A data engineer is a worker
whose primary job responsibility involve preparing data for analytical or
operational uses. The specific tasks handled by data engineers can vary from
organization to organization but typically include building data pipelines to
pull together information from different source systems; integrating,
consolidating and cleansing data; and structuring it for use in individual
analytics applications.
The data engineer often
works as part of an analytics team, providing data in a ready-to-use form
to data scientists who are looking to run queries and algorithms
against the information for predictive analytics, machine learning and
data mining purposes. In many cases, data engineers also work with business
units and departments to deliver data aggregations to executives, business
analysts and other end users for more basic types of analysis to aid in ongoing
operations.
Data engineers commonly
deal with both structured and unstructured data sets -- as a result, they must
be versed in different approaches to data architecture and applications. A
variety of big data technologies, including an ever-growing assortment of open-source
data ingestion and processing frameworks, are also part of the data engineer's
tool kit.
To carry out their duties,
data engineers can be expected to have skills in such programming languages as
C#, Java, Python, Ruby, Scala and SQL. They also need a good understanding of extract,
transform and load tools and REST-oriented APIs for creating and
managing data integration jobs, and providing data analysts and business users
with simplified access to prepared data sets.
Hadoop data lakes that
offload some of the processing and storage work of established enterprise data
warehouses have been a chief area of application for the data engineer in
support of big data analytics efforts. NoSQL databases and Apache Spark systems
are also becoming increasingly common components of the data workflows set up
by data engineers. Another area of focus is Lambda architecture, which
supports unified data pipelines for both batch and real-time processing.
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