Which Career Should I Choose — Hadoop Admin or Spark Developer?

Today’s IT job market is revolving around Big data analytics, 60% of the highest paid jobs direct to Big data careers. However, the job market is ever changing in IT industry and organizations look for well-honed staffs. Hence, if you are looking for a career in Big data, you will be happy to know that Big data market is growing rapidly not only in IT sector but also in banking, marketing, and advertising sectors.

As per the statistics, there will be almost 50,000 vacancies related to Big data are currently available in different business sectors of India. Hadoop is a vast framework covering Hadoop administration and programming areas. It demands skills as Spark developer, Hadoop administration etc and opens up the horizon for a programmer and a non-programmer at the same time. Moreover, whether you are a fresher or experienced, you can step into Big data careers with proper training and certifications.

Which Big Data Career is Suitable for You?

We can answer this question from many angles.

Big data careers can be directed in two main streams –

• Hadoop administration
• Hadoop programmer

Hadoop administration is open to all in Big data careers. Whether you are a database administrator, non-programmer or a fresher you can explore this area. Moreover, if you are already in Big data careers and well acquainted with Hadoop ecosystem, Hadoop administration will add a feather in your cap. Whereas if you are not familiar with any programming languages like Java, Python, exploring Big data careers in Hadoop programming may be a little challenge for you. However, with proper training and practice, you can flourish Big data careers as a Spark developer easily. If you want to know more specifically what the job responsibilities of a Hadoop admin and a Hadoop programmer keep on reading the next sections. It is always easier to validate your position with the right information and data points.

What does a Hadoop Admin do?

With the increased adoption of Hadoop, there is a huge demand for Hadoop administrators to handle large Hadoop clusters in the organizations. A Hadoop admin performs a strong job role, he acts as the nuts and bolts of the business. A Hadoop admin is not only responsible to administrate manage Hadoop clusters but also manage other resources of the Hadoop ecosystem. His duties involve handling installation and maintenance of Hadoop clusters, performing an unaffected operation of Hadoop clusters, and manage overall performance.

Responsibilities of Hadoop Admin

• Installation of Hadoop in Linux environment.
• Deploying and maintaining a Hadoop cluster.
• Ensuring a Hadoop cluster is up and running all the time
• To decide the size of the Hadoop cluster based on the data to be stored in HDFS.
• Creating or removing a new node in a cluster environment.
• Configuring NameNode and its high availability
• Implement and administer Hadoop infrastructure on an ongoing basis.
• To deploy new and required hardware and software environments for Hadoop. In addition to that working on expanding existing environments.
• Creating Hadoop users including Linux users for different Hadoop ecosystem components and testing the access. Moreover, as a Hadoop administrator, you need to set up Kerberos principals
• Performance tuning in Hadoop clusters environment and also for Map Reduce.
• Screening of Hadoop cluster performances
• Monitoring connectivity and security in the cluster environment.
• Managing and reviewing log files.
• File system management.
• Providing necessary support and maintenance for HDFS.
• Performing necessary backup and recovery jobs in Hadoop
• Coordinating with the other business teams like infrastructure, network, database, application, and intelligence to ensure high data quality and availability.
• Resource management.
• Installing operating system and Hadoop updates when required. Furthermore, collaborating with application team for such installations.
• As a Hadoop admin working as Point of Contact for Vendor communications.
• Troubleshooting

Hence, keeping in mind the above points you must possess the following skills to achieve Big data careers as Hadoop admin.

Required Skills for Hadoop Administration

• Hadoop runs on Linux. Hence, you should have excellent working knowledge of LINUX
• Good experience in shell scripting
• Good understanding of OS levels like process management, memory management, storage management and resource scheduling.
• Good hold on configuration management.
• Basic knowledge of networking.
• Knowledge of automation tools related to installation.
• Knowledge of cluster monitoring tools
• Programming knowledge of core java is an added advantage but not mandatory.
• Good knowledge of networking
• Good understanding of Hadoop ecosystem and its components like Pig, Hive, Mahout, etc.

