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Why a Career in Big Data Is the Right Choice for You?

Are you in that job market where the Big Data skills are more appreciated? Confused about whether to make a career shift in Big Data or not? What will be the next career options available for me after Big Data? Just spend some time reading this blog and know the answers to all these questions and the reasons for making Big Data as a career choice.  “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris LynchReasons to Must-Have Big Data in your career1.Increased Job Opportunities for Big Data professionalsWith the technology reaching greater heights, undoubtedly Big Data is becoming a buzz word and a growing need for the organizations in the upcoming years. But, as Jeanne Harris, a senior executive at Accenture Institute said- “Data is useless without the skill to analyze it.”Today, Big Data professionals have a soaring demand across organizations worldwide. Organizations are making huge use of Big Data to stay ahead of the competitive market. The candidates with Big Data skills and expertise are in high demand. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.2. Salary GrowthThe strong demand for Big Data professionals is affecting the wages for qualified professionals. According to Glassdoor, the salary provided by various organizations based on the employees working in these organizations in the US region are as follows:CompanySalaryJ.P. Morgan$93K – $100KCognizant Technology Solutions$92K – $98KCSAA Insurance Group$133K – $144KZipRecruiter$81K – $89KThe salary of Big Data professionals is directly proportional to the factors like the skills earned, education, experience in the domain, knowledge of technology, etc. Also, one needs to understand and solve the real-world Big Data problems and a good grasp of tools and technologies.   3. Massive Big Data adoptionForbes stated that- Big data adoption in enterprises is increased from 17% in 2015 to 59% in 2018, reaching a Compound Annual Growth Rate (CAGR) of 36%. Big Data is steadily spreading its wings across numerous sectors including sales, marketing, research and development, logistics,  strategic management, etc.According to the 'Peer Research – Big Data Analytics' survey by Intel, the decision has incurred that- Big Data is one of the top priorities of the enterprises taking part in the survey as they believe that it improves the performance of their organizations. From the survey, it is found that 45% of the respondents trust that Big Data will offer more business benefits to rank on the top of the Big data market.    “Bigiota Insight out forecasted that the Big Data market is expected to grow to $80 billion from current $40 billion making a revenue of $187 billion.”4. Various options in job titles and responsibilitiesBig Data professionals have an array of job titles open depending on the skills they have achieved so far. The options for the Big Data job aspirants are many where they are free to align their career paths based on their career interests. Some of the job roles Big Data professionals can play are as follows:Data EngineerBusiness Analyst,Visualization SpecialistMachine Learning ExpertAnalytics ConsultantSolution ArchitectBig Data Solution ArchitectBig Data Analyst5. Usage Across numerous firms/industriesToday, Big Data is used almost in every firm. The top 5 industries recruiting Big Data professionals widely are Professional, Scientific and Technical Services (27%), Information Technology (19%), Manufacturing (15%), Finance and Insurance (9%), Retail Trade (9%) and Others 21%.The career path of a Big Data professionalAlthough the term Big Data is used commonly nowadays, there are many career paths available for the Big Data professionals to stand out in the industries that can be explored as per one’s potentiality and interest. The career paths that Big Data professionals can play are:Data ScientistBig Data EngineerBig Data AnalystData Visualization DeveloperMachine Learning EngineerBusiness Intelligence EngineerBusiness Analytics SpecialistMachine Learning ScientistLet us see them in details:Data Scientist:This is the most sought-after career path in Big Data careers. The Data Scientists are the individuals who use their technical and analytical skills to extract meaning from data. They are responsible for collecting, cleaning, and manipulating data.Big Data Engineer:Big Data Engineer is a well-known and more demanding career option. Data Engineers are the professionals responsible for building the designs created by Solution Architects. They are responsible for developing, testing, managing, and maintaining the big data solutions in the enterprises.Big Data Analyst:Being a command on the big data technologies like Hadoop, Hive, Pig, etc. and analytics skills, Data Analyst finds out relevant information from the datasets. This is also most demanding in Big Data career.Data Visualization Developer:The data visualization developers have the responsibilities of designing, conceptualizing, developing the graphics or data visualization, and supporting the data visualization activities. They should have strong technical skills for implementing visualization using tools.Machine Learning Engineer:Today, Machine Learning has become a crucial part of Big Data. Being an expert in machine learning (Machine Learning Engineer) responsible for building the data analysis software to run the product code without human intervention.Business Intelligence Engineer:Business Intelligence Engineer is in more demand today as around 90 percent of IT professionals are planning to increase spending on BI tools, as stated in the Forbes report. BI engineers are responsible for managing the big data warehouses with the help of Big Data tools and solving complex issues related to Big Data.Business Analytics Specialist:Business Analytics Specialist is an expert in Business Analytics field who aids in developing the scripts to test scripts and carrying out testing. They are also responsible for taking up business research activities to analyze the issues for developing cost-effective solutions.Machine Learning Scientist:Machine Learning Scientist work most probably in the research and development department. They are responsible for developing the algorithms to use in adaptive systems, adding product suggestions, and forecasting the demand for the same.Conclusion:As per Entrepreneur, Businesses that use Big Data saw a profit increase from 8 to10 percent and almost 10% reduction in overall cost. Another survey from Forbes states that IBM predicts demand For Data Scientists will reach 28% by the year 2020. As the data pours in, many high-rated companies like Google, Apple, NetApp, Qualcomm, Intuit, FactSet, The MITRE Corporation, Adobe, Salesforce, and so on are investing in Big Data.   According to the most recent McKinsey report, companies based in the U.S. are seeking for hiring 1.5 million Managers and Data Analysts with the strong knowledge and experience in Big Data. One can attain the most in-demand Big Data skills by taking specialized training in Big Data to go for any of the Big Data careers available in the job market.With the rising demand that industries are witnessing, it is an ideal time to add Big data skills to your curriculum vitae and offer yourself the wings to fly in the job market with the ample of Big Data jobs available today!  
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Why a Career in Big Data Is the Right Choice for You?

