache Spark developers are the ones earning the highest salary. IT professionals can leverage this upcoming skill set gap by pursuing a certification in Apache Spark. A developer with expertise in Apache Spark skills can earn an average salary of $78K as per Payscale. It is the right time for you to learn Apache Spark as there is a very high demand for Spark developers chances of getting a job is high.Here are the reasons why you should learn Apache Spark today:In order to go with the growing demand for Apache SparkTo fulfill the demands for Spark developersTo get benefits of existing big data investmentsResources to learn ReactTo learn Spark, you can refer to Spark’s website. There are multiple resources you will find to learn Apache Spark, from books, blogs, online videos, courses, tutorials, etc. With these multiple resources available today, you might be in the dilemma of choosing the best resource, especially in this fast-paced and swiftly evolving industry.BooksCertificationsVideosTutorials, Blogs, and TalksHands-on Exercises 1. BooksWhen was the last time you read a book? Do you have reading habits? If not, it’s the time to read the books. Reading has a significant number of benefits. Those aren’t fans of books might miss out the importance of Apache Spark. To learn Apache Spark, you can skim through the best Apache Spark books given below.Apache Spark in 24 hours is a perfect book for beginners which comprises 592 pages covering various topics. An excellent book to learn in a very short span of time. Apart from this, there are also books which will help you master.Here is the list of top books to learn Apache Spark:Learning Spark by Matei Zaharia, Patrick Wendell, Andy Konwinski, Holden KarauAdvanced Analytics with Spark by Sandy Ryza, Uri Laserson, Sean Owen and Josh WillsMastering Apache Spark by Mike FramptonSpark: The Definitive Guide – Big Data Processing Made SimpleSpark GraphX in ActionBig Data Analytics with SparkThese are the various Apache Spark books meant for you to learn. These books include for beginners and others for the advanced level professionals.2. Apache Spark Training and CertificationsOne more way to learn Apache Spark is through taking up training. Apache Spark Training will boost your knowledge and also help you learn from experience. You will be certified once you are done with training. Getting this certification will help you stand out of the crowd. You will also gain hands-on skills and knowledge in developing Spark applications through industry-based real-time projects.3. Videos:Videos are really good resources to help you learn Apache Spark. Following are the few videos will help you understand Apache Spark.Overview of SparkIntro to Spark - Brian ClapperAdvanced Spark Analytics - Sameer FarooquiSpark Summit VideosVideos from Spark Summit 2014, San Francisco, June 30 - July 2, 2013Full agenda with links to all videos and slidesTraining videos and slidesVideos from Spark Summit 2013, San Francisco, Dec 2-3-2013Full agenda with links to all videos and slidesYouTube playist of all KeynotesYouTube playist of Track A (Spark Applications)YouTube playist of Track B (Spark Deployment, Scheduling & Perf, Related projects)YouTube playist of the Training Day (i.e. the 2nd day of the summit)You can learn more on Apache Spark YouTube Channel for videos from Spark events. 4. Tutorials, Blogs, and TalksUsing Parquet and Scrooge with Spark — Scala-friendly Parquet and Avro usage tutorial from Ooyala's Evan ChanUsing Spark with MongoDB — by Sampo Niskanen from WellmoSpark Summit 2013 — contained 30 talks about Spark use cases, available as slides and videosA Powerful Big Data Trio: Spark, Parquet and Avro — Using Parquet in Spark by Matt MassieReal-time Analytics with Cassandra, Spark, and Shark — Presentation by Evan Chan from Ooyala at 2013 Cassandra SummitRun Spark and Shark on Amazon Elastic MapReduce — Article by Amazon Elastic MapReduce team member Parviz DeyhimSpark, an alternative for fast data analytics — IBM Developer Works article by M. Tim Jones 5. Hands-on ExercisesHands-on exercises from Spark Summit 2014 - These exercises will guide you to install Spark on your laptop and learn basic concepts.Hands-on exercises from Spark Summit 2013 - These exercises will help you launch a small EC2 cluster, load a dataset, and query it with Spark, Spark Streaming, and MLlib.So these were the best resources to learn Apache Spark. Hope you found what you were looking for. Wish you a Happy Learning!
