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Data Engineering – The New Era Has Begun

  • April 01, 2022
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Programming Languages

To understand Data Engineering, one needs to first understand the difference between the following terms.

• Project vs Operations

• Data Analyst vs Data Scientist vs Data Engineer A project is an endeavor, which has a defined start and end date and a unique outcome. This is the responsibility of Project Management. Operation is a repetitive task, which generates a repetitive outcome. Usually, once a project is a success, operations management will replicate the successful outcome continuously.


  • Data is collected from various sources
  • This collected data is stored in a database
  • Data is then pre-processed and cleansed, and appropriate variables/features are selected
  • Either reports are generated, or a model is built based on the needs of stakeholders

For the first time when this is done then this is called a Project. However, companies & decision-makers want to repeat the steps, which have proven to show results. The team working on this initiative does not want to repeat all the steps manually. Hence, the team tries to establish the connection among various systems (database, programming tool, end-user application, etc.) via., APIs (application programming interface). From there on reports are automatically generated at a defined frequency without meddling with the connections between varied systems.

Now that you have a fair idea of the difference between project and operations, let us discuss the difference in responsibilities of the roles – Data Analyst, Data Scientist, and Data Engineer. We shall spend the remaining article on understanding data engineering, which is becoming more attractive than data science. Glassdoor mentions that data engineer job openings are 5 times more than data scientist job openings.

For all ‘3’ roles, data and flow of data understanding are pivotal.

  • The Data Analyst role performs Data Analytics activities as a profession. Data Analytics is all about getting access to past data and then generating reports and dashboards to get insights. Typically, insights talk about past and present scenarios. The activity performed by data analysts is usually part of descriptive analytics.

  • Data Scientist role performs Data Science activities as a profession. Data Science is all about getting access to past data and typically does predictions and forecasting to get proactive insights. Typically, insights talk about future scenarios. The activity performed by a data scientist is usually part of predictive analytics. However, all the tasks performed by Data Analyst are also performed by a Data Scientist at the beginning of any data-related project.

  • The Data Engineer role performs Data Engineering activities as a profession. Data Engineering is all about establishing data pipelines (which will be discussed in further sections) to ensure that the connections are established among disparate systems/applications. Connections are usually established by writing lines of code and are called APIs.

Learn the core concepts of Data Science Course video on Youtube:

Data Engineer Prime Responsibilities:

  • Design the data pipeline, which helps understand the flow of data from source to the end-user interface

  • Implement the designed data pipeline, so that the data analyst’s or data scientist’s scope of work is incorporated into existing applications and results can be seen in action

  • Maintain the implemented data pipeline, so that there are no unforeseen errors during production usage of the application

  • Data transformation so that there is optimized data available for data analysts and data scientists

  • Data must be made available to different stakeholders, who then draw insights for business decision making

  • Must create processes for data quality verification and must create metadata for better data understanding

  • Must work on code lifecycle management related to data transformation

  • Must work on integrating data consumption tools with transformed data

Note: Data pipeline will help mainly with raw data ingestion from both internal as well as external sources into the data storage system. Data ingestion can be either batch ingestion or real-time ingestion (streaming data ingestion).

‘SQL’ & ‘Python’ knowledge is needed at a bare minimum for extracting data from different formats & databases. Data Engineering Tools, which a data engineer must be well-versed, are as follows.

Programming Languages:


Python can be used to query data from both – Relational as well as NoSQL databases. SQL knowledge is extremely important for optimizing SQL queries for speed. Combining both Python & SQL skills is extremely pivotal to becoming a good Data Engineer. Data engineers also perform data transformations. Data Lakes & non-SQL databases also have tools to query the data from SQL. Java, Scala, Clojure, Groovy, Jython, etc., are a few of the data engineering tools extensively used.

Databases (DB):

Data is usually stored in relational databases. The licensed databases, which are widely used in the production environment include Oracle DB, Microsoft SQL, etc. Widely used open-source databases include MySQL, PostgreSQL, etc. These databases store the data in rows.

SQL databases, which are used in Data warehouse (DWH) setup include Amazon Redshift, Google Big Query, Apache Cassandra. A few of these are on Cloud and a few of these are open source. These databases store the data in columnar format as opposed to row format. Data from these databases can be queried via SQL queries as well. When it comes to speed, columnar databases win the race with their fast-querying capability. Elasticsearch, a search engine based on Apache Lucene is the NoSQL database used in DWH setup, which stores data in the form of documents.

