In this post you will understand how to become Big Data Engineer. Data engineering is an excellent opportunity to start a career in data management for people who have only a basic understanding of machine learning, but are also interested in developing databases and managing them. Thus, such work, of course, is more suitable for software engineers, architects and database administrators.
Make sure you understand that Data Scientist and Data Engineer are not the same thing.
A Data Scientist builds models using mathematics, statistics and machine learning to explain and predict complex behavior, and codifies those models into real-world software. A Data Engineer designs and builds data architectures for ingestion, processing, and surfacing data for large-scale data-intensive applications.
Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale. The Data Scientist is interested in large scale architecture only insomuch as it allows the "science to scale." Thus any Big Data project should have a Data Scientist alongside the Data Engineer to ensure that what gets built is analytically sound (no point in engineering a big data architecture that doesn't prepare and process data in a way that supports the specific models built by the Scientist).Data Engineers should understand the core concepts in computer science and should be very well versed in building and designing large scale applications; end-to-end. They should understand the pros and cons of using relational and noSQL databases. They must understand distributed computing and should be able to work with the Data Scientist to help split algorithms effectively to still yield predictive accuracy across a variety of domains. They should know when to push schemas towards the application to allow for "data lake" designs that assist in large scale analysis but still serve domain-specific applications. And they should be very familiar with the core technologies that are used to build these systems.
Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale. The Data Scientist is interested in large scale architecture only insomuch as it allows the "science to scale." Thus any Big Data project should have a Data Scientist alongside the Data Engineer to ensure that what gets built is analytically sound (no point in engineering a big data architecture that doesn't prepare and process data in a way that supports the specific models built by the Scientist).Data Engineers should understand the core concepts in computer science and should be very well versed in building and designing large scale applications; end-to-end. They should understand the pros and cons of using relational and noSQL databases. They must understand distributed computing and should be able to work with the Data Scientist to help split algorithms effectively to still yield predictive accuracy across a variety of domains. They should know when to push schemas towards the application to allow for "data lake" designs that assist in large scale analysis but still serve domain-specific applications. And they should be very familiar with the core technologies that are used to build these systems.
To become a data engineer, The following technologies you should have skills:
Programming languages (such as Scala, Java, Python, C++ …) but definitely make sure that you also know at least 1 scripting language (such as javascript). Your job is to basically get data from the source and make sure that it lands somewhere, but the getting and putting can go through scripts and/or apps.
Databases and query languages:
Databases and query languages:
SQL is no excessive luxury, invest in it and make sure that you’re at least a bit proficient. As for databases, make sure that you understand how they work, why to use NoSQL alternatives, what the benefits of certain architectures over others, …
soft skills:
soft skills:
Communication is an essential part of your job. You will need to communicate to business as well as the technical teams and make sure that you grasp everything that both teams are trying to communicate.
Basics of big data:
Learn about Spark, Hadoop and make sure that you understand the impact of those types of architectures on the traditional RDBMS systems.
Data management:
Data management:
Checking the data quality and managing the data appropriately will be one of your tasks; Make sure you know some techniques to make sure that you guarantee that the data is managed in a correct way.
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