With a humongous 2.5 quintillion bytes of data produced every day, there is a tremendous increase in digitization and automation, which led companies to adopt Data Science as a mandatory cog in the business framework. While Data Science is concerned with exploring data, uncovering insights, and building Machine Learning Algorithms, the quality of the data is maintained by Data Engineers.
Organizations need to construct well-curated Data Pipelines that can be instrumental in achieving business goals. This is where Data Engineers can streamline data flow while maintaining information integrity. The domain of Data Engineering facilitates data infrastructures based on Databases and Data Warehouses, ensuring that data is in a usable shape by the time it reaches Data Scientists. It primarily focuses on practical applications of Data Collection and Data Cleaning.
Table of Contents
- What is a Data Engineer?
- Data Engineers vs Data Scientists
- What are the Roles of Data Engineers?
- Why should you pursue a Career in Data Engineering?
- What are the Key Responsibilities of Data Engineers?
- How to Become a Data Engineer?
- What tools do Data Engineers use?
- What are the Key Skills for Data Engineers?
- Why the Critical Need for Data Engineering now?
- Basic Understanding of ETL practices.
- Basic Understanding of Databases and Data Warehousing.
What is a Data Engineer?
A Data Engineer evaluates data sets for trends and develops algorithms to turn raw data into a format that can be used by businesses. In general, their job entails streamlining information flow and data access rather than doing a lot of Analysis or Experiments. Other data jobs, in fact, rely on Data Engineers’ efforts to extract meaningful information from collected data.
Hence, Data Engineers should be well-versed in Big Data technologies and articulate ideas both within and beyond the team. They should be familiar with NoSQL solutions, Cassandra, HIVE, CouchDB, and HBase, with experience in data structuring, data processing technologies, and more. In addition, they must have a good understanding of Python, SQL, ETL (Extract, Transform, Load), Apache Spark, and Apache Hadoop.
Data Engineers vs Data Scientists
The difference between the responsibilities of Data Engineers and Data Scientists are as follows:
1) Data Engineers’ Responsibilities
The Data Engineer develops, builds, tests, and maintains architectures such as databases and large-scale processing systems.
Data engineers work with raw data that contains errors caused by humans, machines, or instruments. The data may not have been validated and may contain suspect records; it will be unformatted and may contain system-specific codes.
Data Engineers must recommend and, in some cases, implement methods to improve data reliability, efficiency, and quality. To do so, they will need to use a variety of languages and tools to connect systems or look for opportunities to acquire new data from other systems so that the system-specific codes can be used.
Data Engineers must ensure that the architecture in place meets the needs of data scientists as well as the stakeholders, the business. The data engineering team will need to develop data set processes for data modeling, mining, and production in order to deliver the data to the data science team.
2) Data Scientists’ Responsibilities
The Data Scientist, on the other hand, cleans, processes, and organizes huge amounts of data or big data.
Data Scientists are typically given data that has already been cleaned and manipulated, which they can then feed into Sophisticated Analytics Programs, Machine Learning, and Statistical Methods to prepare data for use in Predictive and Prescriptive Modeling. Of course, in order to build models, they will need to conduct industry and business research, as well as leverage massive amounts of data from internal and external sources to solve the business needs.
Once the Data Scientists have completed the analyses, they must present a clear narrative to the key stakeholders, and once the results are accepted, they must ensure that the work is automated so that the insights can be delivered to the business stakeholders on a daily, monthly, or yearly basis.
Both parties must clearly collaborate in order to wrangle the data and provide insights into business-critical decisions. Skillsets clearly overlap, but the two are gradually becoming more distinct in the industry.
The Data Scientists must be aware of Distributed Computing because they will need access to data processed by the data engineering team, but they must also be able to report to business stakeholders: storytelling and visualization are essential.
What are the Roles of Data Engineers?