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Why a Career in Big Data Is the Right Choice for You?

Are you in that job market where the Big Data skills are more appreciated? Confused about whether to make a career shift in Big Data or not? What will be the next career options available for me after Big Data? Just spend some time reading this blog and know the answers to all these questions and the reasons for making Big Data as a career choice.  

“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” 

– Chris Lynch

Reasons to Must-Have Big Data in your career

1.Increased Job Opportunities for Big Data professionals

With the technology reaching greater heights, undoubtedly Big Data is becoming a buzz word and a growing need for the organizations in the upcoming years. But, as Jeanne Harris, a senior executive at Accenture Institute said- “Data is useless without the skill to analyze it.”

Today, Big Data professionals have a soaring demand across organizations worldwide. Organizations are making huge use of Big Data to stay ahead of the competitive market. The candidates with Big Data skills and expertise are in high demand. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.

2. Salary Growth

The strong demand for Big Data professionals is affecting the wages for qualified professionals. According to Glassdoor, the salary provided by various organizations based on the employees working in these organizations in the US region are as follows:

CompanySalary
J.P. Morgan$93K – $100K
Cognizant Technology Solutions$92K – $98K
CSAA Insurance Group$133K – $144K
ZipRecruiter$81K – $89K

The salary of Big Data professionals is directly proportional to the factors like the skills earned, education, experience in the domain, knowledge of technology, etc. Also, one needs to understand and solve the real-world Big Data problems and a good grasp of tools and technologies.   

3. Massive Big Data adoption

Forbes stated that- Big data adoption in enterprises is increased from 17% in 2015 to 59% in 2018, reaching a Compound Annual Growth Rate (CAGR) of 36%. Big Data is steadily spreading its wings across numerous sectors including sales, marketing, research and development, logistics,  strategic management, etc.

