Big Data Engineers: Myths vs. Realities

The data present with the organisations is increasing with every passing minute. This data is in varied formats, sizes, and types, and is thus extremely difficult to study, let alone analyse efficiently. To help with that, there are Big Data Engineers! These are the people who are responsible for converting the useless Big Data into useful Big Data which can then be further studied and analysed by data scientists.

Big Data Engineers can be rightly called as a mix between data scientist and an engineer. Any organisation dealing with big data by default needs a Big Data Engineer.

Typically, the role of a Big Data Engineer requires them performing one (or more) of the following skills :

Data Analysis

• Hadoop, MapReduce, IBM Biginsights, Hortonworks, and MapR are some of the tools Big Data Engineers are expected to have a command over to perform data analysis. Most engineers tend to have experience with just MapReduce (since it’s the oldest; and others are quite new), but the underlying algorithms make it easy to learn new technologies quickly and efficiently.
• Data mining is one of the essential aspects of Data analysis. Big Data Engineers work on technologies like Mahout to carry out the jobs related to Data Mining. The Big Data Engineer’s first responsibility is to scrounge for data – even before he can clean it. So, they need to be proficient with Mahout or other data mining tools.
• Statistical analysis also plays a significant role, and a Big Data Engineer is expected to have some command over R, SPSS, SAS, and MATLAB, etc.
• Big Data Engineers are at the end of the day engineers. They need to be well-versed with the fundamentals of programming. Most of the strong programming skills will be required only for custom/specialised implementations of algorithms.

Data Warehousing

• Data warehousing refers to hoisting the data onto a warehouse. For that, a big data engineer is expected to have a working knowledge of either of MySQL, MS SQL Server, Oracle, or any relational databases. These tools allow the prominent big data engineers to tackle the relational data present with their organisation seamlessly.
• Today, not all data is structured and relational. Most of the data with these organisations are non-relational. Hence, a knowledge of non-relational databases like NoSQL, HBase, HDFS, Cassandra, CouchDB, etc. also comes in quite handy for a big data engineer.  

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Data Collection

• Data collection forms one of the core tasks of a Big Data Engineer. They need to work with Data APIs, ex. RESTful interfaces, to fetch data from the data warehouse. For this, they need to be hands-on with some scripting language.
• Further, Big Data Engineers need to be experts in SQL and data modelling. This comes in extremely handy while collecting the data. Data modelling allows the big data engineers to have a clear sight of the data and its interdependencies.

Data Transformation and Cleaning

• Once the data has been collected, now the primary responsibility of a Big Data Engineer is to transform it into a format suitable for the data scientist. For that comes various ETL Tools like Informatica, DataStage, Redpoint, and SSIS. Proficiency in any one of these tools allows Big Data Engineers to transform the data that they collected earlier efficiently.
• Once the data is transformed, it is cleaned of all the anomalies and inconsistency. It is important because this data is further going to be analysed by a Data Scientist and his analysis will only be as good as the data he gets.

Big Data Engineering is a comparatively newer field with increasing opportunities every passing day. A Big Data engineer is the master of the skills we discussed earlier. However, not all Big Data Engineers know all of these skills. Every role is different, so some may require more specialised knowledge in one of these areas over the others. However, for an expert in one of these skills, it’s not usually too challenging to translate those skills to the other areas. Now we are on the same page regarding the responsibilities and tasks of a Big Data Engineer.

Let’s take a step further and bust some prevalent myths about their lives, jobs, and qualifications:

Myth #1: There is not much difference between a regular day of a data scientists and a big data engineer.

If you have been following our series, you’ll know better. A data scientist is someone who looks for trends, meanings, and patterns in a data and tries to formulate actionable insights that improve an organisation’s functioning. A Big Data Engineer, on the other hand, quite evidently, works with data before it is analysed. He is responsible for cleaning the data and presenting it to the data scientist in as pristine a form as possible.

Myth #2: Big Data engineers are much more valuable than data scientists (or vice-versa).

Both of these job roles have their own importance for an organisation’s functioning. Without an efficient Big Data engineer, a data scientist will have a hard time delivering good results. Similarly, without an expert Data Scientist, the organisation will never know what to make of their data. So, we just can not order these job roles on the basis of their importance, as at the end of the day, both of these profiles form the pillars of any successful data science team.

Myth #3: Big Data Engineers are only required in large businesses.

Like we said earlier, if your organisation deals with Big Data, you need a Big Data Engineer. Today, any organisation, however big or small, has terabytes of customers data. There is no company, irrespective of their domain, that can’t improve its functions by making sense of their Big Data. As the tools and technologies surrounding Big Data are becoming cheaper and more accessible, more and more SMEs are taking the Big Data route and appointing Big Data Engineers and Scientists to help them stay ahead of the curve.

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Myth #4: A Big Data Engineer needs to be an expert programmer.

More than core programming, a Big Data Engineer needs to be an expert in managing data. More often than not, you’ll find Big Data Engineers working with a library or a framework that fits their case. These come ready-made and do most of the heavy lifting programming. It’s still recommended that a Big Data engineer has a clear understanding of the underlying fundamentals of programming. This will help them tweak/modify any algorithm/framework/library depending on their particular use-case. Also, some knowledge of scripting language is a must as these big data engineers are responsible for fetching the data from the warehouses and cleaning it which requires writing scripts.  

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Myth #5: Big Data engineers are required only in tech companies

Today, organisations use data for everything including targeting their customers better. A detailed insight into their customer data allows any organisation to lay out a successful marketing campaign. Big Data Engineers are required by organisations both tech and non-tech. Just about any organisation can become better and more efficient at their job if they have access to the right data.
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Wrapping up

With that, we come to the end of our myth busters for today. Stay tuned, and we’ll be back with more such Mythbusters. Do let us know if you’ve come across any more such myths that need busting!

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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