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Best ways to learn Apache Spark

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How To Run Your Python Scripts

If you are planning to enter the world of Python programming, the first and the most essential skill you should learn is knowing how to run Python scripts and code. Once you grab a seat in the show, it will be easier for you to understand whether the code will actually work or not.Python, being one of the leading programming languages, has relatively easy syntax which makes it even easier for the ones who are in their initial stage of learning the language. Also, it is the language of choice for working with large datasets and data science projects. Get certified and learn more about Python Programming and apply those skills and knowledge in the real world.What is the difference between Code, Script and Modules?In computing, the code is a language that is converted from a human language into a set of ‘words’ which the computer can understand. It is also referred to as a piece of statements together which forms a program. A simple function or a statement can also be considered a code.On the other hand, a script is a file consisting of a logical sequence of instructions or a batch processing file that is interpreted by another program instead of the computer processor.In simple terms, a script is a simple program, stored in a plain file text which contains Python code. The code can be directly executed by the user. A script is also called a top-level-program-file. A module is an object in Python with random attributes that you can bind and reference.Is Python a Programming Language or a Scripting Language?Basically, all scripting languages are considered to be programming languages. The main difference between the two is that programming languages are compiled, whereas scripting languages are interpreted. Scripting languages are slower than programming languages and usually sit behind them. Since they only run on a subset of the programming language, they have less access to a computer’s local abilities. Python can be called a scripting language as well as a programming language since it works both as a compiler and an interpreter. A standard Python can compile Python code into bytecodes and then interpret it just like Java and C.However, considering the historical relationship between the general purpose programming language and the scripting language, it will be more appropriate to say that Python is a general-purpose programming language which works nicely as a scripting language too.The Python InterpreterThe Interpreter is a layer of software that works as a bridge between the program and the system hardware to keep the code running. A Python interpreter is an application which is responsible for running Python scripts.The Python Interpreter works on the Read-Eval-Print-Loop (REPL) environment.Reads the command.Evaluates the command.Prints the result.Loops back and process gets repeated.The interpreter terminates when we use the exit() or quit() command otherwise the execution keeps on going.A Python Interpreter runs code in two ways— In the form of a script or module.In the form of a piece of code written in an interactive session.Starting the Python InterpreterThe simplest way to start the interpreter is to open the terminal and then use the interpreter from the command-line.To open the command-line interpreter:On Windows, the command-line is called the command prompt or MS-DOS console. A quicker way to access it is to go to Start menu → Run and type cmd.On GNU/Linux, the command-line can be accessed by several applications like xterm, Gnome Terminal or Konsole.On MAC OS X, the system terminal is accessed through Applications → Utilities → Terminal. Running Python Code InteractivelyRunning Python code through an interactive session is an extensively used way. An interactive session is an excellent development tool to venture with the language and allows you to test every piece of Python code on the go.To initiate a Python interactive session, type python in the command-line or terminal and hit the ENTER key from the keyboard.An example of how to do this on Windows:C:\users>python Python 3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 23:09:28) [MSC v.1916 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license()" for more information. >>>The >>> on the terminal represents the standard prompt for the interactive mode. If you do not see these characters, you need to re-install Python on your system.The statements you write when working with an interactive session are evaluated and executed immediately:print('HELLO WORLD!') HELLO WORLD! 2 + 3 5 print('Welcome to the world of PYTHON') Welcome to the world of PYTHON The only disadvantage is when you close the interactive session, the code no longer exists.Running Python Scripts by the InterpreterThe term Python Execution Model is given to the entire multi-step process to run Python scripts.At first, the statements or expressions of your script are processed in a sequential manner by the interpreter. Then the code is compiled into a form of instruction set called the bytecode.Basically, the code is converted into a low-level language known as the bytecode. It is an intermediate, machine-independent code which optimizes the process of code execution. So, the interpreter ignores the compilation step when executing the code for the next time.Finally, the interpreter transfers the code for execution.The Python Virtual Machine (PVM) is the ultimate step of the Python interpreter process. It is a part of the Python environment installed in your system. The PVM loads the bytecode in the Python runtime and reads each operation and executes them as indicated. It is the component which actually runs your scripts.Running Python Scripts using Command-LineThe most sought after way of writing a Python program is by using a plain text editor. The code written in the Python interactive session is lost once the session is closed, though it allows the user to write a lot of lines of code. On Windows, the files use the .py extension.  