{"id":2257,"date":"2020-02-13T04:03:36","date_gmt":"2020-02-13T01:03:36","guid":{"rendered":"http:\/\/kusuaks7\/?p=1862"},"modified":"2024-01-10T19:16:11","modified_gmt":"2024-01-10T19:16:11","slug":"most-in-demand-tech-skills-for-data-engineers-58f4c1ca25ab","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/most-in-demand-tech-skills-for-data-engineers-58f4c1ca25ab\/","title":{"rendered":"Most In Demand Tech Skills for Data Engineers"},"content":{"rendered":"\t\t
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How do they compare to data scientist tech skills?<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data Engineer\u00a0<\/em>is the fastest growing job title according to a\u00a02019 analysis<\/a>. Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Which tech skills are most in demand for data engineers? How do they compare to the most in demand tech skills for data scientists? Read on to find out!<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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I analyzed job listings for data engineers in January 2020 to see which technology skills are most in demand. I compared the results to data scientist job listings and uncovered some interesting differences.<\/p>\n

Without further ado, here are the top 10 technologies from data engineer job listings as of January 2020.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Let\u2019s jump in!<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Data Engineer Role<\/h1><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data engineers have a vital role to play in today\u2019s organizations.\u00a0They are responsible for storing and making data usable by others.<\/span><\/mark><\/p>\n

Data engineers set up pipelines to injest streaming and batch data from many sources. Then the pipelines perform extract, transform, and load (ETL) processes to make the data more usable. The data is then made available to data scientists and data analysts for further processing. Eventually the data finds its way into dashboards, reports, and machine learning models.<\/p>\n

I searched for data to determine which technologies are most in demand for data engineers in 2020.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Method<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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I scraped information from\u00a0SimplyHired<\/a>,\u00a0Indeed<\/a>, and\u00a0Monster<\/a>, to see which keywords appeared with \u201cData Engineer\u201d in job listings in the United States. I used the\u00a0Requests<\/a>\u00a0and\u00a0Beautiful Soup<\/a>\u00a0Python libraries.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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I included keywords from my analysis of data scientist job listings and from reading data engineer job listings. LinkedIn was not searched due to previously locking me out of my account after scraping.<\/p>\n

For each job search website, I calculated the percentage of total data engineer job listings for that site that each keyword appeared in. Then I averaged those percentages across the three sites for each keyword.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Results<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Here are the thirty highest scoring data engineer technology terms from the job listing search results.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Below is the same percentage data in tabular form.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Let\u2019s break this down.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Discussion<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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SQL and Python both appear in over two-thirds of job listings. They are the top two technologies to know.<\/p>\n

Python<\/a>\u00a0is a very popular programming language for working with data, websites, and scripting.<\/p>\n

SQL<\/a>\u00a0stands for Structured Query Language. SQL is a standard implemented by a family of languages and is used for getting data out of relational databases. It has been around for ages and has shown its resiliency.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Spark appears in about half of all listings.\u00a0Apache Spark<\/a>\u00a0is \u201ca unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing\u201d. It\u2019s particularly popular with really big datasets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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AWS is in about 45% of listings. AWS is Amazon\u2019s cloud computing platform. It has the largest marketshare of any cloud platform.<\/p>\n

Then come Java and Hadoop, each in just over 40% of listings.<\/p>\n

Java<\/a>\u00a0is a commonly used, battle-tested language that was the 10th most dreaded in\u00a0Stack Overflow\u2019s 2019 Developer Survey<\/a>. In contrast, Python was the second most loved language. Oracle controls Java and this website home page, from January 2020, tells you all you need to know about it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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It\u2019s like a time machine.<\/p>\n

Apache Hadoop<\/a>\u00a0uses the MapReduce programming model with sever clusters for big data. The MapReduce model is falling out of favor.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Then come Hive, Scala, Kafka, and NoSQL, each in about a quarter of data engineer listings.<\/p>\n

Apache Hive<\/a>\u00a0is data warehouse software that \u201cfacilitates reading, writing, and managing large datasets residing in distributed storage using SQL\u201d.<\/p>\n

Scala<\/a>\u00a0is programming language popular with big data. Spark was built with Scala. Scala is the 11th most dreaded language in Stack Overflow\u2019s 2019 Developer Survey results.<\/p>\n

Apache Kafka<\/a>\u00a0is a distributed streaming platform. It\u2019s very popular for injesting streaming data.<\/p>\n

