Top 10 Must Have Skills for a Data Engineer

Data is transforming the way businesses are optimized. The management of data has become increasingly important over the past few decades. As long as the data is high quality and competent to perform complicated tasks, organizations can reap innumerable benefits.

Extracting such huge quantities of data, validating it, managing it, and maximizing its potential for use is, in fact, a technological art and science. Let us take a look at how data engineers contribute to the process of optimizing the benefits of data. What is data engineering, and what are the Required skills to Become a Data Engineer?

What Is Data Engineering?

Data engineering refers to the science of collecting and validating information so that data scientists can use it to their advantage. The aim is to create systems for managing the collected information across nearly all major industries. Different information systems are designed and developed through a software engineering approach.

Data engineering was developed to support data management to ensure that analysts and data scientists can utilize data with security, accuracy, and speed. A data engineering pipeline is designed and built so that it is in a highly functional state when it reaches the data scientists.

As the name suggests, data engineering looks at the engineering part – designing and building pipelines for data transformation and transportation. A data warehouse is meant to assemble the data from various sources into a single place to present it with uniformity. Data engineers are the resources who do data engineering best!

10 Skills Required to Become a Data Engineer

Here is the list of 7 Essential Data Engineer Skills:

1.      Cloud computing tools

It is one of the key responsibilities of big data teams to set up the cloud to store and ensure data availability. Therefore, applying it becomes a vital skill when dealing with big data. Businesses use hybrid cloud platforms, public clouds, or internal clouds depending on data storage needs. You should also be familiar with platforms like AWS, Azure, GCP, OpenStack, and Openshift.

2.      Database tools

A deep understanding of database design & architecture is necessary for data engineering careers, typically managing and storing vast data. The two types of databases commonly used are structure query language (SQL) and NoSQL databases. While techniques such as MySQL and PL/SQL are used for structured data storage, NoSQL databases such as Cassandra, MongoDB and others are suitable for storing large volumes of structured, semi-structured & unstructured data depending on the application.

3.      Machine Learning skills

Bringing machine learning into the big data process can help identify patterns and trends that can expedite the process. Data can be categorized, identified, and translated into insights using machine learning algorithms. A strong background in mathematics and statistics is required to understand machine learning. In order to develop these skills, you can learn to use tools such as SAS, SPSS, R, etc.

4.      Data transformation tools

The raw data presented in big data cannot be used directly. In order to process it, it needs to be converted into consumables based on the use case. Many data transformation tools are available, including Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage, and more. Depending on the sources, formats, and output requirements, they can be simple or complex.

5.      Data mining tools

It’s also important to know how to handle big data with data mining, which involves extracting valuable information from large data sets in order to find patterns and prepare them for analysis. With data mining, predictions can be made, and data classifications can be performed. Big data pros must be adept at using data mining tools like Apache Mahout, KNIME, Rapid Miner, Weka, and others.

6.      Data ingestion tools

Data ingestion is a crucial part of big data skills, moving data from a source to a destination where it can be analyzed. Data ingestion becomes more complex as data volumes and formats increase. This requires knowledge of data ingestion tools and APIs in order to prioritize data sources, validate them, and dispatch data to ensure success. There are several data ingestion tools to know, including Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop, Wavefront, and many others.

7.      Data warehousing and ETL tools

Enterprise data warehouses, including ETL, help companies leverage big data effectively. This is accomplished by converting heterogeneous data into a single stream. Data from multiple sources is extracted, transformed, and loaded into the warehouse using ETL. There are several popular ETL tools, including Talend, Informatica PowerCenter, AWS Glue, Stitch, and more.

8.      Data visualization skills

Visualization tools are used by big data professionals all the time. A consumable format needs to be generated for the end-users to consume the insights and learnings generated. Tableau, Qlik, Tibco Spotfire, Plotly, and more are some of the popular visualization tools that you can learn.

9.      Real-time processing frameworks

To generate timely insights into your data, you need to process it in real-time. It is primarily used as a real-time distributed processing framework for analyzing data in the real world. Others to be aware of include Hadoop, Apache Storm, Flink, etc.

10.  Data buffering tools

With the growing volume of data, data buffering has become a pivotal technology to speed up data processing. In essence, a data buffer stores data temporarily while being transferred from one place to another. Streaming data is generated continuously from thousands of different sources, so buffering becomes essential. Tools like Kinesis, Redis Cache, and GCP PubSub are commonly used to buffer data.

FAQ

Conclusion

Five common data engineering mistakes cannot be made; data complexity, inaccurate data, miscommunication, usage requirements, and insufficient communication. Learning new things is generally a time-consuming process without proper guidance. The key to your success is a comprehensive training program that fits your schedule, is adaptable, uses real-world laboratories, and allows you to study with an experienced instructor.