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ETL developer vs Data engineer

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Rephrased question : ETL developer vs Data engineer Answer : Unfortunately there are no strict industry standards on these job titles. That is just one part of it. Before ETL tools such as DataStage, Informatica, Ab Initio, etc., became popular, developers were hand coding every ETL flow. These ETL tools shortened the ETL flow development time to a great extent and allowed ETL developers to focus on business rule/logic/requirement (what to implement) than how to code it or optimize the code. There are many other benefits of using a tool but I won’t go into that in this answer. So an ETL developer with experience in these tools without any programming (coding) experience was/is able to design and develop end to end data flows. Whenever new types of source/target data format comes up, these tools catch up but it takes time, i.e., the ETL tool provider (e.g. Microsoft, IBM, etc.,) adds new components/connectors within the ETL tool to be able to work with new data format. For example, let’
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3 years of IBI

This week (on 19th Feb 2020) I completed 3 years of IBI. To celebrate it I have created a Dashboard in Google Data Studio. So from now on instead of using the charts in the Google Sheets I will use the Dashboard. Google Data Studio is more user friendly and mobile friendly too, also it comes with easy to use filters and drill down/up capabilities. PDF version of the first 2 pages of the shareable dashboard is attached below. I am happy to share the first 2 pages of the IBI Dashboard, reach out to me if you would like to get the Google Data Studio IBI dashboard. Once you get the dashboard (without my data of course) you can make a copy of it and point it to your IBI data (assuming you have started), add more charts, pages, etc., and use it for your benefit. Like with every year, this IBI year too I have learnt a lot about myself, made new mistakes, changed several things about me. The benefits of IBI is much more than we can think of. Use it to know it. And what are some of the
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Businesses should classify data not IT

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Currently what we see is that tools and technology limitations are used as a basis for classifying data, and even worse is that the classification is in itself incorrect. The so-called big data is so wrongly named. I have already explained why the naming is incorrect in another article - Why big data is actually small?   Wouldn't it be much simpler, better, more meaningful and a standardized approach to classify data into primary and secondary data instead of using misnamed, meaningless and non-standard terms such as small data and big data?  Primary data is the essential or core data without which the business cannot function. For example, a purchase transaction in a retail store has to be captured and stored for billing, payment, compliance,  etc. These are mandatory requirements. Business Intelligence (BI) on top of that data is not mandatory but very useful, but that is not the main purpose of storing that core data in the first place. Secondary data is all of that data which e
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