According to the 'Peer Research – Big Data Analytics' survey by Intel, the decision has incurred that- Big Data is one of the top priorities of the enterprises taking part in the survey as they believe that it improves the performance of their organizations. From the survey, it is found that 45% of the respondents trust that Big Data will offer more business benefits to rank on the top of the Big data market.    

Bigiota Insight out forecasted that the Big Data market is expected to grow to $80 billion from current $40 billion making a revenue of $187 billion.”

4. Various options in job titles and responsibilities

Big Data professionals have an array of job titles open depending on the skills they have achieved so far. The options for the Big Data job aspirants are many where they are free to align their career paths based on their career interests. Some of the job roles Big Data professionals can play are as follows:

  • Data Engineer
  • Business Analyst,
  • Visualization Specialist
  • Machine Learning Expert
  • Analytics Consultant
  • Solution Architect
  • Big Data Solution Architect
  • Big Data Analyst

5. Usage Across numerous firms/industries

Today, Big Data is used almost in every firm. The top 5 industries recruiting Big Data professionals widely are Professional, Scientific and Technical Services (27%), Information Technology (19%), Manufacturing (15%), Finance and Insurance (9%), Retail Trade (9%) and Others 21%.

Industries recruiting  big data professionals

The career path of a Big Data professional

Although the term Big Data is used commonly nowadays, there are many career paths available for the Big Data professionals to stand out in the industries that can be explored as per one’s potentiality and interest. The career paths that Big Data professionals can play are:

  1. Data Scientist
  2. Big Data Engineer
  3. Big Data Analyst
  4. Data Visualization Developer
  5. Machine Learning Engineer
  6. Business Intelligence Engineer
  7. Business Analytics Specialist
  8. Machine Learning Scientist

Let us see them in details:

The career path of a Big Data professional

  • Data Scientist:

This is the most sought-after career path in Big Data careers. The Data Scientists are the individuals who use their technical and analytical skills to extract meaning from data. They are responsible for collecting, cleaning, and manipulating data.

  • Big Data Engineer:

Big Data Engineer is a well-known and more demanding career option. Data Engineers are the professionals responsible for building the designs created by Solution Architects. They are responsible for developing, testing, managing, and maintaining the big data solutions in the enterprises.

  • Big Data Analyst:

Being a command on the big data technologies like Hadoop, Hive, Pig, etc. and analytics skills, Data Analyst finds out relevant information from the datasets. This is also most demanding in Big Data career.

  • Data Visualization Developer:

The data visualization developers have the responsibilities of designing, conceptualizing, developing the graphics or data visualization, and supporting the data visualization activities. They should have strong technical skills for implementing visualization using tools.

  • Machine Learning Engineer:

Today, Machine Learning has become a crucial part of Big Data. Being an expert in machine learning (Machine Learning Engineer) responsible for building the data analysis software to run the product code without human intervention.

  • Business Intelligence Engineer:

Business Intelligence Engineer is in more demand today as around 90 percent of IT professionals are planning to increase spending on BI tools, as stated in the Forbes report. BI engineers are responsible for managing the big data warehouses with the help of Big Data tools and solving complex issues related to Big Data.

  • Business Analytics Specialist:

Business Analytics Specialist is an expert in Business Analytics field who aids in developing the scripts to test scripts and carrying out testing. They are also responsible for taking up business research activities to analyze the issues for developing cost-effective solutions.

  • Machine Learning Scientist:

Machine Learning Scientist work most probably in the research and development department. They are responsible for developing the algorithms to use in adaptive systems, adding product suggestions, and forecasting the demand for the same.

Conclusion:

Companies hiring Big Data professionals

As per Entrepreneur, Businesses that use Big Data saw a profit increase from 8 to10 percent and almost 10% reduction in overall cost. Another survey from Forbes states that IBM predicts demand For Data Scientists will reach 28% by the year 2020. As the data pours in, many high-rated companies like Google, Apple, NetApp, Qualcomm, Intuit, FactSet, The MITRE Corporation, Adobe, Salesforce, and so on are investing in Big Data.   

