Data is one of the most important assets of any business. Businesses use data to fix various issues occurring in the company, such as customer relations, product development and website-related processes. Data generated from various sources about customer preferences, bills and shipping and other behavioral information helps businesses to obtain an overall and comprehensive view of their customers.
Nowadays, data is available to us in enormous amounts and is increasing ten folds over time. And managing data in such big sizes is not easy for any enterprise. Companies of all sizes, big or small have data in substantial amounts, and to make that data accessible and readable, we need data engineering.
What is data engineering?
In layman terms, data engineering organized unstructured and unfiltered raw data to make it easy for other people and systems to use that data. According to TechEela, data engineering revolves around designing and creating data pipelines to work upon data analysts and data scientists. These pipelines collect data from various sources and store them in a warehouse, representing data in uniformity. TechEela has also covered a beginner’s guide to understand what is data engineering too.
Although it sounds like an easy job, a lot of skill is required for this role. That is why data engineers are in such high demand these days and are available in noticeably short supply. The term ‘data engineering’ was evolved to define a different role from working with traditional ETL tools and was developed with its tools and techniques to manage the ever-increasing data size. As data kept increasing exponentially, data engineering became a part of software engineering, whose emphasis was essentially on data. Data mining, data warehousing, data modelling, data visualization, data crunching, data infrastructure, and metadata management are said to be its processes.
Need for Data Engineering
- Data engineering is significant to transform it into readable and usable formats as there is a pool of data available to us. This data transformation helps data scientists and analysts work on that data to derive trends and valuable insights.
- Data has become the key asset to companies and across distinct functions such as marketing, sales, IT, finance, and other departments. They are using data for innovative solutions for their business growth and success.
- The technologies that are being used for data analysis and transformation have become complex. Different companies use different technologies to handle their data such as Hadoop, SQL, NoSQL, and other relational databases.
- Organisations are benefitting from data in many ways. They use their data to get an idea of their business’ current state and predict their future state. They can build new products, reduce risks and threats to their business, and model their clients. Data engineering has a key role to play in all these processes.
- If there will not be data engineering, there will not be any data as well. Without data, there will not be artificial intelligence and machine learning. Hence, data engineering is required to have data science.
- Data engineering provides velocity to our data. Stale and old data will not help us in identifying frauds or cyber-crimes. Having real time data can give us insights into activities happening in that very moment.
- Data engineering helps in keeping our data protected by maintaining data privacy throughout the systems.
- Data engineering can provide a robust data analytics program with the help of solid data governance. It can help in finding quality issues and gaps as well as improve data management and collection.
To conclude, data engineering is needed to create pipelines, identify business trends to extract valuable insights and information from the data. Without data engineering, there won’t be the advancements in technology that we see today.