Search

Fundamentals of Apache Spark

IntroductionBefore getting into the fundamentals of Apache Spark, let’s understand What really is ‘Apache Spark’ is? Following is the authentic one-liner definition.Apache Spark is a fast and general-purpose, cluster computing system.One would find multiple definitions when you search the term Apache Spark. All of those give similar gist, just different words. Let’s understand these special keywords which describe Apache Spark. Fast: As spark uses in-memory computing it’s fast. It can run queries 100x faster. We will get to details of architecture later to understand this aspect better little later in the article. One would find the keywords ‘Fast’ and/or ‘In-memory’ in all the definitions. General Purpose: Apache spark is a unified framework. It provides one execution model for all tasks and hence very easy for developers to learn and they can work with multiple APIs easily. Spark offers over 80 high-level operators that make it easy to build parallel apps and one can use it interactively from the Scala, Python, R, and SQL shells.Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.Cluster Computing: Efficient processing of data on Set of computers (Refer commodity hardware here) or distributed systems. It’s also called a Parallel Data processing Engine in a few definitions. Spark is utilized for Big data analytics and related processing. One more important keyword associated with Spark is Open Source. It was open-sourced in 2010 under a   BSD license.Spark (and its RDD) was developed(earliest version as it’s seen today), in 2012, in response to limitations in the   MapReduce cluster computing paradigm. Spark is commonly seen as an in-memory replacement of MapReduce.Since its release, Apache Spark has seen rapid adoption due to its characteristics briefly discussed above.Who should go for Apache SparkBefore trying to find out whether Apache spark is for me? Or whether I have the right skill set, It's important to focus on the generality characteristic in further depth.Apache Spark consists of Spark Core and a set of libraries. The core is the distributed execution engine and the Java, Scala, and Python APIs offer a platform for distributed ETL application development. Additional libraries, built atop the core, allow diverse workloads for streaming, SQL, and machine learning.As Spark provides these multiple components, it’s evident that Spark is developed and widely utilized for big data and analytics.  Professionals who should learn Apache SparkIf one is aspiring to be landed into the following professions or anyone who has an interest in data and insights, Knowledge of spark will prove useful:Data ScientistsData EngineersPrerequisites of learning Apache SparkMost of the students looking for big data training, Apache spark is number one framework in big data. So most of the knowledge seekers looking for spark training, it is important to note that there are few prerequisites to learn apache spark.Before getting into Big data, you must have minimum knowledge on:Anyone of the programming languages >> Core   Python or Scala.Spark installations can be done on any platform but its framework is similar to Hadoop and hence having knowledge of HDFS and YARN is highly recommended. Having knowledge of Hive is an added advantage but is not mandatory.Basic knowledge of SQL. In SQL mainly select * from, joins and group by these three commands highly recommended.Optionally, knowing any cloud technology like AWS. Recommended for those who want to work with production-like environments.System requirements of Apache SparkOfficial site for  Apache Spark gives following recommendation (Traverse link for further details)Storage System: There are few ways to set this up as follows: Spark can run on the same node as HDFS. Spark standalone node cluster can be installed on the same nodes and configure Spark and Hadoop memory and CPU usage accordingly to avoid any interference.Or,1. Hadoop and Spark can execute on common Resource Manager ( Ex. Yarn etc)Or,2. Spark will be executing in same Local Area Network as HDFS but on separate nodes.Or3. If a requirement is a quick response and low latency from data stores then execute compute jobs on separate nodes than that of storage nodes.Local Disks: Typically 4-8 disks per node, configured without RAID.If underline OS is Linux then mount the disk with noatime option and in Spark environment configure spark.local.dir variable to be a comma-separated list of local disks.Note: For HDFS, it can be the same disk as HDFS.Memory: Minimum 8GB - 100s of GBs of memory per machine.A recommendation is the allocation of 75% of the memory to Spark.Network: 10GB or faster speed network.CPU cores: 8-16 Cores per machineHowever, for Training and Learning purpose and just to taste Spark, following are two available options: Run it locally Use AWS EMR (Or any cloud computing service)For learning purposes, minimum 4gb ram system with minimum 30gb disk may prove enough.History of Apache SparkSpark was primarily developed to Overcome the Limitations of MapReduce.Versioning: Spark initial version was version 0, version 1.6 is assumed to be a stable version and is being used in multiple commercial corporate projects. Version 2.3 is the latest available version. MapReduce is cluster computing  paradigm, which forces a particular linear  data flow structure on distributed programs: MapReduce programs read input data from disk,  map a function across the data,  reduce the results of the map, and store reduction results on disk. Due to multiple copies of data and multiple I/O as described, MapReduce takes lots of time to process the volume of data. MapReduce can do only batch time processing and is unsuitable for