Interesting Careers in Big Data

Big Data & Data analytics has opened a wide range of new & interesting career opportunities. There is an urgent need for Big Data professionals in organizations.

Not all these careers are new, and many of them are remapping or enhancements of older job functions. For example, a Statistician was formerly deployed mostly in government organization or in sales/manufacturing for sales forecast for financial analysis, statisticians today have become center of business operations. Similarly, Business analysts have become key for data analytics – as business analysts play a critical role of understanding business processes and identifying solutions.


Here are 12 interesting & fast growing careers in Big Data.

1. Big Data Engineer
Architect, Build & maintain IT systems for storing & analyzing big data. They are responsible for designing a Hadoop cluster used for data analytics. These engineers need to have a good understanding of computer architectures and develop complex IT systems which are needed to run analytics.

2. Data Engineer
Data engineers understand the source, volume and destination of data, and have to build solutions to handle this volume of data. This could include setting up databases for handling structured data, setting up data lakes for unstructured data, securing all the data, and managing data throughout its lifecycle.

3. Data Scientist
Data Scientist is relatively a new role. They are primarily mathematicians who can build complex models, from which one extract meaningful analysis.

4. Statistician
Statisticians are masters in crunching structured numerical data & developing models that can test business assumptions, enhance business decisions and make predictions.

5. Business Analyst
Business analysts are the conduits between big data team and businesses. They understand business processes, understand business requirements, and identify solutions to help businesses. Business analysts work with data scientists, analytics solution architects and businesses to create a common understanding of the problem and the proposed solution.

6. AI/ML Scientist
This is relatively a new role in data analytics. Historically, this was part of large government R&D programs and today, AI/ML scientists are becoming the rock stars of data analytics.

7. Analytics Solution Architects
Solution architects are the programmers who develop software solutions – which leads to automation and reports for faster/better decisions.

8. BI Specialist
BI Specialists understand data warehouses, structured data and create reporting solutions. They also work with business to evangelize BI solutions within organizations.

9. Data Visualization Specialist
This is a relatively new career. Big data presents a big challenge in terms of how to make sense of this vast data. Data visualization specialists have the skills to convert large amounts of data into simple charts & diagrams – to visualize various aspects of business. This helps business leaders to understand what’s happening in real time and take better/faster decisions.

10. AI/ML Engineer
These are elite programmers who can build AI/ML software – based on algorithms developed by AI/ML scientists. In addition, AI/ML engineers also need to monitor AL solutions for the output & decisions done by AI systems and take corrective actions when needed.

11. BI Engineer
BI Engineers build, deploy, & maintain data warehouse solutions, manage structured data through its lifecycle and develop BI reporting solutions as needed.

12. Analytics Manager
This is relatively a new role created to help business leaders understand and use data analytics, AI/ML solutions. Analytics Managers work with business leaders to smoothen solution deployment and act as liaison between business and analytics team throughout the solution lifecycle.

5 Pillars of Data Management for Data Analytics

Basic Data Management Principles for Data Analytics

Data is the lifeblood for Big data analytics and all the AI/ML solutions built on top.

Here are 5 basic data management principles that must never be broken.

1. Secure Data at Rest

• Most of the data is stored in storage systems which must be secured.
• All data in storage must be encrypted  

2. Fast & Secure Data Access 

• Fast access to data from databases, storage systems. This implies using fast storage servers and FC SAN networks.  
• Strong access control & authentication is essential

3. Manage Networks for Data in Transit

• This involves building fast networks - a 40Gb Ethernet for compute clusters and 100Gb FC SAN networks
• Fast SD-WAN technologies ensure that globally distributed data can be used for data analytics.

4. Secure IoT Data Stream

• IoT endpoints are often in remote locations and have to be secured.
• Corrupt data from IoT will break Analytics.
• Having Intelligent Edge helps in preprocessing IoT data - for data quality & security

5. Rock Solid Data backup and recovery

• Accidents & Disasters do happen. Protect from data loss & data unavailability with a rock solid data backup solutions.
• Robust disaster recovery solutions can give zero RTO/RPO.

Skills Needed To Be A Successful Data Scientist

Data Scientist, the most demanded job of 21st century, requires multidisciplinary skills – mix of Math, Statistics, Computer Science, Communication & Business Acumen.

Data Life Cycle Management in the Age of Big Data

Organizations are eager to harness the power of big data. Big data creates tremendous opportunities and challenges. 

The data lifecycle stretches through multiple phases as data is created, used, shared, updated, stored and eventually archived or defensively disposed. Data lifecycle management plays an especially key role in three of these phases of data’s existence:

1. Disclose Data
2. Manipulate Data
3. Consume Data

Organizations can benefit from data only if they can manage the entire data lifecycle, focus on good governance, use, share and monetize data.

Advantages of Couchbase

On opensource  NoSQL database that provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. It is optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data.