Introduction to data engineering: What it is and why it matters

Data Engineering

INTRODUCTION TO DATA ENGINEERING

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. 

What is a ‘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 – Overview 

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.

Significance of Data Engineering 

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: 

  1. Managing data together at one place via data integration tools. 
  2. Getting clear understanding of business intelligence. 
  3. Improvement in data security. 
  4. Protecting business information from cyber attacks 
  5. Improving your practice of SDLC i.e. Software Development Life Cycle.

What do Data Engineers do? 

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. 

 

Data Engineering Skills 

  • Database Skills – Data engineers are proficient in SQL as well as Cassandra, MongoDB, and HBase. 
  • Data Warehousing – Data engineers have clear understanding of Data Warehousing, ETL process & data modelling. 
  • Data Integration – Data Engineers are capable of integrating data from various sources like web applications, APIs, etc. 
  • Programming Skillls – Data engineers are proficient in programing languages like Python, Java, etc. 
  • Cloud Computing – Data engineers are familiar with cloud computing technologies like Azure, AWS, GCP, etc. 
  • Data Pipelines – Data engineers are capable of creating data pipelines using tools like Apache Nifi, Apache Kafka, Apache Airflow etc. 
  • Soft Skills – Problem solving skills, Good communications Skills, readiness to learn new technologies. 

Top Data Engineering tools used at 64 Squares

TOP DATA ENGINEERING TOOLS USED AT 64 SQUARES
  • Amazon Redshift

    – 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

    – BigQuery is a fully managed cloud warehouse by Google.  

  • Tableau

    – Tableau is one of the most commonly used BI tool with drag and drop functionality to use data from different departments. 

  • Snowflake

    – Snowflake with its unique shared data architecture provides high performance & flexibility. Also data workloads are scaled independently. Hence making it superior platform. 

  • DBT

    – A command line tool to transform the data in the data warehouse. Data engineers can easily write transformation to mage data more effectively. 

  • Fivetran

    – 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

    – Apache Kafka is used to real-time streaming of data pipelines and applications that adapt these data streams.  

  • Power BI

    – 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.

Conclusion 

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. 

Author

  • Vikrant Chavan

    Vikrant Chavan is a Marketing expert @ 64 Squares LLC having a command on 360-degree digital marketing channels. Vikrant is having 8+ years of experience in digital marketing.

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