What does a Hadoop Developer do?

Hadoop’s programming part is handled through Map Reduce or Spark. However, Spark is going to replace Map Reduce in near future. Hence, if you want to be a Spark developer, your first and foremost job responsibility should be understanding data. Big data careers are all about handling with the big chunk of data. Hence if you want to stand out as a developer you should understand data and its pattern. Unless you are familiar with data it will be hard for you to get a meaningful insight out of those data chunk. Furthermore, you can foresee the possible results out of those scattered chunks of data.

In a nutshell, as a developer, you need to play with data, transform it programmatically, and decode it without destroying any information hidden in the data. In addition to that, it is all about programming knowledge. You will receive either unstructured or a structured data and after cleaning through various tools will need to process those in the desired format. However, this is not the only job that you have to do as a Spark developer. There are many other jobs to do on daily basis.

Responsibilities of Spark Developer

• Loading data using ETL tools from different data platforms into Hadoop platform.
• Deciding file format that could be effective for a task.
• Understanding the data mapping i.e. Input-output transformations.
• Cleaning data through streaming API or user-defined functions based on the business requirements.
• Defining Job Flows in Hadoop.
• Creating data pipelines to process real-time data. However, this may be streaming and unstructured data.
• Scheduling Hadoop jobs.
• Maintaining and managing log files.
• Hand holding with Hive and HBase for schema operations.
• Working on Hive tables to assign schemas.
• Deploying HBase clusters managing them.
• Working on pig and hive scripts to perform different joins on datasets
• Applying different HDFS formats and structure like to speed up analytics. For example Avro, Parquet etc.
• Building new Hadoop clusters
• Maintaining the privacy and security of Hadoop clusters.
• Fine tuning of Hadoop applications.
• Troubleshooting and debugging any Hadoop ecosystem at runtime.
• Installing, configuring and maintaining enterprise Hadoop environment if required

Required Skills for Spark Developer

From the above-mentioned job responsibilities, you must have gained some overview of required skills you must possess as a Hadoop developer. Let’s look into the list to get a comprehensive idea.

• A clear understanding of each component of Hadoop ecosystem like HBase, Pig, Hive, Sqoop, Flume, Oozie, etc.
• Knowledge of Java is essential for a Spark developer.
• Basic knowledge of Linux and its commands
• Excellent analytical and problem-solving skills.
• Hands on knowledge of scripting languages like Python or Perl.
• Data modeling skills with OLTP and OLAP
• Understanding of data and its pattern
• Good hands-on experience of java scheduling concepts like concurrency and multi-threading programming.
• Knowledge of data visualizations tools like Tableau.
• Basic database knowledge of SQL queries and database structures.
• Basic knowledge of some ETL tools like Informatica.

Salary Trend in the Market for Hadoop Developer and Administrator

The package does not vary much for different positions in Big Data. The average salary for a Hadoop admin is around $123,000 per year whereas for a Spark developer it could be $110,000. However, salary should not be the prime concern while choosing the Big Data careers. Because with experience it will increase automatically. Moreover, if you obtain a Hadoop certification it will give you an extensive knowledge along with a future scope in your Big data careers with an amazing salary.

Job Trend in the Market for Big data

This is an obvious fact that market demands for developers are more than the administrator in Big data careers. A developer can take over the job of a Hadoop administrator whereas an admin can’t play the role of a developer unless he has adequate programming knowledge. However, with the huge and complex production environment, now companies need dedicated Hadoop administrators.

Conclusion

If you are a programming savy then definitely Spark developer would be an easy transition and right fit for you. However, if you are a software administrator and want to continue to this role then go for Hadoop administration. Finally, the choice is solely up to you and your knack towards the Big Data careers you are looking for your future.

A good Training, Certifications in Big Data and 100% dedication can make anything possible. Remember one day you started from scratch!

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What is Apache Spark?

Apache Spark could be a new process engine that is an element of the Apache code Foundation that's powering the massive information applications round the world.