If you are at the beginning of working with Python, you can use editors like Sublime or Notepad++ which are easy-to-use or any other text editors.Now you need to create a test script. In order to do that, open your most suited text editor and write the following code:print('Hello World!')Then save the file in your desktop with the name first_script.py or anything you like. Remember you need to give the .py extension only.Using python commandThe most basic and the easy way to run Python scripts is by using the python command. You need to open a command-line and type the word python followed by the path to your script file, like this:python first_script.py Hello World!Then you hit the ENTER button from the keyboard and that's it. You can see the phrase Hello World! on the screen. Congrats! You just ran your first Python script. However, if you do not get the output, you might want to check your system PATH and the place where you saved your file. If it still doesn’t work, re-install Python in your system and try again.Redirecting outputWindows and Unix-like systems have a process called stream redirection. You can redirect the output of your stream to some other file format instead of the standard system output. It is useful to save the output in a different file for later analysis.An example of how you can do this:python first_script.py > output.txtWhat happens is your Python script is redirected to the output.txt file. If the file doesn’t exist, it is systematically created. However, if it already exists, the contents are replaced.Running modules with the -m optionA module is a file which contains the Python code. It allows you to arrange your Python code in a logical manner. It defines functions, classes, and variables and can also include runnable code.If you want to run a Python module, there are a lot of command-line options which Python offers according to the needs of the user. One of which is the command  python -m . It searches the module name in the sys.path and runs the content as __main__:python -m first_script Hello World!Note that the module-name is a module object and not any string.Using Script FilenameWindows makes use of the system registers and file association to run Python scripts. It determines the program needed to run that particular file. You need to simply enter the file-name containing the code.An example on how to do this using command prompt:C:\Users\Local\Python\Python37> first_script.py Hello World!On GNU/Linux systems, you need to add a line before the text— #!/usr/bin/env python. Python considers this line nothing but the operating system considers it everything. It helps the system to decide what program should it use to run the file.The character combination #! known as hashbang or shebang is what the line starts with, which is then followed by the interpreter path.Finally, to run scripts, assign execution permissions and configure the hashbang line and then simply type the filename in the command line:#Assign the execution permissions chmod +x first_script.py #Run script using its filename ./first_script.py Hello World!However, if it doesn’t work, you might want to check if the script is located in your currentworking directory or not. Otherwise, you can use the path of the file for this method. Running Python Scripts InteractivelyAs we have discussed earlier, running Python scripts in an interactive session is the most common way of writing scripts and also offers a wide range of possibilities.Using importImporting a module means loading its contents so that it can be later accessed and used. It is the most usual way of invoking the import machinery. It is analogous to #include in C or C++. Using import, the Python code in one module gets access to the code in another module. An implementation of the import:import first_script Hello World!You can see its execution only when the module contains calls to functions, methods or other statements which generate visible output.One important thing to note is that the import option works only once per session. This is because these operations are expensive.For this method to work efficiently, you should keep the file containing the Python code in your current working directory and also the file should be in the Python Module Search Path (PMSP). The PMSP is the place where the modules and packages are imported.You can run the code below to know what’s in your current PSMP:import sys for path in sys.path: print(path)\Users\Local\Python37\Lib\idlelib \Users\Local\Python37\python37.zip \Users\Local\Python37\DLLs \Users\Local\Python37\lib \Users\Local\Python37 \Users\Local\Python37\lib\site-packagesYou’ll get the list of directories and .zip files where your modules and packages are imported.Using importlibimportlib is a module which is an implementation of the import statement in the Python code. It contains the import_module whose work is to execute any module or script by imitating the import operation.An example to perform this:import importlib importlib.import_module('first_script') Hello World! importlib.reload() is used to re-import the module since you cannot use import to run it for the second time. Even if you use import after the first time, it will do nothing. importlib.reload() is useful when you want to modify and test your changes without exiting the current session.The following code shows that:import first_script #First import Hello World! import first_script import importlib #Second import does nothing importlib.reload(first_script) Hello World! However, you can only use a module object and not any string as the argument of reload(). If you use a string as an argument, it will show a TypeError as follows:importlib.reload(first_script)Traceback (most recent call last): ... ...   