How to make your SQL upskilling more effective, through ready-to-go leader databases (free). My goal is to share with you some ways to upskill SQL / PL-SQL / T-SQL skills.
“Upskilling” in any Data topic means 2 complementary things: learning new skills (languages, algorithms, etc.), and keeping up-to-date the skills you have already built.
In this story, my goal is to share with you some ways to upskill SQL / PL-SQL / T-SQL skills.
Any Data Science discipline, or any job title containing the “..Data..” word, requires at some point to query a database and to do some developments. Actually, any job interview I ever did (either as an interviewer or interviewee) tested the candidate’s SQL expertise, among other things.
But here comes the problem: once we get the job, we tend to use only the little part of our skillset which necessary to deal with the company’s data ecosystem, and eventually to fulfill our boss’s expectations. I admit it: sometimes it happened to me to become “lazy” while writing queries or doing database developments, just because the databases and tools allowed me to choose dirty ways to do things. A remarkable example: once I worked on a churn analysis project, in which a well-tuned Teradata cluster was used to process relatively small volumes of telco transactions. It could run any dirty analytical query in just milliseconds.
On the other hand, when the database is poorly configured, with too big volumes, or not properly tuned, we have to find the smart ways of building queries or developing on it. Querying needs a perfectly optimized code, and development requires to use the proper objects (right types of indexes, partitions, stats collection, etc..). Also, in such conditions we need to deeply understand the platforms we are dealing with: the tricks that work best on MS-SQL Server might be different than in Oracle, etc.
I am pretty sure that you met some crappy DB as well, right? So the best we can do to find smart solutions in any context is trying to constantly upskill our Database and SQL knowledge.
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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