It is taking over from where Hadoop MapReduce gave up or from where MapReduce started finding it increasingly difficult to cope with the exacting needs of a fast-paced enterprise. Businesses today are struggling to find an edge and get new opportunities or practices that drive innovation and collaboration. Large amounts of unstructured data and the need for increased speed to fulfill the real-time analytics have made this technology a real alternative for Big Data computational exercises. Evolution of Apache Spark Before Spark, there was MapReduce which was used as a processing framework. Initially, Spark was started as one of the research projects in 2009 at UC Berkeley AMPLab. It was later open sourced in 2010. The major intention behind this project was to create a cluster management framework that supports various computing systems based on clusters. After its release to the market, Spark grew and moved to the Apache Software Foundation in 2013. Now, most of the organizations across the world have incorporated Apache Spark for empowering their Big Data applications. What Does Spark Do? Spark has the capacity to handle zetta and yottabytes of data at the same time it is distributed across various servers (physical or virtual). It has a comprehensive level of APIs and developer libraries, supporting various languages like Python, Scala, Java, R, etc. It is mostly utilized in combination with distributed data stores like Hadoop’s HDFS, Amazon’s S3, and MapR-XD. And, it also used with NoSQL databases like Apache HBase, MapR-DB, MongoDB, and Apache Cassandra. Sometimes, it is also used with distributed messaging stores like Apache Kafka and MapR-ES. Spark takes the programs that are written in complex languages and distributes to many machines. This is achieved based on an API like datasets and dataframes built upon Resilient Distributed Datasets (RDDs). Who Can Use Apache Spark? An extensive range of technology-based companies across the globe has moved toward Apache Spark. They were quick enough to identify the real value possessed by Spark such as Machine Learning and interactive querying. Industry leaders such as Huawei and IBM have adopted Apache Spark. The firms which were based on Hadoop, such as Hortonworks, Cloudera, and MapR, have moved to Apache Spark, already.

Apache Spark may be down pat by professionals WHO are within the IT domain so as to extend their marketability.

Big Data Hadoop professionals surely need to learn Apache Spark since it is the next most important technology in Hadoop processing. Moreover, even ETL professionals, SQL professionals, and project managers can gain immensely if they master Apache Spark. Finally, Data Scientists also need to gain in-depth knowledge of Spark to excel in their careers. Spark is extensively deployed in Machine Learning scenarios. Data Scientists are also expected to work in the Machine Learning domain, and hence they are the right candidates for Apache Spark training. Those who have an innate desire to learn the latest emerging technologies can also learn Spark. What Sets Spark Apart? There are multiple reasons to choose Apache Spark, out of which the most significant ones are given below: Speed: For large-scale processing of data, Spark is 100 times faster than Hadoop, regardless of the fact that data is stored in memory or on disk. Even if the data is stored on disk, Spark will be performing faster. Spark has a world record in on-disk sorting for large-scale data.Ease of use: Spark has a crystal-clear and declarative approach toward a cluster of datasets. It has a collection of operators for data transformation, APIs specific to the dataset domain, or dataframes to manipulate semi-structured and structured data. Spark also has a single-entry point for applications.Simplicity: Spark is designed in such a way that it can be easily accessible just by rich APIs. It is specially designed for quick and easy interaction in large data scale. APIs are well-documented for application developers and Data Scientists to instantly start working on Spark.Support: As mentioned earlier, Spark supports too many programming languages like Python, Scala, Java, R, etc. It also integrates with other storage solutions based on Hadoop ecosystem, such as MapR, Apache Cassandra, Apache HBase, and Apache Hadoop (HDFS). Increased Demand for Spark Professionals

Apache Spark is witnessing widespread demand with enterprises finding it progressively tough to rent the correct professionals to require on difficult roles in real-world situations.

It is a proven fact that these days the Apache Spark community is one in all the quickest massive information communities with over 750 contributors from over two hundred corporations worldwide.

Also, it is a fact that Apache Spark developers are among the highest paid programmers when it comes to programming for the Hadoop framework as compared to ten other Hadoop development tools.