raise TypeError("reload() argument must be a module") TypeError: reload() argument must be a moduleUsing runpy.run_module() and runpy.run_path()The Python Standard Library has a module named runpy. run_module() is a function in runpy whose work is to execute modules without importing them in the first place. The module is located using import and then executed. The first argument of the run_module() must contain a string:import runpy runpy.run_module(mod_name='first_script') Hello World! {'__name__': 'first_script',     ... '_': None}}Similarly, runpy contains another function run_path() which allows you to run a module by providing a location.An example of such is as follows:import runpy runpy.run_path(file_path='first_script.py') Hello World! {'__name__': '',     ... '_': None}}Both the functions return the globals dictionary of the executed module.Using exec()Other than the most commonly used ways to run Python scripts, there are other alternative ways. One such way is by using the built-in function exec(). It is used for the dynamic execution of Python code, be it a string or an object code.An example of exec() is:exec(open('first_script.py').read()) Hello World!Using py_compilepy_compile is a module which behaves like the import statement. It generates two functions— one to generate the bytecode from the source file and another when the source file is invoked as a script.You can compile your Python script using this module:import py_compile py_compile.compile('first_script.py'  '__pycache__\\first_script.cpython-37.pyc' The py_compile generates a new subdirectory named "__pycache__" if it doesn’t already exist. Inside the subdirectory, a Compiled Python File (.pyc) version of the script file is created. When you open the .pyc file, you can see the output of your Python script.Running Python Scripts using an IDE or a Text EditorAn Integrated Development Environment (IDE) is an application that allows a developer to build software within an integrated environment in addition to the required tools.You can use the Python IDLE, a default IDE of the standard Python Distribution to write, debug, modify, and run your modules and scripts. You can use other IDEs like Spyder, PyCharm, Eclipse, and Jupyter Notebook which also allow you to run your scripts inside its environment.You can also use popular text editors like Sublime and Atom to run Python scripts.If you want to run a Python script from your IDE or text editor, you need to create a project first. Once it is created, add your .py file to it or you can just simply create one using the IDE. Finally, run it and you can see the output in your screen.Running Python Scripts from a File ManagerIf you want to run your Python script in a file manager, all you need to do is just double-click on the file icon. This option is mainly used in the production stage after you have released the source code.However, to achieve this, some conditions must be met:On Windows, to run your script by double-clicking on them, you need to save your script file with extension .py for python.exe and .pyw for pythonw.exe.If you are using the command-line for running your script, you might likely come  through a situation where you’ll see a flash of a black window on the screen. To avert this, include a statement at the tail of the script — input(‘Enter’). This will exit the program only when you hit the ENTER key. Note that the input() function will work only if your code is free of errors.On GNU/Linux and other Unix-like systems, your Python script must contain the hashbang line and execution permissions. Otherwise, the double-click trick won’t work in a file manager.Though it is easy to execute a script by just double-clicking on the file, it isn’t considered a feasible option because of the limitations and dependency factors it comes with, like the operating system, the file manager, execution permissions, and also the file associations.So it is suggested to use this option only after the code is debugged and ready to be in the production market.ConclusionWorking with scripts has its own advantages like they are easy to learn and use, faster edit and run, interactivity, functionality and so on. They are also used to automate complex tasks in a simplified manner.In this article, you have learned to run your Python scripts using:The terminal or the command-line of the operating system.The Python Interactive session.Your favorite IDE or text editor.The system file manager.Here, you have gathered the knowledge and skills of how to run your scripts using various techniques.You will feel more comfortable working with larger and more complex Python environments which in turn will enhance the development process and increase efficiency. You can learn more about such techniques as KnowledgeHut offers Python Certification Course.
