Introduction to data engineering

AbstractThe emergence of big data technology has altered the manner in which we do our daily business. Spontaneous growth of big data technology has necessitated the creation of data engineers who collects these data and manage them.

INTRODUCTION

Data engineering focuses on making the data more useful and readily available for data consumers. Data engineers build systems to be used in data collection, storage, and analysis of data. In the current cognitive era of computing, data engineering is a primary need for every industry. Most modern organizations collect huge amounts of data daily, which is facilitated by the growth of smart sensors and internet of things (IoT) technology. Most of the smart devices used in industries have smart sensors and transducers. Data from the devices are taken and transferred through IoT and stored in different locations. It is the work of the data engineer to fine-tune the collected data and convert it into the appropriate formats. Data engineering is done before the data is forwarded to the data scientists. It's worth noting that emerging data technologies such as deep learning can not thrive without competent data engineers.

WORK OF THE DATA ENGINEER

Data engineers, as we have seen, ensure that the raw collected data is pre-processed and converted into a suitable format to be utilized by the data scientists for the purpose of data analysis. They create data pipelines used by the data-centric and data scientists in their applications. The main goal of data engineers is to make the data available and accessible to the analysis team. Data engineers require a sound knowledge of technical skills in areas such as SQL database and a master of high-level programming languages such as python. They work tirelessly to ensure that this data can be evaluated and optimized to solve different problems of different organizations. Data engineers design and build algorithms used in accessing raw data. Before coming up with such algorithms, they first have to understand the objective of their clients. This is done mainly to make the algorithm perform better per the business goal. Data engineers need to understand data optimization and have the skills to help them develop dashboards and reports. Data engineers may sometimes be tasked with communicating data trends in an organization. The huge organization may have several data scientists and analysts. For data engineers to understand the right tools to be used in data engineering, they should understand different architectural principles that are used in the data processing. The main function of data infrastructure is;

1. Data extraction: in most cases, the information is located in some locations in either structured or unstructured nature. The information can be in the database or in the internal CRM systems. This information can also be real-time data streams coming directly from sensors.
2. Data storage: after the data is extracted, it should be stored securely in certain locations. Data engineering mainly incorporates data warehouses for the purpose of analytics.
3. Data transformation: the raw data is of no use to the user. It makes little or no sense to the business problem being solved. It is difficult and time-consuming to analyze such data. Transformation is done to clean data and format it into appropriate formats to be used by the analytics team Data engineers' roles can be subdivided into three main subcategories. i. Generalists: these are roles for data engineers who work for small companies. They are given the responsibility of the entire data process. they take all data tasks from data management to data analysis. ii. Pipeline centric: This is mainly found in middle-sized companies.in such a setup, data engineers work with data scientists as they gain insight from the collected data. Such engineers need to have sound knowledge of distributed systems and computer science. iii. Database centric: this is for large organizations. In large organizations, the management of data flow is vital. Data engineers in such companies will work with different warehouses from different databases, and they are responsible for developing table schemas

SKILLS NEEDED IN DATA ENGINEERING

To land in a data engineering job, one needs to have big data skills. These skills range from designing, creating, building, or maintaining data pipelines. Knowledge of big data frameworks,databases,and containers is also vital. Knowledge of tools such as Hadoop, scala, storm, and python name a few, is also needed. Below are some skills one needs to have to build a successful career as a data engineer.
i. Database tool: data engineers deal with storing, organizing, and maintaining big data. For one to become a competent data engineer, he needs to understand database design as well as the database structure. The commonly used database structure are the structured query language (SQL) based and NoSQL based.SQL based includes databases such as MySQL, which are used to store structured data.NoSQL includes technologies such as MongoDB and Cassandra, which are used to store unstructured, structured, or semi-structured data.
ii. Tools for data transformation: raw data can not be used directly. They are first cleaned and transformed into desirable formats. The commonly used data transformation tools are the Talend, Pentaho data integration Hevo data, and more
iii. Tools for data mining: these tools extract useful information and then find the patterns in the big data. Mainly data mining assists in data classification and predictions. Some of the data mining tools include Apache mahout, KNIME, Weka, and more