Data Processing Engines:

Data Processing Engines

These engines allow for data transformation to occur in parallel as well as serial manner. For data batches or streaming data, it will process them in parallel. Apache Spark is the one amazing processing engines used extensively by data engineers and one can write the transformation program in Python. Another engine catching the attention of the world is Apache Storm, Apache Flink, and Samza. These can also handle data, which gets generated endlessly.

Data Pipelines:

In data engineering, a data pipeline is a series of steps used to extract, transform, and load (ETL) data from various sources into a destination for analysis or storage. Data pipelines play a critical role in data engineering, as they enable data engineers to move and transform large volumes of data between systems efficiently and reliably.

The process of building a data pipeline involves several steps, including:

Data ingestion: This is the process of collecting data from various sources such as databases, logs, and APIs. Data can be ingested in real-time or in batches depending on the use case.

Data cleaning: Raw data is often incomplete, inconsistent, or contains errors. Data cleaning involves removing duplicates, correcting errors, and ensuring data quality.

Data transformation: This is the process of converting data from one format to another and preparing it for analysis. Data can be transformed using tools such as SQL, Python, or Apache Spark.

Data storage: Once the data is cleaned and transformed, it needs to be stored in a destination such as a data warehouse, data lake, or database.

Data processing: Data processing involves running analytics and generating insights from the data. This can be done using tools such as SQL, Python, or Apache Spark.

Data delivery: Finally, the insights generated from the data are delivered to the end-users or systems that need them. The design of a data pipeline can vary depending on the specific requirements of the use case. It may involve using various tools and technologies such as Apache Kafka for real-time data ingestion, Apache Airflow for workflow management, and Apache Hadoop for distributed data processing.

data pipelines are a critical component of data engineering as they enable the efficient movement and transformation of data between systems. Building a data pipeline involves several steps, including data ingestion, cleaning, transformation, storage, processing, and delivery

Deploying Data Pipelines in Production:

Deploying data pipelines in production is a crucial step in data engineering, as it enables organizations to automate the process of data ingestion, processing, and delivery. Deploying a data pipeline involves several steps, including testing, monitoring, and scaling.

Testing: Before deploying a data pipeline to production, it is essential to test it thoroughly to ensure that it works as expected. This involves testing the pipeline end-to-end, from data ingestion to data delivery, and verifying the accuracy of the results.

Monitoring: Once the data pipeline is deployed to production, it is essential to monitor it continuously to ensure that it is working correctly. This involves setting up monitoring tools to track key metrics such as data throughput, latency, and error rates.

Alerting: In addition to monitoring, it is essential to set up alerting mechanisms to notify the data engineering team in case of any issues or failures in the data pipeline. This enables them to take corrective action promptly.

Scaling: As the data volume and complexity increase, it may be necessary to scale the data pipeline to handle the additional load. This involves adding more resources such as servers or clusters to the pipeline to ensure that it can handle the increased data volume and processing requirements.

Documentation: Finally, it is essential to document the data pipeline and its deployment process thoroughly. This enables new team members to understand the pipeline's design, dependencies, and deployment process quickly.

To deploy a data pipeline in production, organizations typically use containerization tools such as Docker to package the pipeline and its dependencies into a portable container. This enables them to deploy the data pipeline consistently across different environments, such as development, testing, and production.

Version Control & Monitoring Data Pipelines:

Data pipeline can break in between and start throwing errors. To counter this and to ensure that the pipeline is reset to the best-known previous state, version control is pivotal. NiFi registry will help track the version of all the stages of the pipeline and the data engineer can ensure that the latest version of the pipeline is always available.

Post-deployment, we can encounter errors pertaining to code, data workflow, network, etc. Monitoring the data pipeline can be easily accomplished by NiFi. Data engineers can build their monitoring tools using Python in conjunction with NiFI REST APIs.

Streaming data can be handled, during data pipeline building, using a tool called Apache Kafka. Kafka cluster creation requires a ZooKeeper cluster, which maintains the information about the cluster. To perform data transformation on huge streaming datasets, data engineers often make use of Apache Spark. This can be used to process data in a distributed environment. PySpark is a Python programming-enabled Spark variant. If one knows Python programming, then PySpark coding becomes extremely easy. For edge computing deployments, data engineers usually use MiNiFi which is usually used to deploy the solutions on mobile applications, IoT devices, etc., which requires very low processing power.

The conclusion is that we have a wide plethora of tools available to accomplish a Data Pipeline. However, one needs to understand whether the deployment is going to be on-premises implementation or on-cloud. Depending on this data engineer must perform a mix and a match to ensure that the business objectives are met. It is all about cost-benefit analysis to ensure the right tools are selected and a seamless working production-ready data pipeline is built.

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