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How To Run Your Python Scripts

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Choosing the Right Visualization Type for a Project Report

Data visualization is a way of creating a picture with your data rather than leaving it on a spreadsheet. There are a lot of different types of data visualization, and selecting the Right Data Visualizations for a project depends on what you’re trying to show. Visualization can be used to compare values, show the composition of something, and help you to analyze trends in the data.Are you comparing values?If you want to compare values, there are different types of charts to represent data that can be used. These are as follows :Column charts can show comparisons of different items or a comparison of items over time.A mekko chart not only compares values but also measures their composition and demonstrates how data is distributed across each value.Bar graphs are useful because they prevent clutter when you have a very long data label or a lot of values to compare. A pie chart shows how much certain categories make up the total percentage. Line graphs can show how values progress and change over a period of time, and can show many categories at once.Scatter plot charts, show the relationship between two different variables and are useful for determining your data’s distribution.Bullet graphs show progress towards a goal, compared to another value, and given a rating.Let’s take a look at the different types of charts and their uses-1. Are you showing the composition of something?If you want to visually represent how much certain parts contribute to the whole, you can use pie charts, stacked bar graphs, mekko charts, stacked columns, area charts, and waterfall charts. These kinds of visualizations and graphs for your project are useful for displaying data such as your total sales, broken down by representative, or by-product.2. Are you trying to better understand the distribution of your data?If you’re looking closely at your distribution, it probably means you’re searching for outliers and trying to see what the normal tendency actually is. Mekko charts, line charts, columns, bar graphs, and scatter plots can be used. Scatter plot charts and show the relationship between two sets of variables.3. Are you analyzing trends in your data?Sometimes you’ll want to know how your variables did during a certain time period. It is good to display this kind of information using line charts, dual-axis charts, and columns. A dual-axis chart is unique. It allows you to plot two y-axes that share an x-axis. Dual-axis is used to see if there is a correlation between the three data sets.Proper use of number charts“Number charts are used for showing an overview of key performance indicators (KPI), the only decision to be made with number charts is the time period you would like to show. They can show the latest quarter or a company’s entire history,” advises Paul Drolet, data analyst at Writemyx. Avoid using too many number charts, or your point will become diluted.Proper use of line chartsLine charts are mostly used to show trends because they visualize a continuous string of data over time. Adding in goal lines can show how close actual performance came to reaching, or exceeding, benchmarks that were set. Very useful because they can be effectively combined with other types of visualization, such as bar graphs. Try to avoid creating line charts with a lot of variables, because they will become very hard to read.Proper use of horizontal bar graphsIf you’re wanting to visualize some comparative rankings, then horizontal bar graphs are your go-to. They can also be used to represent values with very long names, just be sure they are placed in a logical order in that case, such as alphabetical. Avoid using horizontal bar graphs if you have a lot of values to represent because it looks overcrowded.Proper use of stacked chartsYou can use stacked charts when you are comparing data to itself, basically comparing percentages of a whole. “Pie charts and stacked bar graphs are two examples of stacked charts, but best used for different purposes. Pie charts should be used to represent single part-to-whole relationships, while stacked bar charts can be used to visualize multiple part-to-whole relationships,” Jacob Harris, Data Scientist at AcademicBrits and 1Day2Write.Proper use of pie chartsPie charts are useful for showing how much percentage a variable can make out of a static value, at any given time, but not overtime. As mentioned, they’re useful for showing single part-to-whole relationships. Overall pie charts are very limited and best used for showing approximations, and for the right audience, i.e. an audience not made up of data scientists. The pie-charts are good for showing data in a simple way.Usage of gauge chartsGauge charts use needles and colors to show data as if it were being measured on a speedometer. They are easy to understand and very good for showing one value at a time, usually side by side with another for a comparison of two variables. Their weakness is that you cannot use them to show trends and they take up a lot of space.Proper use of spider chartsUse a spider chart, when you need to compare data with three or more variables. This means comparing items with several different aspects at once. They are great for rankings, appraisals, and reviews. Don’t use a spider chart if you’re comparing more than five things otherwise they become very hard to read.ConclusionThere are many different ways of showing data visually. The types of data visualization that you use depends on what you are trying to show with your data. Knowing what type of chart to use to compare data make your report much more effective and informative for the reader’s perspective.
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Choosing the Right Visualization Type for a Projec...

Data visualization is a way of creating a picture ... Read More