How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data Extraction and ETL developer with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
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Data Extraction and ETL
What is ETL?
Extract, transform, load (ETL) is a tried-and-tested data integration process for collecting data from different source systems, cleaning and processing it, and loading it into a data warehouse or data lake (or other downstream ‘target’ data store). By the mid-1970s, as these databases gained popularity, the term ETL was used to define a process to extract, transform and load data for computation and analysis in order to become the primary way to prepare data for data warehouse projects.
ETL supports the primary workstreams in data analytics and machine learning through a series of business rules, which prep the data into a form that fits the specific need, whether it’s a monthly reporting requirement or more advanced analytics that can help optimise back-end processes or end-user experiences.
An organisation frequently uses ETL to:
- Extract data from legacy systems Cleanse the data to improve data quality and establish consistency
- Load data into a target database