According to the most recent McKinsey report, companies based in the U.S. are seeking for hiring 1.5 million Managers and Data Analysts with the strong knowledge and experience in Big Data. One can attain the most in-demand Big Data skills by taking specialized training in Big Data to go for any of the Big Data careers available in the job market.

With the rising demand that industries are witnessing, it is an ideal time to add Big data skills to your curriculum vitae and offer yourself the wings to fly in the job market with the ample of Big Data jobs available today!  

KnowledgeHut

KnowledgeHut

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KnowledgeHut is a fast growing Management Consulting and Training firm that is a source of Intelligent Information support for businesses and professionals across the globe.


Website : https://www.knowledgehut.com/

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Amir 21 Jun 2019

I got to know more about Career in Big Data loved it thanks for such blogs...

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Apache Spark Vs Apache Storm - Head To Head Comparison

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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. 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Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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Apache Spark Vs MapReduce

Why we need Big Data frameworksBig data is primarily defined by the volume of a data set. Big data sets are generally huge – measuring tens of terabytes – and sometimes crossing the threshold of petabytes. It is surprising to know how much data is generated every minute. As estimated 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.You can read DOMO's full report, including industry-specific breakdowns, here.To store and process even only a fraction of this amount of data, we need Big Data frameworks as the traditional Databases would not be able to store so much of data nor traditional processing systems would be able to process this data quickly. Here comes the frameworks like Apache Spark and MapReduce to our rescue and help us to get deep insights into this huge amount of structured, unstructured and semi-structured data and make more sense out of it.Market Demands for Spark and MapReduceApache Spark was originally developed in 2009 at UC Berkeley by the team who later founded Databricks. Since its launch Spark has seen rapid adoption and growth. Most of the cutting-edge technology organizations like Netflix, Apple, Facebook, Uber have massive Spark clusters for data processing and analytics. The demand for Spark is increasing at a very fast pace. According to marketanalysis.com report forecast, the global Apache Spark market will grow at a CAGR of 67% between 2019 and 2022. 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Product recommendations, BI and analytics, real-time malicious activity filtering, and data mining.More examples can be found on Apache’s  Powered By pageSpark Example in Scala (Spark shell can be used for this)// “sc” is a “Spark context” – this transforms the file into an RDD val textFile = sc.textFile("data.txt") // Return number of items (lines) in this RDD; count() is an action textFile.count() // Demo filtering.  Filter is a transform.  By itself this does no real work val linesWithSpark = textFile.filter(line => line.contains("Spark")) // Demo chaining – how many lines contain “Spark”?  count() is an action. textFile.filter(line => line.contains("Spark")).count() // Length of line with most words.  Reduce is an action. textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b) // Word count – traditional map-reduce.  collect() is an action val word Counts = text File.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b) word Counts.collect()Sample Spark Transformationsmap(func): Return a new distributed dataset formed by passing each element of the source through a function func.filter(func): Return a new dataset formed by selecting those elements of the source on which func returns trueunion(other Dataset): Return a new dataset that contains the union of the elements in the source dataset and the argument.Sample Spark Actionsreduce(func): Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel.collect(): Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data.count(): Return the number of elements in the dataset.The data is referred from the RDD Programming guide.What is MapReduce?MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a cluster.Programmers have been writing parallel programs for a long time in different languages like C++, Java, C#, Python. But, they have their own nuances and maintaining these, is the programmer's responsibility. There are chances of application crashing, performance hit, incorrect results. Also, such systems if grows very large is not