Data Engineering and Why it is trending now ? | 7wData

Data Engineering and Why it is trending now ? | 7wData

As we all know that Data is at the centre of every business today. Data is the fuel that drives companies and no Organization can function without data these days. With huge amounts of data being generated every second from business transactions, sales figures, customer logs, stakeholders and associated devices. All this data gets piled up in a huge data set that is referred to as Big Data.Together, this data provides a comprehensive view of our business.

This data needs to be analysed to enhance decision making. However, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data professionals, validating data, and accumulating data from different sources. Data analysis is challenging because the data is managed by different technologies and stored in various structures.

Companies use data to answer many different aspect of their business, such as: a. Identifying Customer and their 360 degree view b. What’s a new customer worth? c. How can I improve Customer experience ? d. What are the fastest-growing product lines? e. Detect and remediate security vulnerabilities before it occur

Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions.

Now we all want to know what is Data engineering and where it fits in Big Data world. So here is my answer: The key to understand what data engineering lies in the “engineering” part. Engineers design and build things. “Data” engineers design and build pipelines that transform and transport data into a format wherein, by the time it reaches to end users, it is in a highly usable state.These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth.

Many would say that data engineering as a profession has been around for well over a decade, maybe a couple, ever since databases, Microsoft SQL Servers and ETL came to be. Some would say ever since IBM popularised database management systems in the 1970s. Even after the rise of the internet in the 1990s and 2000s, ‘big data” came to be. Yet DBAs, SQL Developers and IT professionals working in the field were not labeled “Data Engineers” at that time. So why the new job title? Let’s summarise by saying that a lot of huge technological changes happened which escalated big data volumes, variety, and velocity. Around 2011 the term “Data Engineer” started to crop up in the circles of new data-driven companies such as Facebook and AirBnB. Sitting on mountains of potentially valuable real-time data, software engineers at these companies needed to develop tools to handle all the data quickly and correctly.

The term “data engineering” evolved to describe a role that moved away from using traditional ETL tools and developed its own tools to handle the increasing volumes of data. As big data grew, “data engineering” came to describe a kind of software engineering that focused deeply on data — data infrastructure, data warehousing, data mining, data modeling, data crunching, and metadata management. Who is Data Engineer, Birth of the data engineer ?

Data engineers make raw data usable and accessible to other data professionals. Organizations have multiple sorts of data, and it’s the responsibility of data engineers to make them consistent, so data analysts and scientists can use the same. If data scientists and analysts are pilots, then data engineers are the plane-builders. Without the latter, the former can’t perform its tasks.

Data teams before the Big Data craze were composed of BI and ETL developers. Typical BI / ETL developer activities involved moving data sets from source to destination and building the web-hosted dashboards with that data (BI).

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