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Home»Data»Advanced ETL Techniques for Beginners | 💡Written by Mike Shakhomirov | February 2024
Data

Advanced ETL Techniques for Beginners | 💡Written by Mike Shakhomirov | February 2024

5gantennas.orgBy 5gantennas.orgFebruary 4, 2024No Comments2 Mins Read
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On a scale of 1 to 10, how good are your data ingestion skills?

💡Mike Shakomirov
Towards data science
Photo by Blake Connally on Unsplash

Data ingestion is a critical step in data engineering. Data engineers load large amounts of data into various database systems for further transformation and processing. While you may be lucky to never run out of memory while processing relatively small amounts of data on staging, working with production data pipelines containing terabytes (or petabytes) of records often requires It will be a big challenge. Existing ETL solutions provide automatic data loading into the required data warehouse and often employ a row-based pricing model. In this story, I would like to explain how to create a bespoke data loading solution for your pipelines that enables efficient data loading. Let’s take a closer look at common data ingestion design patterns and common ways to organize processes. Reverse engineer some of the most popular ETL solutions to see how to efficiently ingest data without any outages or losses. To summarize our findings, we provide an example of data loading using Python libraries and tools that are freely available on the market.

How good are your data reading skills on a scale of 1 to 10? –

This is one of my favorite data engineering interview questions. I continue to look for people who know how to build a bespoke ETL system for her.

In fact, in my opinion, experience shows that it is possible to create a robust data loading system that can process data efficiently, does not fail, does not consume large amounts of memory, and handles various data formats and scales well. This is the hallmark of a rich data engineer. . Fortunately, this isn’t really necessary, as there are plenty of tools on the market for ETL tasks. Until the company decided to build this in-house. There can be many reasons for this, but one obvious one is: Security and Regulation. Handling sensitive data is always difficult, and data is often must not Leaving a particular region and/or geographic location. Another good reason to develop ETL expertise in-house is that it can save you a lot of money in the long run. It’s always great to have a well-rounded software engineer with experience in data platform design and familiarity with many of his ETL tools and frameworks. Companies are looking for such people. I…



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