very fault tolerant or difficult to maintain.MapReduce has simplified all these. Fault tolerance, parallel execution, resources management is all responsibility of the Resource manager and the framework. Programmers have to only concentrate on business logic by writing only map and reduce functions.Brief Description of MapReduce ArchitectureA MapReduce application has broadly two functions called map and reduce.Map: Mapper process takes input as key/value pair, processes them i.e. performs some computation and then produces intermediate results as key/value pairsi.e. map(k1,v1) ---> list(k2,v2)Reduce: Reducer process receives an intermediate key and a set of values in sorted order. It processes these and generates output key/value pairs by grouping values for each key.i.e. reduce(k2, list(v2)) ---> list(v3)Can also define an option function “Combiner” (to optimize bandwidth)If defined, runs after Mapper & before Reducer on every node that has run a map taskCombiner receives as input all data emitted by the Mapper instances on a given nodeCombiner output sent to the Reducers, instead of the output from the MappersIs a "mini-reduce" process which operates only on data generated by one machineHow does MapReduce work?MapReduce is usually applied to huge datasets. A MapReduce job splits the input data into smaller independent chunks called partitions and then processes them independently using map tasks and reduce tasks. Below is an example.MapReduce Word Count (Pseudocode)map(String input_key, String input_value): // input_key: document name // input_value: document contents for each word w in input_value: EmitIntermediate(w, "1"); reduce(String output_key, Iterator intermediate_values): // output_key: a word // output_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit(AsString(result));MapReduceApply a function to all the elements of Listlist1=[1,2,3,4,5]; square x = x * x list2=Map square(list1) print list2 -> [1,4,9,16,25]0Combine all the elements of list for a summarylist1 = [1,2,3,4,5]; A = reduce (+) list1 Print A -> 15Apache Spark vs MapReduceAfter getting off hangover how Apache Spark and MapReduce works, we need to understand how these two technologies compare with each other, what are their pros and cons, so as to get a clear understanding which technology fits our use case.As we can see, MapReduce involves at least 4 disk operations whereas Spark only involves 2 disk operations. This is one reason for Spark to be much faster than MapReduce. Spark also caches intermediate data which can be used in further iterations helping Spark improve its performance further. The more iterative the process the better is the Spark performance due to in-memory processing and caching. This is where MapReduce performance not as good as Spark due to disk read/write operations for every iteration.Let’s see a comparison between Spark and MapReduce on different other parameters to understand where to use Spark and where to use MapReduceAttributesMapReduceApache SparkSpeed/PerformanceMapReduce is designed for batch processing and is not as fast as Spark. It is used for gathering data from multiple sources and process it once and store in a distributed data store like HDFS. It is best suited where memory is limited and processing data size is so big that it would not fit in the available memory.Spark is 10-100 times faster because of in-memory processing and its caching mechanism. It can deliver near real-time analytics. It is used in Credit Card Processing, Fraud detection, Machine learning and data analytics, IoT sensors etcCostAs it is part of Apache Open Source there is no software cost.Hardware cost is less in MapReduce as it works with smaller memory(RAM) as compared to Spark. Even commodity hardware is sufficient.Spark also is Apache Open Source so no license cost.Hardware cost is more than MapReduce as even though Spark can work on commodity hardware it needs a lot more memory(RAM) as compared to MapReduce since it should be able to fit all the data in Memory for optimal performance. Cluster needs little high-end commodity hardware with lots of RAM else performance gets hitEase of UseMapReduce is a bit complex to write. MapReduce is written in Java and the APIs are a bit complex to code for new programmers, so there is a steep learning curve involved. The Pig has SQL like syntax and it is easier for SQL developers to get onboard easily. Also, there is no interactive mode available in MapReduceSpark has APIs in Scala, Java, Python, R for all basic transformations and actions. It also has rich Spark SQL APIs for SQL savvy developers and it covers most of the SQL functions and is adding more functions with each new release. Also, Spark has scope for writing User Defined Analytical Functions and Functions (UDF/UDAF) for anyone who would like to have custom functions.CompatibilityMapReduce is also compatible with all data