Do you know the skills and steps required to successfully transition to a Big Data career?
If you’re someone who doesn’t belong to the Big Data Industry yet but has a background which may have links to it – you may be thinking about a lucrative and long-term Big Data career.
If you’re aspiring to be a Big Data Engineer or a Team Lead/Tech Lead or even a Project Manager/Architect, there are some key technical skills required by employers in the Big Data Ecosystem. These skills vary for different Big Data Roles.
In this article, we will discuss the technical skills required by employers for different Big Data profiles. We’ll also discuss organisational expectations from different hierarchical levels and steps to make a successful Big Data career transition.
Essential Skills
Here are the essential skills needed for making a successful Big Data career transition:
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Distributed Computing Big Data Environments
- You should have hands-on skills in at least one of the many Hadoop Distributions (viz. Hortonworks, Cloudera, MapR, IBM Infosphere BigInsights). At this point in time, Cloudera distribution is the most deployed distribution.
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Cloud Data Warehouses
- Since there is an increased affinity towards moving from on-premise data warehousing solutions to cloud-based data warehousing solutions, you should have skills in technologies like Amazon Redshift or Snowflake. Redshift is a fully managed cloud-based petabyte-scale data warehousing solution.
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NoSQL & NewSQL
- You should have skills in some of the new emerging NoSQL technologies. For e.g. MongoDB (which is a document database) or Couchbase (which is a key-value store). Others like Cassandra and HBase are also popular. On the cloud, Amazon has specific databases like DynamoDB and SimpleDB (both key-value pair stores).
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Data Integration & Visualisation
- As you work on large-scale analytics projects, you will be ingesting data from multiple sources. Keeping this in mind, you should have knowledge of Big Data compliant integration technologies like Flume, Sqoop, Storm Kafka etc. Data Integration products like Informatica and Talend have also upgraded their capabilities to Big Data processing. In the world of visualisation, Tableau and QlikView are popular. They also integrate with other BI (business intelligence) reporting data stores.
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Business Intelligence (BI)
- Hands-on knowledge of Business Intelligence technologies is also helpful. There are several technologies available in BI. For e.g. IBM, Oracle and SAP have acquired BI suites. Microsoft’s BI stack is largely organically developed. Others like Microstrategy and SAS are also independent BI providers.
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Big Data Testing
- Big Data Testing is fundamentally different from traditional ETL and application testing because of the volume of data involved. The differences in test scenarios occur due to the velocity and variety of data. Also, in certain cases, execution of test cases requires scripting and programming skills (Pig scripts, Hive query language etc.).
Organisational Expectations and Hierarchical Responsibilities
An organisation has different expectations from different levels of the workforce:
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Young Professionals (less than 5 years of overall experience)
People in this age group mostly work as Big Data Engineers. As a Big Data Engineer, you are expected to be conversant with the above-mentioned technologies in the form of hands-on skills. As engineers, you would be responsible for building, testing and deploying the Big Data solutions.
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Mid-Career Professionals (5 to 10 years overall experience)
People in this age group work as a team or tech leads. As a leader too, you are expected to be conversant in the above-mentioned technologies but will also be responsible for taking design decisions, conducting regular checkpoint reviews of the deliverables and providing overall technical guidance to the developers.
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Senior Professionals (overall experience of more than 10 years)
- Enterprise Architects: Enterprise architects are expected to be familiar with the above-mentioned technologies along with having a holistic view of the Big Data Landscape. As an architect, you are expected to be trusted partners of the clients, advising them on the right architecture, transformation strategy and roadmap, tool selection and vendor evaluation.
- Project Managers: For a PM, managing a Big Data project team requires cross-functional team management skills – data warehousing teams, Business Intelligence teams, statisticians, domain experts and data teams. Knowledge management is another key skill. It is important to understand and plug knowledge gaps in the team. Further, a Big Data PM is expected to understand Agile methodologies to deliver the projects.
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Transitioning to Big Data
The best way to make a Big Data career transition is by acquiring the relevant skills and then applying them in case studies/projects that simulate real-life scenarios. These could be part of a training program/education program, or through shadowing in-flight projects (or Proof of Concepts – PoCs) in existing organisations, wherever possible.
The following is a breakdown of the kind of activities practitioners can do in these case studies, according to the experience levels.
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Young Professional (less than 5 years of overall experience)
You should be looking to acquire the skills through training programs/PoCs and then apply them to projects that simulate real-life scenarios.
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Mid Career Professional (5 to 10 years overall experience)
You should drive technology solution discussions, coming up with designs and conducting reviews of work products and guiding teams during the case studies.
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Senior Professionals (overall experience of more than 10 years)
You should be the one who kick-starts the execution of the case studies, acquiring a clear understanding of functional requirements, developing the solution strategy to meet project requirements within stipulated timelines and developing the project charter (PM roles) and overall technology solution (Architect roles).
This takes us to the question:
In-Demand Software Development Skills
What should you look for in a good Big Data Program or Course?
The course should provide the right enablers for the participants to complete a Big Data career transition into these roles.
The following are the 3 key expectations you should have of any course:
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Technical skills:
The course should impart the above-mentioned skills through a suitably designed curriculum.
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Cloud platform:
You should get access to a cloud platform with the relevant software and experiment with it.
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Case studies/Projects:
The course should have a simulation of real-life scenarios as explained above, where participants in the various categories can play out the roles as explained above.
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