real-time data processingIt is unsuitable for trivial join like transformations. It’s unfit for large data on a network and also with OLTP data.Also, not suitable for graphics and interactive data.Spark overcomes all these limitations and able to do faster processing too on the local disk as well.Why Apache Spark?Numerous advantages of Spark have made its a market favorite.Let’s discuss one by one.Speed: Extends MapReduce Model to support computations like stream processing and interactive queries.Single Combination for processes and multiple tools:  Covers multiple workloads ( in a traditional system, it used to require different distributed systems), which makes combining different processing types and ease of tool management.Unification: Developers have to learn only one platform unlike multiple languages and tools in a traditional system.Support to different Resource Managers: Spark supports Hadoop HDFS system, and YARN for resource management but it’s not the only resource manager it supports. It works on MESOS and on any standalone scheduler like spark resource manager.Support for cutting-edge Innovation: Spark provides capabilities and support for an array of new-age technologies ranging from built-in machine learning libraries,   visualization tools, support for near processing (which was in a way the biggest challenge pre- spark era) and supports seamless integration with other deep learning frameworks like TensorFlow. This enables Spark to provide an innovative solution for new age use-cases.Spark can access diverse data sources and make sense of them all and hence it’s trending in the market over any other cluster computing software available. Who uses Apache SparkListing a few use cases of Apache spark below :1. Analytics - Spark can be very useful when building real-time analytics from a stream of incoming data.2. E-commerce - Information about the real-time transaction can be passed to streaming clustering algorithms like alternating least squares or K-means clustering algorithm. The results can be combined with data from other sources like social media profiles, product reviews on forums, customer comments, etc. to enhance the recommendations to customers based on new trends.Shopify: At Shopify, we underwrite credit card transactions, exposing us to the risk of losing money. We need to respond to risky events as they happen, and a traditional ETL pipeline just isn’t fast enough. Spark Streaming is an incredibly powerful real-time data processing framework based on Apache Spark. It allows you to process real-time streams like Apache Kafka using Python with incredible simplicity.Alibaba: Alibaba Taobao operates one of the world’s largest e-commerce platforms. We collect hundreds of petabytes of data on this platform and use Apache Spark to analyze these enormous amounts of data.3. Healthcare Industry –Healthcare has multiple use-cases of unstructured data to be processed in real-time. It has data ranging from image formats like scans etc to specific medical industry standards and wearable tracking devices. Many healthcare providers are keen on using spark for patient’s records to build 360 degrees view of the patient to do accurate diagnosis.MyFitnessPal: MyFitnessPal needed to deliver a new feature called “Verified Foods.” The feature demanded a faster pipeline to execute a number of highly sophisticated algorithms. Their legacy non-distributed Java-based data pipeline was slow, did not scale, and lacked flexibility.Here are a few other examples from industry leaders:Regeneron: Future of Drug Discovery with Genomics at Scale powered by SparkZeiss: Using Spark Structured Streaming for Predictive MaintenanceDevon Energy: Scaling Geographic Analytics with Spark GraphXYou can also learn more about use cases of Apache Spark  here.Career Benefits:Career Benefits of Spark for you as an individual:Apache Spark developers earn the highest average salary among all other programmers. According to its  2015 Data Science Salary Survey, O’Reilly found strong correlations between those who used Apache Spark and those who were paid more money. In one of its models, using Spark added more than $11,000 to the median salary.If you’re considering switching to this extremely in-demand career then taking up the  Apache Spark training will be an added advantage. Learning Spark will give you a steep competitive edge and can land you up in market best-paying jobs with top companies. Spark has gained enough adherents over the years to place it high on the list of fastest-growing skills; data scientists and sysadmins have evaluated the technology and clearly seen what they liked.  April’s Dice Report explored the fastest-growing technology skills, based on an analysis of job postings and data from Dice’s annual salary survey. The results are below; percentages are based on year-over-year growth in job postings:Benefits of Spark implementing Spark in your organization:Apache spark is now a decade older but still going strong. Due to lightning-fast processing and numerous other advantages discussed so far, Spark is still the first choice of many organizations.Spark is considered to be the most popular open-source project on the planet, with more than 1,000 contributors from 250-plus organizations, according to Databricks.ConclusionTo sum up, Spark helps to simplify the computationally intensive task of processing high volumes of real-time or batch data. It can seamlessly integrate with complex capabilities such as machine learning and graph algorithms. In short, Spark brings exclusive Big Data processing (which earlier was only for giant companies like Google) to the masses.Do let us know how your learning experience was, through comments below.Happy Learning!!!

