Take a Look at the Top 7 Skills that a Data Engineer Certainly has to have

These days, everyone aspires to have a career in data science. What about those who work as data engineers? The reality of the matter is that a Data Scientist is only as good as the quality of the data they are given to work with. Because companies store their data in a variety of formats across databases and text files, the primary responsibility of Data Engineers is to build data workflows, pipelines, and ETL processes that prepare and transform data for Data Scientists, thereby making it easier for Data Scientists to do their jobs. Data Engineers are just as vital as Data Scientists, despite the fact that they are seen to be less visible. This is because data engineers prefer to shift away from the end result of analysis.

Knowledge of constructing intricate database management systems for businesses is included in the list of tools and components of data architecture. This phrase also refers to procedures that deal with data while it is at rest, data while it is in motion, data sets, and how these things connect to applications and processes that are reliant on data. It is essential for your company to use the services of data engineering service providers in order to get priceless insights from your data. Let us check below data engineering skills that are necessary to grow:

1. Technologies on Hadoop and Big Data Frameworks: Within the Hadoop Ecosystem, there are a number of tools that serve a variety of professions and professionals from a variety of backgrounds, including the following: HDFS (Hadoop Distributed File System), YARN (Hadoop Resource Namespace), MapReduce, PIG & HIVE, Flume & Sqoop, ZooKeeper, and Oozie are all components of the Hadoop ecosystem.
2. Time processing framework: Apache Spark is a distributed real-time processing framework that can be readily connected with Hadoop by exploiting HDFS. It is the third real-time processing framework.
3. Extensive and in-depth knowledge of SQL databases (such as MySQL) and NoSQL databases (such as HBase, Cassandra, and MongoDB): Structured Query Language is a tool that is utilized to organize, manipulate, and manage the data that is stored in databases. On the other hand, NoSQL databases are able to store large volumes of structured, semi-structured, and unstructured data with rapid iteration and an agile structure in accordance with the needs of the application.
4. Machine Learning: Although machine learning is technically something that is allocated to the Data Scientist, having some degree of knowledge of how to put the data to use using statistical analysis and data modeling is a tremendous benefit.
5. Solid Knowledge of Operating Systems: In addition to having broad expertise in operating systems, having solid knowledge in operating systems such as UNIX, Linux, Solaris, or MS Windows may be extremely valuable since the majority of the tools will be based on these systems.
6. Transformation tools: Raw formats of big data are now available, however, they cannot be utilized immediately. In order to process information, data has to be transformed into a format that can be consumed, which is determined by the use case. Data transformation may be simple or complicated based on the data sources, formats, and necessary output. Hevo Data, Matillion, Talend, Pentaho Data Integration, and InfoSphere DataStage are just some of the data transformation solutions that are available. There are many more.
7. Constructing and analyzing frameworks: It is vital to process the data being created in real-time in order to provide timely insights that can be acted upon. Data processing is one of the most common applications for Apache Spark, and its most common usage is as a framework for distributed real-time processing. Hadoop, Apache Storm, and Flink are just few of the other frameworks that you should be familiar with.

Bottom Line

It is always a good practice to be acquainted with the abilities listed above, whether you are hoping to become a data engineer in the future or you are seeking for a job in that field. Get in contact with the top firm to take benefit of data engineering service providers that provide services that are accurate, on the leading edge, and cost-effective. They provide a diverse selection of services to clients in a variety of sectors and markets.