HomeAuthor InterviewsInterview with Sakti Mishra

Interview with Sakti Mishra

Sakti Mishra is the author of Simplify Big Data Analytics with Amazon EMR we got the chance to sit down with him and find out more about his experience of writing with Packt.

Q: What are your specialist tech area(s)?

Sakti: I have expertise in multiple industry domains and technologies such as Big Data, Analytics, Machine Learning, Artificial Intelligence, Relational/NoSQL/Graph Databases, Web/Mobile Application development and cloud technologies such as Amazon Web Services & Google Cloud Platform. 

Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?

Sakti: Writing a book was always there in my wish list but it always took a back seat because of other priorities. When the Packt team approached me with the opportunity, I gave it a thought and decided to go for it as the book topic is one of my expertise, and if I wish to write then why later and why not now.

Q: What kind of research did you do, and how long did you spend researching before beginning the book?

Sakti: Big Data Analytics is my core technical focus area and I am pretty confident in it. But I had to do some research and gave a lot of thought to compile all the topics before coming up with the chapters that might be useful for the readers.

Q: Did you face any challenges during the writing process? How did you overcome them?

Sakti: One of the primary challenges was to manage time to finish the book within the defined timeline. I had to stretch through late night and make weekends as working days to accommodate time for the book. The initial few chapters were very hectic but then I could come up to speed easily.

Q. What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?


Hadoop ecosystem services offered through Amazon EMR will be always in demand. If it was a single framework such as MapReduce or Spark then the chances of it getting replaced by another better framework was high. But EMR provides a bundle of different open source frameworks, which gives a complete ecosystem. EMR keeps its services up to date with the open source community releases and includes new components as per the customer demand, so it will grow with the open source releases. In addition, EMR has started offering a serverless version, which will increase its adoption.

Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?

Sakti: I have been working with the big data, analytics tech stack for 7-8 years and got years of working experience with different Hadoop/Spark providers such as Cloudera, Hortonworks, Amazon EMR, AWS Glue, and other AWS services. This book provides a step-by-step approach to make the readers learn about different EMR features, talks about different real-world use cases where EMR can be integrated, and provides a holistic view of the end-to-end data analytics pipeline, which makes it unique compared to any other book.

Q. What are the key takeaways you want readers to come away from the book with?

Sakti: Readers will first learn about the basics of EMR and understand use cases around its integration. Then they will dive deep into advanced topics related to EMR features and get step by step guide to implement the top 3 use cases where EMR is being used. Finally, they will learn about best practices and cost optimization techniques they can follow while implementing data analytics solutions.

Q. What advice would you give to readers learning tech? Do you have any top tips?

Sakti: First be clear with concepts, fundamentals, features and then start hands-on implementation. Also always try to relate the technical solution you are building with the real-world business problem you are trying to solve.

Q. Do you have a blog that readers can follow?

Sakti: The following are a few of the blogs I have written recently, which explain how you can implement data analytics solutions by integrating AWS services. 

Q. Can you share any blogs, websites and forums to help readers gain a holistic view of the tech they are learning?

Sakti: These two links should help gain more insights on Amazon EMR: https://aws.amazon.com/emr/ and https://aws.amazon.com/blogs/big-data/tag/amazon-emr/

Q. Do you belong to any tech community groups?

Sakti: Not directly but I actively follow Apache community and AWS/Google Cloud.

Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?

Sakti: Yes, I would recommend Packt to aspiring authors. Packt team is very competent and friendly to work with. They provide all necessary resources, editing, and review support needed to make the book valuable for readers.

Q. What are your favorite tech journals? How do you keep yourself up to date on tech?

Sakti: Medium, Google, TechCrunch, and CNN are a few of the journals or websites I follow to stay up to date with the tech industry.

Q. How did you organize, plan, and prioritize your work and write the book?

Sakti: Yes, there are primary job responsibilities and personal commitments that I cannot de-prioritize, so I had to stretch through late night and work over weekends to accommodate time. After writing the first chapter, I could easily find the average time it takes per page and used that as a reference to come up with a plan to finish a fixed number of pages per week and then a date by which I should complete the book. There were few deviations to the plan as other priorities came in and to compensate for the time loss, I have utilized most of my personal leaves to stay on track.

Q. What is that one writing tip that you found most crucial and would like to share with aspiring authors?

Sakti: Plan well before starting the book. Analyze and detail out the topics, number of chapters, average number of pages per chapter, and also how the flow would look like that can help your readers understand better.

You can find Sakti’s book on Amazon by following this link: Please click here

Simplify Big Data Analytics with Amazon EMR on Amazon.com