Fundamentals of Apache Spark

10K
Fundamentals of Apache Spark

Introduction

Before getting into the fundamentals of Apache Spark, let’s understand What really is ‘Apache Spark’ is? Following is the authentic one-liner definition.

Apache Spark is a fast and general-purpose, cluster computing system.

One would find multiple definitions when you search the term Apache Spark. All of those give similar gist, just different words. Let’s understand these special keywords which describe Apache Spark. 

Fast: As spark uses in-memory computing it’s fast. It can run queries 100x faster. We will get to details of architecture later to understand this aspect better little later in the article. One would find the keywords ‘Fast’ and/or ‘In-memory’ in all the definitions. 

General Purpose: Apache spark is a unified framework. It provides one execution model for all tasks and hence very easy for developers to learn and they can work with multiple APIs easily. Spark offers over 80 high-level operators that make it easy to build parallel apps and one can use it interactively from the Scala, Python, R, and SQL shells.

Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Cluster Computing: Efficient processing of data on Set of computers (Refer commodity hardware here) or distributed systems. It’s also called a Parallel Data processing Engine in a few definitions. Spark is utilized for Big data analytics and related processing. 

One more important keyword associated with Spark is Open Source. It was open-sourced in 2010 under a   BSD license.

Spark (and its RDD) was developed(earliest version as it’s seen today), in 2012, in response to limitations in the   MapReduce cluster computing paradigm. Spark is commonly seen as an in-memory replacement of MapReduce.

Since its release, Apache Spark has seen rapid adoption due to its characteristics briefly discussed above.

Who should go for Apache Spark

Before trying to find out whether Apache spark is for me? Or whether I have the right skill set, It's important to focus on the generality characteristic in further depth.

Apache Spark consists of Spark Core and a set of libraries. The core is the distributed execution engine and the Java, Scala, and Python APIs offer a platform for distributed ETL application development. Additional libraries, built atop the core, allow diverse workloads for streaming, SQL, and machine learning.

Who should go for Apache Spark

As Spark provides these multiple components, it’s evident that Spark is developed and widely utilized for big data and analytics.  

Professionals who should learn Apache Spark

If one is aspiring to be landed into the following professions or anyone who has an interest in data and insights, Knowledge of spark will prove useful:

  • Data Scientists
  • Data Engineers

Prerequisites of learning Apache Spark

Most of the students looking for big data training, Apache spark is number one framework in big data. So most of the knowledge seekers looking for spark training, it is important to note that there are few prerequisites to learn apache spark.

Before getting into Big data, you must have minimum knowledge on:

  • Anyone of the programming languages >> Core   Python or Scala.
  • Spark installations can be done on any platform but its framework is similar to Hadoop and hence having knowledge of HDFS and YARN is highly recommended. Having knowledge of Hive is an added advantage but is not mandatory.
  • Basic knowledge of SQL. In SQL mainly select * from, joins and group by these three commands highly recommended.
  • Optionally, knowing any cloud technology like AWS. Recommended for those who want to work with production-like environments.

