Saurabh Rana

Prerequisite is Python as typically Python has been the programming language used for Machine learning. If done using Spark ML, then it can be done using Scala too. It requires some mathematics and statistics too but I think not that much for beginner level.

Dave Tolls wrote:

Saurabh Rana wrote: In case of working from home, literally one is always 'ON'.

That requires you to create a separation between the two environments.
If you're working from the kitchen table then yes, possibly. Even then, I would expect once the laptop has been put away that it would revert to being a kitchen.

I've ensured, this past 15-20 years or so, that I have an office set up at home. That keeps things separate.

Useful advice. Thank You.

I would share my experience for WFH. It comes with pros and cons.

Pros

1) You definitely are going to save travel time, lesser for some and more for some but overall everyone would agree that they save some travel time. This time can be utilized for other things which can be self learning or any activity. You are likely to have more time in the day and one can think of spending time for some other activity which one may not have been able to devote time for earlier.
2) The time one is close to your family would be surely more. One would be working and not talking to them but still it gives satisfaction if you are near them. One can have lunch together.
3) One can prepare our own tea/coffee while taking a break instead of having whatever is served by the office coffee machine.

Cons
1) In addition to what we learn by yourself and by working , we learn a lot by interacting with other employees too. Although we would still interact using skype etc, actual interactions would be missed.
2) When working from office, once we come home there would be better separation between work and life. In case of working from home, literally one is always 'ON'. Depends on whether you prefer work life balance or work life blending.

List would be different for everyone but there would be some pros and cons. We have to make the best use of this changed situation.

AWS and Azure are two of the most common cloud providers. The cloud fundamentals will be same. You can choose one of them and learn for e.g AWS. You can start by understanding what is cloud and why is it required. You will come across that the most common services offered by any cloud are IAAS and PAAS (and also SAAS). You can understand the differences between these. After learning IAAS (simply the raw servers provided with OS ), you can learn about the numerous PAAS components offered in cloud. You can learn about Serverless programming and how it is done using lambda functions / azure functions. You can learn about the persistence services provided like RDS/Azure SQL, CosmosDB/DynamoDB. You can learn about the S3 bucket/Blob Storage. You can learn about deploying your applications using Elastic Bean Stack/ App Service. You can learn about queue service like SQS /Storage queue and others. This can be a good start. Later, you may want to have a look at Iot Hub/Azure IoT and Stream Analytics/Kinesis Analytics and others.

You can first learn what is Big Data and why does it require different kind of handling. You will come across the V's of big data. You will come across some of the most common tools in Big Data world. You will learn that for Big Data the processing can be batch processing or Realtime processing. For the former, you may use map reduce or Spark batch and for the latter you may use Spark Streaming or Strom. You would learn that Map Reduce is part of Hadoop which includes tools like map reduce, hive, hbase, flume, oozie and others. You will learn that persisting Big Data requires nosql databases like hbase, cassandra , mongodb, cosmosdb etc. You will learn that the first two are the columnar databases while the last 2 are key value stores. You can make applications and learn in depth, some of the technologies mentioned here and other big data technologies. This can be a good start.

You can begin with either of Spring or Struts or JSF. You can make some application while learning it.Some interviewers get impressed seeing a number of frameworks in the resume.In my opinion if a resource knows just 1 framework to begin with and is really good in it, its not a big deal to make him learn any other framework required in the job but if the resource has all the frameworks in his resume and is not good in either of those that is not good.

Why would someone want to avoid Finally block Execution. But if you just want to know a case when finally block would not be executed , then a System.exit would cause it.

No way should it cause an issue....sometimes the current company notice period is say 3 months and they decide to leave you after 2 months..then one can prepone the joining in the new company or take a break during that 1 month.Only thing is that you have to be without salary for few days..but worth spending time with family..

Is this designation/salary package relevant to my experience?

Well in this industry you will see lot of variation on this aspect.There will be people with lesser experience and earning more and there will be a lot of people with more experience than you have and earning lesser.Since you asked ,being a developer from India(Pune) I can say your package looks good but decision depends upon what (if any) other offers you have in hand and taking a decision which you feel will be good decision in long run(based on lot of factors and priorities not necessarily money).

How many rows are you trying to process? What are the details of your cluster?how many nodes?

It is a very normal feedback after interviews. Just think about what you can offer from your side. You can keep updating your skills and do hard work from your side, the result will be good.You might meet failure in some interview like this case but ultimately if you are good you will get selected in next interviews.All the best.