Apache Hadoop is a powerful platform for storing and processing big data. We’ll tell you how to find a competent Hadoop developer. 

The Hadoop Troop

The Big Data is taking over. So, recruiting some sparkling Hadoop talents is a must if you plan to reap the benefits of using large databases.

But how can you tell a worthy Hadoop specialist from a rookie candidate?

Well, there are a few nuances to pay attention to when you search for a freelance Hadoop developer or want to hire a senior Apache dev.

Our guide will walk you through the necessary interview stages.

We’ve also prepared some popular questions — they are used by the likes of Amazon, LinkedIn, and many others. They’ll help to understand whether the candidate has really good knowledge of how Hadoop works or not.

Skill count

Hadoop developer hire freelance Apache — this is an associative array that comes to mind when we’re talking about Hadoop.

So, what are the building blocks that form a Hadoop virtuoso in the first place? Here are some Hadoop common competencies a candidate should have.

  1. SQL

Proficiency in SQL, as well as in Distributed Systems, is a good start. The trick is that the more your candidate knows about these two, the better they understand database terminology. And Hadoop is all about architecting databases.

  1. Programming languages

The next requirement is a firm grip on these PLs: Java, JavaScript, NodeJS.

And not to forget their “relatives”: Closure, Python, Kotlin, and others. Any language from the Java family will be an excellent addition, basically.

Why? Hadoop was sculpted from Java. So, the more experience your candidate has in programming with some of these tools, the higher their competence is.

For instance, ask them if they have developed Pig Latin scripts before. Of if they know how to create JSP servlets. If yes — that’s a definitely huge plus.

  1. Portfolio

Now it’s time to let the creative genius shine! It’s preferable that a job seeker should have at least one Hadoop project in their portfolio.

It doesn’t have to be something fancy. It doesn’t need to be a ready-to-use product that you can integrate into your ecosystem right this minute. A “student project” will do.

First, it’ll prove that the applicant understands Hadoop’s terminology. And also how some other intricacies — analyzed data, pig scripting, design patterns — work.

Second, it shows that they can deliver a finished project. And doing so requires a good deal of discipline and focus. Especially if it was produced solo.

  1. Frameworks

HDFS or Hadoop Distributed File System is a data warehouse offered by the platform. The key benefits are simple:

  • It’s cheap.
  • It’s pretty much monstrous in size.

Needless to say, HDFS is related to such essential aspects as importing and exporting data, processing it, and finally extracting the results that your business needs.

In turn, this requires your candidate to be good with Apache Spark and MapReduce. These are vital frameworks that allow manipulating the big data stored in HDFS.

  1. Spark SQL

We’ve already mentioned SQL. Basically, Spark SQL is a tool responsible for structured data processing. The key benefit of this module is that it makes data querying tasks extremely quick.

Thanks to its programming abstraction, DataFrames, and other perks, Spark SQL enables devs to create SQL queries with the help of code transformations.

In the long run, this tool will let your project achieve impressive results. Much faster. So, if the candidate knows how to operate SQL Spark — that’s another “pro”.

  1. Apache Hive

Many Hadoop developer jobs on Hired mention Apache Hive proficiency as a critical skill. And there’s a good reason!

In a nutshell, Apache Hive is a digital warehouse used for data storage. It’s a fundamental tool for performing data queries from various file systems and databases. Plus, it has a high Plus, it has a high fault tolerance.

Again, it’s a tool powered by SQL. Ask the candidate if they are familiar with creating hive tables loading or writing hive queries.

Besides, a great feature that Apache Hive has is partitioning. This feature makes data retrieval simpler and faster. In turn, it’s quite helpful for big data analytics.

  1. Kafka

Not a Bohemian novelist, but a module used for analytical work. So, experience with it is mandatory.

This module is a life-saver when you need to process data. Lots of data, to be precise! It’s also quite helpful with the in-memory microservices.

Kafka has a remarkable variety of practical applications.

With it, you can keep an eye on the feedback coming from your call centers. Kafka can learn about complaints, requests, orders, and other valuable info. (That comes from your clientele).

Another great way to use it is by analyzing feedback from the IoT sensors.

This type of info will help you explore the habits and behavior of the users. Which functions do they enjoy more? Which smart appliances do the biggest chunk of work? What voice assistants are the regular go-to’s? You get the idea.

  1. Sqoop

Experience in importing and transferring data is another must. Sqoop is a flexible tool which allows running data between HDFS and other database servers: Teradata, SAP, AWS, Postgres, and many others.

Your soon-to-be developer must be Sqoop-experienced. Otherwise you won’t be able to dispatch vast chunks of data from Hadoop to the external storage. And at some point you will need to execute this maneuver to:

  • Back up the valuable info.
  • Share it with a third party.
  • Do extra processing.

In other words, knowledge of technicalities that go along with Sqoop are indispensable.

  1. Graphics

A Hadoop developer resume that makes you want to hire it must mention GraphX or Graph. These are API tools, with which devs can create graphs, edges, vertices and other visual data.

For example, GraphX comprises exploratory analysis and iterative graph computation. Plus, it can boast of the Extract, Transform and Load approach. This know-how allows you to load and transform large data amounts to a different system. A whole caboodle of perks!

  1. Clusters

A Hadoop cluster is a network, which consists of master and worker nodes. In turn,  these nodes keep the distributed file system going like a Swiss clock.

So, it would be great to see the likes of Ambari, Google Cloud Dataproc, RStudio, Qubole, and others.

Operating Hadoop clusters is critical. Besides, those tools are great for monitoring progress  — many of them check and update the status of every app active.

What else to know?

During the interview, use some of these top questions related to Hadoop:

  • Define speculative execution.
  • Does Distributed Cache have any benefits?
  • How many JVMs can be on a single node?
  • What does InputSplit do? Why is it necessary?
  • Which tool would you use to find that unique URL?
  • How to find the first unique URL in a billion of URLs?
  • How big is the Big Data that you’ve personally worked with?
  • In which scenarios would you use Bucketing and Partitioning?
  • Where do heap errors come from, and how do you get rid of them?
  • TextInput and KeyValue — what’s the difference between these formats?

Why do you need Hadoop?

Apache Hadoop is a top-notch tool when it comes to handling big data. And you already know how essential this data is for a business. Especially the one that operates on a large scale.

As statistics show, big data is an area that needs some hard workers. Badly!

Among all else, it is reported that 95% of companies suffer from poorly structured data. 97.2% of organizations — commercial and non-profit — invest in it. And Netflix saves up $1 billion with its help!

The demand for big data is far from reaching its peak. Enormous budgets are poured into it. And it is the right tool to make it all work for you. Plus, Hadoop is an open-source system.

Adobe, Spotify, Yahoo, eBay, and others already employ it. Maybe it’s your turn now?

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We’ll help your business evolve! Hadoop devs, SQL developer jobs and direct hire are at your service — just announce a job opening and scout for the best talents!