System requirements of Apache Spark

Official site for  Apache Spark gives following recommendation (Traverse link for further details)

Storage System: There are few ways to set this up as follows: 

Spark can run on the same node as HDFS. Spark standalone node cluster can be installed on the same nodes and configure Spark and Hadoop memory and CPU usage accordingly to avoid any interference.
Or,
1. Hadoop and Spark can execute on common Resource Manager ( Ex. Yarn etc)
Or,
2. Spark will be executing in same Local Area Network as HDFS but on separate nodes.
Or
3. If a requirement is a quick response and low latency from data stores then execute compute jobs on separate nodes than that of storage nodes.

Local Disks: Typically 4-8 disks per node, configured without RAID.
If underline OS is Linux then mount the disk with noatime option and in Spark environment configure spark.local.dir variable to be a comma-separated list of local disks.
Note: For HDFS, it can be the same disk as HDFS.

Memory: Minimum 8GB - 100s of GBs of memory per machine.
A recommendation is the allocation of 75% of the memory to Spark.

Network: 10GB or faster speed network.

CPU cores: 8-16 Cores per machine

However, for Training and Learning purpose and just to taste Spark, following are two available options: 

  1. Run it locally 
  2. Use AWS EMR (Or any cloud computing service)

For learning purposes, minimum 4gb ram system with minimum 30gb disk may prove enough.

History of Apache Spark

History of Apache Spark

Spark was primarily developed to Overcome the Limitations of MapReduce.

Versioning: Spark initial version was version 0, version 1.6 is assumed to be a stable version and is being used in multiple commercial corporate projects. Version 2.3 is the latest available version. 

MapReduce is cluster computing  paradigm, which forces a particular linear  data flow structure on distributed programs: MapReduce programs read input data from disk,  map a function across the data,  reduce the results of the map, and store reduction results on disk. 

  1. Due to multiple copies of data and multiple I/O as described, MapReduce takes lots of time to process the volume of data. 
  2. MapReduce can do only batch time processing and is unsuitable for real-time data processing
  3. It is unsuitable for trivial join like transformations. 
  4. It’s unfit for large data on a network and also with OLTP data.
  5. Also, not suitable for graphics and interactive data.

Spark overcomes all these limitations and able to do faster processing too on the local disk as well.

Why Apache Spark?

Numerous advantages of Spark have made its a market favorite.

Let’s discuss one by one.

  1. Speed: Extends MapReduce Model to support computations like stream processing and interactive queries.
  2. Single Combination for processes and multiple tools:  Covers multiple workloads ( in a traditional system, it used to require different distributed systems), which makes combining different processing types and ease of tool management.
  3. Unification: Developers have to learn only one platform unlike multiple languages and tools in a traditional system.
  4. Support to different Resource Managers: Spark supports Hadoop HDFS system, and YARN for resource management but it’s not the only resource manager it supports. It works on MESOS and on any standalone scheduler like spark resource manager.
  5. Support for cutting-edge Innovation: Spark provides capabilities and support for an array of new-age technologies ranging from built-in machine learning libraries,   visualization tools, support for near processing (which was in a way the biggest challenge pre- spark era) and supports seamless integration with other deep learning frameworks like TensorFlow. This enables Spark to provide an innovative solution for new age use-cases.

Spark can access diverse data sources and make sense of them all and hence it’s trending in the market over any other cluster computing software available. 

Who uses Apache Spark

Who uses Apache Spark

Listing a few use cases of Apache spark below :

1. Analytics - Spark can be very useful when building real-time analytics from a stream of incoming data.

2. E-commerce - Information about the real-time transaction can be passed to streaming clustering algorithms like alternating least squares or K-means clustering algorithm. The results can be combined with data from other sources like social media profiles, product reviews on forums, customer comments, etc. to enhance the recommendations to customers based on new trends.

Shopify: At Shopify, we underwrite credit card transactions, exposing us to the risk of losing money. We need to respond to risky events as they happen, and a traditional ETL pipeline just isn’t fast enough. Spark Streaming is an incredibly powerful real-time data processing framework based on Apache Spark. It allows you to process real-time streams like Apache Kafka using Python with incredible simplicity.

