What is Data Engineering?

Data engineering is a set of processes of creating interfaces and mechanisms for how data should be created, captured, copied, stored, and consumed by data consumers. Dedicated specialists such as data engineers and data analysts handle the organization’s data and data infrastructure so that data remains available and usable by others in the form of analytics and reports. Companies use data to answer several questions related to business.

According to Statista, global data creation is projected to grow to more than 180 zettabytes by 2025. The amount of data created and replicated has already reached a new high of 64.2 zettabytes in 2020. Since then, data volume growth has been witnessed and is supposed to grow at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025.

Why is Data Engineering Popular Now?

Companies of all sizes and kinds are generating a large volume of data. These data sets are coined from fast-growing mobile data traffic, cloud computing traffic, and the rapid development of artificial intelligence and the internet of things. These data sizes are too large and complex for data processing.

Organizations want to leverage this increasing volume of data which has ignited the demand for data engineering in almost every company dealing with a good data set every day. Every company now aspires to have systems that support data mining and predictive analytics to develop new business insights. (Check here an exclusive list of top 10 industries that benefit most from data analytics)

Here are some other reasons behind the growing demand for data engineering:

1. The Data Science Hype: Data is oil for the 21st century. It has given rise to data science and data scientists enabling companies to build new data products, gather insights from data, build incredible models and forecast trends, and much more.
2. Big Data Companies and Cloud services: The emergence of several cloud services and big data technologies has paved the way for data science and data engineering. Several data companies promote and provide support for storing data, building data pipelines, and many data-related services and solutions.
3. The Need For Quality Data: It’s a no-brainer that almost every company is generating a high volume of data at every passing second, but it is not sure that every bit of generated data is useful. Only quality data is useful to companies, and this is where data engineers help companies prepare quality data by collecting, cleaning, storing, and following other mechanisms.
4. Evolution of AI and ML: Data is the core of artificial intelligence and machine learning algorithms. Companies using AI and ML need data sets to be supplied in the form that the algorithm understands. If data is not infused appropriately, AI/ML algorithms will not be able to unlock knowledge available in the data. Creating data sets and setting up a robust infrastructure for it requires another hand of help, i.e. data engineering.

Key Data Engineering Skills and Tools

Data engineering requires specialized knowledge and tools to work with data. Therefore, data engineers pay utmost attention to the way data is modeled, stored, secured, and encoded. Also, they are required to have an understanding of the most efficient ways to access and manipulate the data. To acquire better insights, data engineers work on end-to-end data pipelines. Each pipeline could have one or more sources as well as destinations. Under a pipeline, data is transferred through multiple steps for transformation, validation, enrichment, summarization, or other steps. With the help of different technologies, data engineers build different pipelines based on business requirements. Top data engineering skills and tools that data engineers use include:

1. ETL Tools: ETL stands for Extract Transform Load. It is a process of using technologies to allow data to move between systems. ETL tools are used to access data from several touchpoints of technologies and then apply rules to “transform” and cleanse the data to use them for analysis. SAP Data Services is one of the best examples of ETL tools.
2. SQL: SQL or Structured Query Language (SQL) is widely popular for querying relational databases. It follows well-defined standards and allows easy management of database systems without having to write a substantial amount of code. To perform ETL tasks within a relational database, SQL is highly preferred. Using SQL is highly considered in case the data source and destination are the same types of database. SQL is easy to understand and supports several tools that make it highly popular among software engineers.
3. Python: Data engineering and data analytics has made Python so popular in recent years. Python is a globally adopted and widely-used programming language for data engineering. The core features and benefits make Python a better choice than Java and other high-end programming languages such as Python syntax is human readable. It has the support of an ocean of libraries, packages, and frameworks supporting heavy mathematical jobs easily. It has a highly developing community supported by Google, IBM, and others. On the other hand, Python is a highly preferred programming language among data engineers to build ETL jobs and data pipelines. Python simplifies working with storage technologies, databases, and cloud based data technologies.
4. Hadoop and Spark: Hadoop and Spark are the two most popular data engineering tools that work with large datasets of clusters on computers. Both proved as economical solutions when large data sets have to be stored on a single machine. These data technologies accept data in different forms and sizes including structured and unstructured. Moreover, these tools are easy to use, scalable, and come with all essential libraries and support for multiple programming languages essential for data engineering, data science, and machine learning requirements.
5. HDFS and Amazon S3: HDFS (Hadoop Distributed File System) and Amazon S3 are now available as highly prominent data engineering tool that allows storing data during processing. These are specialized file systems designed to store unlimited amounts of data, which is essentially helpful in performing data science jobs. Amazon S3 and HDFS are inexpensive since processing generates a large volume of data. Data engineers can integrate these data storage systems into an environment where data will be processed. It makes managing data systems much easier.

Conclusion

Data engineering is all about making data more useful and accessible with the help of advanced technologies and tools. For example, companies using IoT and other emerging technologies like AI and ML require data engineering to transform complex IoT data into business insights, structure and analyze data at scale and on-demand. Data engineers master themselves in all the intricacies of data and technology and source and curate the data in a given format so that it is much easier to use for consumers of the data. New data technologies frequently emerge, helping achieve significant performance, security, or other improvements that data engineers need to do their jobs better.