As per a recent survey by O’Reilly Media, it's evident that having Apache Spark skills below your belt will offer you a hike in pay of the tune

of $11,000, and mastering Scala programming can give you a further jump of another $4,000 in your annual salary.Apache Spark and Storm skilled professionals get average yearly salaries of about $150,000, whereas data engineers get about $98,000. As per Indeed the average salaries for Spark developers in San Francisco is 35% more than the average salaries for Spark developers in the United States.

ETLHive provides the most comprehensive Spark Classroom training course to fast-track your career!

Bigdata Vs Machine Learning

Difference Between Big Data and Machine Learning

Data drives the modern organizations of the world so don’t be surprised if I call this world a data-driven world. Today’s business enterprises owe a large a part of their success to associate degree economy that’s firmly knowledge-oriented. The volume, variety, and speed of accessible information have fully grown exponentially. How an organization defines its data strategy and its approach towards analyzing and using available data will make a critical difference in its ability to compete in the future data world. As there area unit loads of choices out there within the information analytics market currently therefore this approach includes loads of selections that organizations have to be compelled to create like which framework to use? Which technology to use etc. One of such approach is that the alternative between huge information and Machine Learning.

Big information analytics is that the method of assembling and analyzing the big volume of {information} sets (called huge Data) to get helpful hidden patterns and different information like client selections, market trends that may facilitate organizations create a lot of wise to and client minded business selections.

Big data is a term that describes the data characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Big information are often analyzed for insights that cause higher selections and strategic business moves.

Machine learning could be a field of AI (Artificial Intelligence) by victimization that package application scan learn to extend their accuracy for the expecting outcomes. In layman’s terms, Machine Learning is the way to educating computers on how to perform complex tasks that humans don’t know how to accomplish.

Machine Learning field is so vast and popular these days that there are a lot of machine learning activities happening in our daily life and soon it will become an integral part of our daily routine. So, have you ever noticed any of those machine learning activities in your everyday life?

• You know those movie/show recommendations you get on Netflix or Amazon? Machine learning does this for you.
• How will Uber/Ola verify the worth of your cab ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with different passengers to reduce detours? The answer to any or all these queries is Machine Learning.
• How will a institution verify if a dealings is dishonorable or not? In most cases, it’s tough for humans to manually review every dealings attributable to its terribly high daily dealings volume. Instead, AI is employed to form systems that learn from the out there information to see what kinds of transactions area unit dishonorable.
• Ever puzzled what’s the technology behind the self-driving Google car? Again the answer is machine learning.

Now we all know What huge information vs Machine Learning area unit, but to decide which one to use at which place we need to see the difference between both.

Key Differences between Big Data vs Machine Learning

Both data processing and machine learning area unit non moving in information science. They typically run across or area unit confused with one another. They lay every other’s activities and therefore the relationship is best represented as mutualistic. It is not possible to check a future with only 1 of them. But there are still some unique identities that separate them in terms of definition and application. Here’s a glance at a number of the variations between huge information and machine learning and the way they will be used.

1. Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a sub field of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed. 

2. Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in huge information analytics, the analysis is completed on huge information. Machine learning, in straightforward terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.

3. Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the link between existing items of information with constant depth that machine learning will.

4. Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to fed to a machine learning system in order to do further analytics for predicting output results.

5. Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.

6. Machine learning performs tasks where human interaction doesn’t matter. Whereas, huge information analysis contains the structure and modeling of information which boosts decision-making system therefore need human interaction.

The Future of Big Data vs Machine Learning

By 2020, our accumulated digital universe of information can grow from 4.4 zettabytes to 44 zettabytes, as reported by Forbes. We’ll conjointly produce 1.7 megabytes of new information every second for every human being on the planet.

We’re simply scratching the surface of what huge information and machine learning area unit capable of. Instead of specializing in their variations, they both concern themselves with the same question: “How we can learn from data? ” At the top of the day, the sole issue that matters is however we tend to collect information and the way will we tend to learn from it to create future-ready solutions.