Alibaba: Alibaba Taobao operates one of the world’s largest e-commerce platforms. We collect hundreds of petabytes of data on this platform and use Apache Spark to analyze these enormous amounts of data.

3. Healthcare Industry –
Healthcare has multiple use-cases of unstructured data to be processed in real-time. It has data ranging from image formats like scans etc to specific medical industry standards and wearable tracking devices. Many healthcare providers are keen on using spark for patient’s records to build 360 degrees view of the patient to do accurate diagnosis.

MyFitnessPal: MyFitnessPal needed to deliver a new feature called “Verified Foods.” The feature demanded a faster pipeline to execute a number of highly sophisticated algorithms. Their legacy non-distributed Java-based data pipeline was slow, did not scale, and lacked flexibility.

Here are a few other examples from industry leaders:

You can also learn more about use cases of Apache Spark  here.

Career Benefits:

Career Benefits of Spark for you as an individual:

Apache Spark developers earn the highest average salary among all other programmers. According to its  2015 Data Science Salary Survey, O’Reilly found strong correlations between those who used Apache Spark and those who were paid more money. In one of its models, using Spark added more than $11,000 to the median salary.

If you’re considering switching to this extremely in-demand career then taking up the  Apache Spark training will be an added advantage. Learning Spark will give you a steep competitive edge and can land you up in market best-paying jobs with top companies. Spark has gained enough adherents over the years to place it high on the list of fastest-growing skills; data scientists and sysadmins have evaluated the technology and clearly seen what they liked.  April’s Dice Report explored the fastest-growing technology skills, based on an analysis of job postings and data from Dice’s annual salary survey. The results are below; percentages are based on year-over-year growth in job postings:

Career Benefits of Apache Spark

Benefits of Spark implementing Spark in your organization:

Apache spark is now a decade older but still going strong. Due to lightning-fast processing and numerous other advantages discussed so far, Spark is still the first choice of many organizations.
Spark is considered to be the most popular open-source project on the planet, with more than 1,000 contributors from 250-plus organizations, according to Databricks.

Conclusion

To sum up, Spark helps to simplify the computationally intensive task of processing high volumes of real-time or batch data. It can seamlessly integrate with complex capabilities such as machine learning and graph algorithms. In short, Spark brings exclusive Big Data processing (which earlier was only for giant companies like Google) to the masses.

Do let us know how your learning experience was, through comments below.
Happy Learning!!!

Shruti

Shruti Deshpande

Blog Author

10+ years of data-rich experience in the IT industry. It started with data warehousing technologies into data modelling to BI application Architect and solution architect.


Big Data enthusiast and data analytics is my personal interest. I do believe it has endless opportunities and potential to make the world a sustainable place. Happy to ride on this tide.


*Disclaimer* - Expressed views are the personal views of the author and are not to be mistaken for the employer or any other organization’s views.

