big data

Big Data has turned into an indispensable business function. Every company has ventured or is about to venture to make a better decision from its analytic capabilities. If you’re on the market, planning to get a job in Big Data, you will need an extensive set of skills. Mostly comprising know-how of different tools, technologies, and an analytical mindset.

Since early 2000, the growth in Big Data has been phenomenal. In terms of adoption, the Big Data market is expected to reach $103 billion in 2023. That shouldn’t be surprising, as over 90 percent of executives are already investing in Big Data in. ( source: ).

As companies increasingly realize the benefits of Big Data, the demand for Big Data Engineers has been increasing. Looking at the promising future of Big Data, many tech professionals taking big data certifications and equipping themselves with the skills required to work in this field.

Before we get down to the skills required for Big Data Engineer, let’s understand what is Big Data.

What is Big Data?

Big Data is a collection of data sets from various sources. The volume and variety of data coming from different sources – social media, IoT, clickstream, etc – is extremely large and processing them with traditional ways is difficult.  Hadoop and related technologies solve this problem.

Now without further ado, let’s dive right into the skills that you will need to grab a job as a Big Data Engineer.

Skills required for Big Data Engineer

1. Hadoop

This is a prominent technology in Big Data. Companies are increasingly targeting distributed storage and processing architecture, which makes Hadoop their go-to choice. Those who are well-versed with Hadoop and are acquainted with components of Hadoop stack—MapReduce, Flume, Oozie, Hive Pig, HBase, Yarn – will be in high demand. 

2. Apache Spark

Apache Spark has grown tremendously in use in the past decade and its application doesn’t seem to slow down anytime soon in the near future. For large data processing apache spark is a unified analytical engine. It can be utilized for analytics of static as well as streaming data and works on all popular data sources including Hadoop, Kubernetes, HBase, etc.

3. No SQL

Moving away from legacy databases such as IBM D2, Oracle, etc. No SQL-based databases like MongoDB, Cassandra, and Couchbase are gaining prominence. Extensive scalability, distributed architecture, and reduced risk of failure lend these technologies an upper hand when it comes to databases. As companies are looking at scalable databases, these technologies are their go-to solutions. Hence, knowledge of these technologies is important.

4.  Deploying Cloud Clusters

Big Data relies heavily on networks. Elasticity offered by cloud servers facilitates makes data transmission easy. Given the vast amount of data available to enterprises, setting up cloud clusters eases this process. Not just this, data crunching is comparatively easier on the cloud. Hence, enterprises set up cloud-based on their requirements. Knowledge of how to set up cloud clusters comes handy to Big Data Engineers.

5. Statistical tools – R, SAS, Python, SPSS, or Matlab

Earlier, grads with quant or analytical background moved to finance or related industry. Thanks to Big Data, it has changed tremendously and now more of those folks are moving to Big Data. Their analytical mindset is put to use. However, after that skills, knowledge of R, SAS, Python is mandatory.

6. SQL

Though the Big Data community has come a long way from SQL and moved to No SQL. Still knowledge of SQL goes a long way in establishing a strong foothold in Big Data. New developments in Big Data such as Impala utilize SQL, and process Hadoop-stored data.

7. Machine learning (ML)

Recommendation systems are an essential tool in the growth of online businesses. Big Data Engineers use machine learning techniques to train their predictive models. Not that it is a mandatory skill, but Big Data Engineers with knowledge of machine learning command good salaries. In case you go for a Big Data certification, you will mostly find this missing. But an aware candidate with knowledge of industry requirements will always have a good grasp on SQL. This acts as a good sign, in case you ever want to tell a certified big data engineer from a non-certified one. 

Data visualization

Tools like Tableau and Qlikview are used to present data in a presentable format. Making sense of unstructured data is difficult as it is, these tools put in-coherent data in a more sensible format such as pie charts, histograms, etc. Often these tools are used after data analysis to present derived results.

Programming languages

Big Data has opened up opportunities for software developers as well. Their experience in programming with object-oriented languages come handy in Big Data roles. Those who are well-equipped with programming and adapted themselves with emerging analytics will pave way for better job opportunities in Big Data.


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