Data engineers must be hands-on with the data engineering tools more than any other practitioner in data science. A data engineer whose CV does not include references to data engineering tools like Hive, Hadoop, Spark, NoSQL, or other high-tech data storage and manipulation technologies is generally not considered a data engineer.
However, while knowledge of data engineering tools is crucial, data architecture and pipeline design principles are far more important. The data engineering tools are useless unless you have a firm conceptual knowledge of:
- Data models
- Relational and non-relational database design
- Information flow
- Query execution and optimization
- Comparative analysis of data stores
- Logical operations
In many aspects, data engineering is comparable to software engineering. Beginning with a certain aim in mind, data engineers assemble effective solutions to achieve that goal.
Data Engineers put data science into practical applications ranging from robots to automobiles. These are all essentially data-driven judgments. Like most data science disciplines, the data engineering function is still being defined and may include different components of the profession at different firms. Data engineers may be in charge of:
- Data architecture
- Database setup and management
- Data infrastructure design and build
All of this frequently boils down to establishing and populating a data warehouse in businesses with vast volumes of data, particularly from heterogeneous sources.
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For Corporate Data Engineers, Data Warehousing Is The Killer App
A data warehouse is a centralized repository for business and operational data for large-scale data mining, analytics, and reporting. Various data sources and repositories merge into a single helpful tool for data scientists and business users to refer to using the warehouse.
The process of creating this resource, on the other hand, often entails some considerable extract, transform, and load (ETL) procedures, which involve extracting data from source databases and reformatting it for inclusion in the warehouse. The design and coding of the procedures underlying ETL operations are often the responsibility of data engineers, as are the automation steps typically produced concurrently to provide a continuous data pipeline that can run without human involvement.
The organic growth of database support systems in modern businesses has made architecting and building functional data warehouses a complicated business, and data engineers are the experts that companies turn to when they have to figure out how to get sales data from an Oracle database to talk with inventory records stored in a SQL Server cluster.
Data engineers are also responsible for managing and optimizing these activities. In addition to having professional knowledge of the database program itself, having some expertise in the underlying server hardware is frequently beneficial.
Data engineers may also be requested to develop other users’ data services. These pipelines flow in the opposite direction as those that deliver data into the data warehouse. Instead, standard APIs (Application Programming Interfaces) give uniform access to backend data repositories. Data engineers essentially develop translators for their data stores that employ a consistent language for accessing information even when the stores themselves differ significantly.
Clairvoyant’s experienced Data Engineering team employs a customized strategy to assist businesses in monetizing and optimizing the value of their data. We create a strong data foundation and then utilize data mining to generate insights. Our goals are to overcome critical barriers that prevent businesses from capitalizing on growth opportunities and converting themselves into data-savvy competitors.