sources and file formats Hadoop supports. But MapReduce needs another Scheduler like YARN or Mesos to run, it does not have any inbuilt Scheduler like Spark’s default/standalone scheduler.Apache Spark can in standalone mode using default scheduler. It can also run on YARN or Mesos. It can run on-premise or on the cloud. Spark supports most of the data formats like parquet, Avro, ORC, JSON, etc. It also supports multiple languages and has APIs for Java, Scala, Python, R.Data ProcessingMapReduce can only be used for batch processing where throughput is more important and latency can be compromised.Spark supports Batch as well as Stream processing, so fits both use cases and can be used for Lambda design where applications need both Speed layer and slower layer/data processing layerSecurityMapReduce has more security features.MapReduce can enjoy all the Hadoop security benefits and integrate with Hadoop security projects, like Knox Gateway and Sentry.Spark is a bit bare at the moment. Spark currently supports authentication via a shared secret. Spark can integrate with HDFS and it can use HDFS ACLs and file-level permissions. Spark can also run on YARN leveraging the capability of Kerberos.Fault ToleranceMapReduce uses replication for fault tolerance. If any slave daemon fails, master daemons reschedule all pending and in-progress operations to another slave. This method is effective, but it can significantly increase the completion times for operations with a single failure alsoIn Spark, RDDs are the building blocks and Spark also uses it RDDs and DAG for fault tolerance. If an RDD is lost, it will automatically be recomputed by using the original transformations.LatencyMapReduce has high latencySpark provides low latency performanceInteractive ModeMapReduce does not have any interactive mode of operation.Spark can be used interactively also for data processing. It has out-of-the-box support for spark shell for scala/python/RMachine Learning/Graph ProcessingNo support for these. A mahout has to be used for MLSpark has dedicated modules for ML and Graph processingBoth these technologies MapReduce and Spark have pros and cons:MapReduce is best suited for Analysis of archived data where data size is huge and it is not going to fit in memory, and if the instant results and intermediate solutions are not required. MapReduce also scales very well and the cluster can be horizontally scaled with ease using commodity machines.Offline Analytics is a good fit for MapReduce like Top Products per month, Unique clicks per banner.MapReduce is also suited for Web Crawling as well as Crawling tweets at scale and NLP like Sentiment Analysis.Another use case for MapReduce is de-duplicating data from social networking sites, job sites and other similar sites.MapReduce is also heavily used in Data mining for Generating the model and then classifying.Spark is fast and so can be used in Near Real Time data analysis.A lot of organizations are moving to Spark as their ETL processing layer from legacy ETL systems like Informatica. Spark as very good and optimized SQL processing module which fits the ETL requirements as it can read from multiple sources and can also write to many kinds of data sources.Spark can also handle Streaming data so its best suited for Lambda design.Most graph processing algorithms like page rank perform multiple iterations over the same data and this requires a message passing mechanism. Spark has great support for Graph processing using GraphX module.Almost all machine learning algorithms work iteratively. Spark has a built-in scalable machine learning library called MLlib which contains high-quality algorithms that leverage iterations and yields better results than one pass approximations sometimes used on MapReduce.Hadoop MapReduce is more mature as it has been there for a longer time and its support is also better in the open source community. It can be beneficial for really big data use case where memory is limited and data will not fit the RAM. Most of the time, Spark use case will involve Hadoop and other tools like Hive, Pig, Impala and so when these technologies complement each other it will be a win for both Spark and MapReduce.Conclusion:Hadoop Mapreduce is more mature as it has been there for a longer time and its support is also better in the open source community. It can be beneficial for really big data use case where memory is limited and data will not fit the RAM. Most of the time, Spark use case will involve Hadoop and other tools like Hive, Pig, Impala and so when these technologies complement each other it will be a win for both Spark and Mapreduce.
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Apache Spark Vs MapReduce

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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

If you ask any industry expert what language shoul... Read More