All About Data Engineers And Tools They Use

Data Engineers

What Does A Data Engineer Do

  • designs develop and maintain architecture for working with big data;
  • configures the collection of data from disparate sources into a single repository;
  • checks the data for correctness and discards incomplete or erroneous data;
  • brings raw data to a form suitable for further processing and analysis;
  • creates pipelines for loading and processing data;
  • I am looking for new opportunities to improve data collection and processing.

What You Need To Know And What Tools To Use

  • Algorithms and data structures: This knowledge is needed to understand how data is stored and how best to extract, process, and store it.
  • SQL: Almost any relational DBMS works with SQL, so a data engineer needs to know this language to retrieve and process data.
  • Python, Java/Scala: Python is considered one of the most suitable languages ​​for data processing, so a data engineer cannot do without knowledge of it. Additionally, Java or Scala comes in handy because most data manipulation tools are written in these languages.
  • Tools for working with big data: There are several popular frameworks and tools for working with big data: Spark, Hadoop, Kafka, and others. Companies can use different tools, so a data engineer may not know all the tools in depth, but he must be able to work with at least one and understand what the rest are for.
  • Pipelines for data processing: A data engineer does most of the data processing work not manually but with the help of pipelines. These automated conveyors do all the routine work for a data engineer: they load data, check it, clean it, and transfer it to another structure.
  • Distributed systems: Companies generate a huge amount of data, so it’s inefficient to handle everything on one server. Now almost all systems operate in a distributed mode; they process a large amount of data in parallel on several servers. A data engineer must be able to create and maintain such distributed systems.
  • Cloud platforms: Now many companies are transferring their infrastructure to the clouds, so a data engineer must be able to work with them. There are several cloud platforms, and each specific company works with a specific provider. A data engineer must be able to work with at least one cloud platform, and know-how cloud architecture differs from on-premise. In addition, he must understand how to choose a provider and choose the optimal architecture for business tasks.

Also Read: Top Data Science And Machine Learning Certification Courses In 2022

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