Join the Discussion

Your email address will not be published. Required fields are marked *

Suggested Blogs

Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in today’s digital world. There are tons of data generated and collected from the various processes carried out by the company. This data could contain patterns and methods as to how the company can improve its processes. The data also contains feedback from the customer. Needless to say, this data is vital to the company and should not be discarded. But, the entire set is also not useful, a certain amount of data is futile. This set should be differentiated from the useful part and discarded. To carry out this major process, various platforms are used. The most popular among these platforms is Hadoop. Hadoop can efficiently analyse the data and extract the useful information. It also comes with its own set of advantages and disadvantages such as: Pros 1. Range of data sources The data collected from various sources will be of structured or unstructured form. The sources can be social media, clickstream data or even email conversations. A lot of time would need to be allotted in order to convert all the collected data into a single format. Hadoop saves this time as it can derive valuable data from any form of data. It also has a variety of functions such as data warehousing, fraud detection, market campaign analysis etc. 2. Cost effective In conventional methods, companies had to spend a considerable amount of their benefits into storing large amounts of data. In certain cases they even had to delete large sets of raw data in order to make space for new data. There was a possibility of losing valuable information in such cases. By using Hadoop, this problem was completely solved. It is a cost-effective solution for data storage purposes. This helps in the long run because it stores the entire raw data generated by a company. If the company changes the direction of its processes in the future, it can easily refer to the raw data and take the necessary steps. This would not have been possible in the traditional approach because the raw data would have been deleted due to increase in expenses. 3. Speed Every organization uses a platform to get the work done at a faster rate. Hadoop enables the company to do just that with its data storage needs. It uses a storage system wherein the data is stored on a distributed file system. Since the tools used for the processing of data are located on same servers as the data, the processing operation is also carried out at a faster rate. Therefore, you can processes terabytes of data within minutes using Hadoop. 4. Multiple copies Hadoop automatically duplicates the data that is stored in it and creates multiple copies. This is done to ensure that in case there is a failure, data is not lost. Hadoop understands that the data stored by the company is important and should not be lost unless the company discards it. Cons 1. Lack of Preventive Measures When handling sensitive data collected by a company, it is mandatory to provide the necessary security measures. In Hadoop, the security measures are disabled by default. The person responsible for data analytics should be aware of this fact and take the required measures to secure the data. 2. Small Data Concerns There are a few big data platforms in the market that aren’t fit for small data functions. Hadoop is one such platform wherein only large business that generates big data can utilize its functions. It cannot efficiently perform in small data environments. 3. Risky Functioning Java is one of the most widely used programming languages. It has also been connected to various controversies because cyber criminals can easily exploit the frameworks that are built on Java. Hadoop is one such framework that is built entirely on Java. Therefore, the platform is vulnerable and can cause unforeseen damages. Every platform used in the digital world comes with its own set of advantages and disadvantages. These platforms serve a purpose that it vital to the company. Hence, it is necessary to check if the pros outweigh the cons. If they do, then utilize the pros and take preventive measures to guard yourself against the cons. To know more about Hadoop and pursue a career in it, enrol for a big data Hadoop certification. You can also gain better with big data Hadoop training online courses.
22354
Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in tod... Read More

Master Big Data With A Hadoop Certification

Ever wondered how your social media posts or online transaction details are always available? And not just yours, but anyone who uses the internet can access their data whenever they want. With the advent of technology and the internet, the amount of data generated online is humongous. According to a report, almost 90% of the data that we have today have been created over the last couple of years. Whether it’s networking sites or weather reports, data is being generated every second and it needs to be stored for various purposes. How does this happen? Surely, all of these information can’t be stored in physical storage devices like pen drives or hard disks, unless there’s a huge football field to accommodate them. This is where ‘Big Data’ plays a huge role. Let’s find out more about it. What Is Big Data? Big data is a technology that can collate and store huge amounts of data every day. The information stored is important as it allows companies to assess their customers and market their products. For example, when a product or service is advertised on Facebook, users like or comment on the post. This data is then used by companies to judge the popularity of their product and further promote or improve their marketing campaigns accordingly. Big data, therefore, is one of the most important technologies of the modern world. However, most of the data is unstructured, which means it can’t be used for analysis and data mining. This where you need a software that can sort the unstructured data and provide data security. How Does Hadoop Help Big Data? Hadoop is an open-source, Java-based programming framework that’s capable of storing and processing large amounts of data. Hadoop makes use of a distributed computing framework, wherein data is formatted and stored in clusters of commodity hardware. Simultaneously, it also processes data by using cheap computers. This software is available for free download and is run and maintained by developers from all around the world. However, nowadays, commercial Hadoop software are being made available to suit the various data processing and storage needs of organizations. What Are The Advantages Of Hadoop? Apart from the fact that Hadoop can process and store data quickly, there are many other reasons that makes it the most preferred data storage choice. Let’s take a look at some of them: ● Hadoop offers you the flexibility of storing data as you want. For example, in traditional databases, you would have to first process the data and then store it. But, in Hadoop, you can store anything and then analyze it later and this includes unstructured data like images, videos, and texts. ● When you’re using the Hadoop framework, you can be assured that data will not be lost due to hardware failure. If any one of the nodes become faulty, the data will automatically distributed amongst other nodes. Also, several copies of the data will be made and stored automatically. ● It’s a free open-source software that relies on commodity hardware to process and store data. You can scale it up as per your needs by adding nodes. Why Take A Big Data And Hadoop Certification? If you want to begin a career in the IT industry or would like to become a data analyst, then you can’t do without big data. It’s everywhere and every internet-based service relies on big data technology to store their information and analyze it. No matter which field you choose, right from social media to weather reports, big data plays a big role in keeping them up and running. Therefore, it only makes senses that you take a certification in big data and Hadoop to add another point to your resume and eventually land better jobs. How Will The Certification Help Me? When you’re preparing for the certifying exam, you can take up a training course to better acquaint yourself with the subject. During your training, you will be taught about the various aspects of Hadoop and how it’s used to store big data. Some of the things that you’ll be learning during the training are: ● A clear understanding of the Hadoop ecosystem that includes Flame and Apache oozie workflow scheduler ● Mastery over the basic and advanced concepts of Hadoop 2.7 framework ● Learning to write MapReduce programs ● Conduct detailed data analysis with the help of Pig and Hive Apart from these, you will also be given training on setting up configurations for Hadoop clusters. With big data becoming an integral part of most businesses, mastering the Hadoop technology will help you land well-paying jobs. If you’ve been on the lookout for big data analytics jobs or want to become a software developer and architect, then a Hadoop certification will open up a world of opportunities for you.
14448
Master Big Data With A Hadoop Certification

