Data, data, and data. Go to the internet and search about data engineering, you will realize nowadays the concept of data engineering is been in the center of discussion. Massive amount of data is getting generated every day in most businesses today.
This may include the data generated out of the stock market apps, customers’ responses, everyday sales performance, website/app analytics data, and many more.
This information is crucial for the business to perform well. And hence needs to be analyzed professionally. Now let us understand what’s the DATA.
Data is information stored in a format that is efficient for movement and processing. Data may be stored in different types like video, audio, images, and text. Every data is stored in the computational system in a binary format. Raw data is nothing but data in its most basic digital format.
In the recent few years, we can say data in the process of business analytics has gained importance. Data processing has become popular and important nowadays, in the field of cloud computing.
Data engineering is the field where data engineers build a software system to collect, process, store, and analyze data at a large scale. Organizations nowadays generate a massive amount of data. They need the right people and the right technology to process the data in usable outcomes which helps in the organizational decision-making process.
Data engineers work in a variety of settings to build data processing systems that collect manage and convert raw data into meaningful information for data scientists and business analysts. As we all know, engineers design and build things, the same way, data engineers design and build data pipelines to optimize data into usable information for data scientists and business analysts.
Companies have got huge data with them and every day the number is growing. This data sometimes is in a so desperate form to comb through to answer the vital business question.
Data Engineering is a practice that makes it easy for the data consumers like data scientists & business analysts to quickly and securely examine all the data available with them.
Data engineering plays an important role in this era of big data. As we mentioned earlier that companied are dealing with the massive information collected from the real and digital world. Data engineers are experts in optimizing this large amount of data into usable information. Data engineering plays a large role in the following objectives:
Data engineers are responsible for the foundation of a database and data architecture. Data engineers with the integration number of tools, they apply database techniques to create a robust database architecture. Data engineers are also tasked to perform testing on database to identify any bugs or any performance issues are there.
– An easy-to-use, fully-managed, cloud warehouse by Amazon AWS. Redshift have helped thousands of businesses till now. Tools allows easy process to setup & scale your data warehouse.
– BigQuery is a fully managed cloud warehouse by Google.
– Tableau is one of the most commonly used BI tool with drag and drop functionality to use data from different departments.
– Snowflake with its unique shared data architecture provides high performance & flexibility. Also data workloads are scaled independently. Hence making it superior platform.
– A command line tool to transform the data in the data warehouse. Data engineers can easily write transformation to mage data more effectively.
– One of the most comprehensive ETL tool. It allows easy data collected from APIs, websites, servers and stored in data warehouse, & then transferred to other tools.
– Apache Kafka is used to real-time streaming of data pipelines and applications that adapt these data streams.
– A business analytics tools by Microsoft, primarily used to provide interactive visualizations and business intelligence capabilities. The end users can create their reports and dashboards.
With the growing flow of the data in the technology world, there is a growing demand for the data engineering practices. It is very well understood that processing of data in a meaningful way to generate business intelligence, is a need and key to business success.
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