The amount of data generated every day is growing exponentially. Zoom calls, smartphones and even your wifi-connected ACs contribute to this. At least 2.5 quintillion bytes of data are produced daily worldwide. No wonder then that the Big Data Analytics market in India is estimated at $2 billion in 2022 and is expected to reach approximately $16 billion by 2025, representing a CAGR of 26%. This equals a 32% share in the global market. For companies to use this data to provide insights into their market needs the services of data specialists.
Data engineering makes raw data usable. It helps in building and designing systems for storing, collecting and analysing chunks of data. The processes include employing unique modules and steps like data crunching, infrastructure, mining, modeling, management and acquisition. Businesses can now find practical applications that can be deployed to boost their bottom line. The role of data engineers is to extract, transform, load, aggregate and validate data that involves understanding what kind of data is usable, ensuring it adheres to data governance rules and building data pipelines.
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Data Engineering Tools
The tools are not easy to understand without a solid concept of data models, information flow, logical operation, relational and non-relational database design and query execution. For this, you can consider GeekLurn’s Data Science Architect Program. It guarantees 100% money back in case you are not satisfied and 100% job placement after completion of the course.
The features of the program are 24-hour duration, 6 months of live interactive classes, 18 months of sponsored project work, 1.5 years of real-time experience certificate and 320+ hours of live training sessions. It is highly valuable for those of you looking for a strong foundation in data science, wishing to secure a job as a data engineer or keen on upgrading your skills to expand your opportunities.
Here is a list of tools that are needed for the data engineer role:
- SQL: Structured Query Language is used to retrieve, store, maintain and create a relational database.
- Python: It is used for data wrangling like automation, reshaping, joining disparate sources, aggregating and API interaction.
- Cloud Data Storage: This includes Amazon S3, Google Cloud and Azure Data Lake Storage. It uses data centres with big computer servers that may physically store the data so that it is available to users via the web.
- ETL Tools: They automate transforming and loading data from multiple resources in diverse forms.
- Snowflake: Data engineers can spend little or no time in handling infrastructure to consume, deliver and transform deeper insights.
- Tableau: The tool is used mainly for visualisation, exploring and securely sharing data in the form of dashboards and workbooks.
- Segment: This is used in data engineering to derive insights and send them to various other tools like Google Analytics.
What is Data Engineer’s Role?
A data engineer works in a variety of settings to convert raw structured and unstructured data into workable information. The main aim is to help organisations enhance their performance by getting the desired value from their big data. Data engineers deal with different phases of the data pipeline like ingestion, gathering, storing and accessing. It is a critical aspect of any company’s growth, predicting future trends and network interactions.
Other data engineering basics are building, testing and maintaining database architecture, ensuring compliance with data governance and security policies and collaborating with management to understand company objectives, developing algorithms, acquiring datasets and creating new data validation methods.
Types of Data Engineers
Around 33,000 people across different enterprises in India call themselves data engineers. They can be divided into two main categories:
- The first kind does its data processing with SQL. They can use an ETL tool and may have titles like SQL Developer, DBA or ETL Developer.
- The second kind is a software engineer who specialises in Big Data and is equipped with extensive programming skills. They can also write SQL queries.
It is necessary for leaders and managers to have an understanding of these differences in their business, which help pick the right kind of engineer for the Big Data project at hand.
Data science aspirants must know the required starting skills to be able to pick the right course and succeed in this field. The main skill is programming, which can change the data into a state that is queryable.
Data Engineering Architecture
Data architects make the best use of their design and computer science skills to analyse and review the data infrastructure of a business or organisation. They also help in planning the future database and implementing robust solutions. The main role is to visualise and design the data framework that describes the processes used to plan, enable, specify, maintain, archive, use, control, retrieve and purge data.
Coding is a vital part of a data architect’s job and is widely used to set up different data structures and implement systems in Azure, AWS or the company’s server structure.
A data engineer’s salary in India ranges from ₹3.5 lakhs to ₹21 lakhs per annum, with the average being ₹8.3 lakhs per annum. Once you have a clear understanding of what data engineering is, the next step in advancing your career is to choose a course that can give you a combination of knowledge and experience.
Neel Neeraj
Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