Ever wondered how your social media posts or onlin... Read More

What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop? Big data specifies datasets that are very big. It’s a hoard of large datasets that cannot be processed by the traditional methods of computing. Big data is related to a complete subject rather than merely data that can be processed using various techniques, tools, and framework. Hadoop is an open-source frame, which is based on Java Programming and supports the storage and processing capabilities of extremely large datasets in a computing environment that is distributed across branches. Hadoop was developed by a team of computer scientists, which comprised of Mike Cafarella and Doug Cutting in 2005, to support the distribution capabilities of search engines. There are pros & cons in hadoop, but compared to pros, cons are negotiable. Benefits of Hadoop • Scalable: Hadoop is a storage platform that is highly scalable, as it can easily store and distribute very large datasets at a time on servers that could be operated in parallel. • Cost effective: Hadoop is very cost-effective compared to traditional database-management systems. • Fast: Hadoop manages data through clusters, thus providing a unique storage method based on distributed file systems. Hadoop’s unique feature of mapping data on the clusters provides a faster data processing. • Flexible: Hadoop enables enterprises to access and process data in a very easy way to generate the values required by the company, thereby providing the enterprises with the tools to get valuable insights from various types of data sources operating in parallel. • Failure resistant: One of the great advantages of Hadoop is its fault tolerance. This fault resistance is provided by replicating the data to another node in the cluster, thus in the event of a failure, the data from the replicated node can be used, thereby maintaining data consistency. Careers with Hadoop Big data with Hadoop training could make a great difference in getting your dream career. Employees with capabilities of handling big data are considered more valuable to the organisation. Hadoop skills are in great demand and thus it is very important for the IT professionals to keep up with the current trend, because the amount of data generated day by day is ever increasing. According to the Forbes magazine report of 2015, around 80% of the global organisations are reported to make high- or medium-level investments in big data analytics. They consider this investment to be very significant and so they plan to increase their investment in big data analytics. There are more job opportunities with Hadoop. Looking at the market forecast for Big Data, it looks like the need for Big Data engineers is going to increase. Big Data is here to stay, as the data is ever increasing and does not seem to slow down in the coming years. A research conducted by the Avendus Capital reported that the IT market in India for big data is hovering near $1.15 billion in the year 2015. Big data analytics contributed for about one-fifth of the nation’s KPO market, which is considered to be worth almost $5.6 billion. The Hindu also predicted that by the end of year 2018, India alone would be facing a shortage of almost quarter million Big Data scientists. Therefore, Big Data Analysis with Hadoop presents a great career and tremendous growth opportunity.
16348
What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop? Big data s... Read More