1. Concept – Data Engineer

A data engineer works in the field of data science, which revolves around designing and building pipelines. These pipelines are used for transforming data into a format where it can be easily accessed and used by the end user. They perform different kinds of ETL processes to achieve this goal. This data can come from one or more disparate sources.

Data engineering has become quite essential because there is more data than ever before. This increase is due to more businesses looking to use data to be more innovative and more effective. Most of this data exists in various systems that use a range of different technologies and needs to be processed and collated into a centralised location that allows for ease of access for end users such as machine learning architects and data analysts. As data and systems used to capture it become more complex, the more data engineering tools and skills will be required by data engineers to process data.

2. Data Pipelines

A data pipeline is a combination of one or more automated steps in processing data. These steps include copying data, moving data from one location to another, reformatting data and joining it with data from other sources. Each of these steps in the pipeline will usually require separate software or tools to complete. The pipeline data can be from many disparate sources and can be collected into a single location. A data pipeline needs to complete its job in a reliable and consistent manner such that we have uniformly processed data. The pipelines need to be constantly monitored, maintained, and updated according to the changing needs of the business.

3. Responsibilities of a Data Engineer

1. Data acquisition – This involves sourcing data from different systems, i.e., relational databases, text files on a server, etc. When acquiring the data, one needs to be aware of requirements such as how the data will be used, and which people or systems would require access to the data.
2. Data cleansing – This involves detecting and correcting errors in the data. This process involves a wide range of tasks, which include detecting formatting errors, de-duplication of data and identifying missing data.
3. Data conversion – Converting data from one form to another so that it can be easily fed into the next step on the pipeline or so that the end user can consume it easily. Conversion can include steps such as summarising or anonymizing data.
4. Disambiguation – Interpreting data that may have multiple other meanings.

4. Top ETL Tools for a Data Engineer

ETL tools are a category of software used to extract, transform, and load data.

1. SQL (Structured query language) – This is the standard language for querying relational databases. It is a very common tool in the arsenal of data engineers because relational databases are widely used. Common types of relational databases include Microsoft SQL Server, Oracle, MySQL, IBM DB2 and Postgres. SQL is useful when querying or extracting data from any kind of relational database.
2. Python – Python is one of the most common programming languages to date due to its ease of use. It also has a wide range of libraries which enables it to perform a wide range of functions. Some of the common libraries include pandas which is mainly for data cleaning and manipulation. Matplotlib and seaborn are also popular for the visualisations that they can create using datasets. What makes Python very robust is its ability to interact with many different systems such as databases and BI tools.
3. Apache Spark – Spark is a very powerful and robust big data tool. It works with large datasets on clusters of computers that work together to process the data. Spark runs in a distributed fashion by combining a driver core process that splits a Spark application into tasks and distributes them among many executor processes that do the work. One can also run the Scala, Python, R, and SQL shells of Spark. The computational requirements for spark to run are a limiting factor to the number of people using this tool.
4. Cloud services – Cloud computing is becoming more commonly adopted for various reasons such as scalability. Cloud computing services such as AWS and Microsoft Azure offer a wide range of tools that can come in handy for data engineers. AWS’s Databricks (Spark) and S3 (storage) are some of the more popular cloud tools for handling data. One can even set up databases on the cloud now.

Conclusion

Data engineering will continuously evolve as the business demands for data also continue to change. This article can be taken as a snapshot of what data engineering entails at the time of writing.