Data Science Tales

   Data is a new oil. To channelize this data we need proper pipelines. These pipelines are mostly called ETL or data pipelines.  There are various tools out there to work out these Extract, Transform, and Load (ETL) operations.  Some of the scripting tools are:  - Python - SQL and NoSQL Apache provides a wide range of products that can be used as Data Engineering tools.  Some of the amazing apache DE services comprised of; - Hadoop - Spark - Kafka - Cassandra - Hive In addition, some cool DE tools are: - Tableau - Talend - MapReduce These DE tools provide a way to manage the data pipelines in a more effective, efficient, and better way.  Almost every Data-Oriented company in the corporate sector is highly dependent upon these technologies. They are leveraging many of the above-mentioned mechanisms for promising Data Services and Architecture. That's it for today. Hope you have enjoyed this article. If you want to know more about data or software related concepts, let me know in th

In a previous blog post about data engineering, we went through several many concepts related to data engineering such as data modeling, and then we went through different types of databases, for example, relational databases and we also got a glimpse of relational as well as NoSQL databases. And in today's blog post we will be writing about data engineer applications. Which are basically the use cases of data engineering 1st let's see what data engineering applications mean; so basically when we have data and we have engineered it then there are several things that we can do with that data and these things that we can do with processed data are the use cases of the data engineering. For example: If we have filled our containers with the data then it's not of any good use if it is not clean and well-formed. If your data is well-formed and if there are no logical mistakes, missing values, and garbage then we can easily perform the use cases and applications of data enginee