NoSQL databases<\/a>\u00a0stand in opposition to SQL. NoSQL databases are non-relational, unstructured, and horizontally scalable. NoSQL is quite popular, but previous hype of it displacing SQL as the dominant storage paradigm seems to overblown.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Comparison with Data Scientist Terms<\/h1>\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Here are top 30 data scientist job listing technology terms, arrived at through the same methodology as the data engineer terms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In terms of total listings, there were about 28% more data scientist listings than data engineer listings (12,013 vs. 9,396).<\/p>\n

Let\u2019s see which terms were more common in data engineer listings than data scientist listings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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More common for data engineers<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The chart below shows the keywords with average differences greater than 10% and less than -10%.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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AWS had the largest increase, appearing in about 25% more listings for data engineers than data scientists. It showed up in about 45% of data engineer listings and about 20% of data scientist listings. That\u2019s quite a difference!<\/p>\n

Here\u2019s another look at the same data that shows the results for data engineer and data scientist job listings side by side:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Spark showed the second largest increase. Data engineers are often dealing with big data.<\/p>\n

Kafka<\/a>\u00a0saw an increase of 20%, too. That\u2019s about four times the percentage data scientist listings. Injesting data is a core job for data engineers.<\/p>\n

Java, NoSQL, Redshift, SQL, and Hadoop appeared in about 15% more data engineer listings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Less common for data engineers<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Now let\u2019s look at which skills are less popular in data engineer job listings.<\/p>\n

R<\/a>\u00a0saw the largest drop from data scientist to data engineer listings. It was in about 17% of listings, instead of about 56%. Wow. R is a programming language popular with academics and statisticians. It was Stack Overflow Survey respondent\u2019s 8th most dreaded language.<\/p>\n

SAS<\/a>\u00a0is also much less common in data engineer listings, with a difference of about 14%. SAS is a proprietary language for statistics and data. Interestingly, my recent analysis of\u00a0data scientist job listings\u00a0<\/a>showed that SAS fell more than any other technology.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Important for both data engineers and data scientists<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It\u2019s worth noting that eight of the top ten technologies were shared between data scientist and data engineer job listings. SQL, Python, Spark, AWS, Java, Hadoop, Hive, and Scala were on both top 10 lists.<\/p>\n

Here are the 15 most common data engineer terms, along with their prevalence in data scientist listings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Advice<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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If you want to be a data engineer, I suggest you learn the following technologies, roughly in order of priority.<\/p>\n

Learn SQL. I suggest you learn PostgreSQL because it\u2019s open source, popular, and growing. My Memorable SQL book shows you how to use PostgreSQL and is available in pre-release\u00a0here<\/a>.<\/p>\n

Learn vanilla Python. My Memorable Python book is designed for Python newbies. It\u2019s available for Kindle and hard copy from\u00a0Amazon<\/a>\u00a0and in .epub and .pdf form\u00a0here<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Once you know basic Python, learn pandas, a Python library for cleaning and manipulating data. If you are looking for a data job that requires Python, and most do, you can expect the organization is expecting you to have pandas skills, too. I\u2019m finishing up an introductory pandas book, so join my\u00a0Data Awesome newsletter<\/a>\u00a0to make sure you don\u2019t miss it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Pandas snacking<\/p>\n

Learn AWS. If you want to be a data engineer, you need a cloud platform under your belt and AWS is the most popular. I found\u00a0Linux Academy<\/a>\u00a0online courses helpful when learning\u00a0Google Cloud Data Engineering<\/a>\u00a0skills, and expect they would be helpful for AWS.<\/p>\n

If you know all those technologies and want to become more in demand as a data engineer, I suggest you learn Apache Spark for big data. Although my\u00a0research on data scientist job listings<\/a>\u00a0shows it\u2019s falling in popularity, it\u2019s still in nearly half of all data engineer job listings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Spark<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Wrap<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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I hope you found this guide to the most in demand technologies for data engineers useful.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data. They are responsible for storing and making data usable by others. Data engineers set up pipelines to injest streaming and batch data from many sources. Eventually the data finds its way into dashboards, reports, and machine learning models. Which tech skills are most in demand for data engineers? How do they compare to the most in demand tech skills for data scientists? Read on to find out!<\/p>\n","protected":false},"author":369,"featured_media":3673,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[2134],"class_list":["post-2257","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":2134,"user_id":369,"is_guest":0,"slug":"jeff-hale","display_name":"Jeff Hale","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Hale","first_name":"Jeff","job_title":"","description":"Jeff Hale is a co-founder of Rebel Desk, where he oversees technology, finance, and operations for this company. He is an experienced entrepreneur who has managed technology, operations, and finances for several companies. "}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2257","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/369"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2257"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2257\/revisions"}],"predecessor-version":[{"id":35456,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2257\/revisions\/35456"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3673"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2257"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}