Data Engineers are assigned to obtain, ingest, process, validate, and clean data as per the business goals. They are frequently in charge of developing algorithms to make raw data more accessible, but in order to do so, they must first understand the company’s or client’s goals. Also, they often work with Unstructured or Incomplete Data and must decide how to process and preserve it. Consequently, Data Engineers must understand how data applications are organized, create and test Data Pipelines, and track how data is used. A Data Engineer’s responsibilities extend well beyond the maintenance of a Database and the Server on which it is hosted.
There are three prominent roles that Data Engineers can perform, viz:
These types of Data Engineers work in small teams or companies. Generalists will likely need to do more end-to-end work, such as following through with the entire process of ingesting the data, processing it, and getting involved in Data Analysis. In other words, they are frequently in charge of all aspects of the data process, from Data Management through Analysis. Smaller organizations do not have to worry about Engineering at scale, these jobs are ideal for anybody trying to switch from a Data Science role to Data Engineering.
2) Pipeline Centric
They work for medium-scale companies parallelly with Data Scientists to help make use of the data they collect. These companies often deal with more complex demands than the generalist Data Engineers described earlier, and as a result, they frequently work in teams since the task necessitates a thorough understanding of organizational requirements and Data Systems.
These roles are perfect for larger organizations and conglomerates. Here the Data Engineer is responsible for managing data flow with priority on Analytics Databases. Database-Centric Data Engineers may also be in charge of creating table schemas for Data Warehouses that encompass multiple Databases. This involves performing ETL work to get data into warehouses.
Why should you pursue a Career in Data Engineering?
According to Domo’s research, humans generate approximately 2.5 quintillion bytes of data per day through social media, video sharing, and other forms of communication. Moreover, the World Economic Forum predicts that by 2025, the world will produce 463 exabytes of data per day, which is the equivalent of 212,765,957 DVDs per day. With an enormous amount of data being generated, there will be a greater need for data engineers to manage it.
If you enjoy experimenting with data, using it to discover patterns in technology, or creating systems that organize and process data to assist businesses in making data-driven decisions, you should consider a career in data engineering. Furthermore, with a median base salary of $102,472, data engineering is a lucrative field. While data engineering can be difficult and complex, and you may need to learn new skills and technology, it is also a rewarding career in a rapidly expanding field.
What are the Key Responsibilities of Data Engineers?
The following are the key responsibilities of every Data Engineer:
1) Data Acquisition
They must first gather data from the appropriate sources before beginning any Database work. After forming a set of dataset procedures, Data Engineers store refined data. Information is stored using specialized technologies that are tailored for specific applications. For example, Relational Database, NoSQL Database, Hadoop, Amazon S3 Storage, and Azure blob storage.
2) Data Modelling
This begins with gathering data requirements, such as how long data must be maintained, how it will be utilized, and who and what systems must have access to it. The Analytical process may include detecting and fixing data errors, translating data from one format to another, interpreting data with various meanings, and eliminating duplicate copies of data.
For this, Data Engineers design classification or clustering Machine Learning Models to scan, label, and categorize Unstructured Data. The models are trained to detect key data points through entity extraction (based on names, locations, and organizations), geotagging, and classification (categorizing text). Later, they have to align architecture with business requirements.
3) Data Preparation
The collected data can be used for carrying out Data Analytics activities like Predictive Analytics or Prescriptive Analytics. However, Data Engineers must first verify that the data is complete, cleaned, and other outlier rules have been defined before they can construct Data Models.
4) Building Data Pipeline Systems
Data Engineers are in charge of designing and producing scalable ETL packages from business source systems and developing ETL processes for populating Databases and creating aggregates. After carrying out the ETL processes, Data Engineers need to manage, improve, and maintain existing Data Warehouse and Data Lake solutions.
During the extraction phase, the Data Engineer must ensure that the Pipeline is resilient enough to keep running in the face of unexpected or malformed data, sources getting unavailable or going offline, and fatal bugs. Maintaining uptime is critical, especially for organizations that rely on real-time or time-sensitive data. In those organizations, Data Scientists and Analysts rely on Data Engineers to construct Data Pipelines that allow them to collect data points from millions of users and analyze the findings in near real-time.
Undeniably, these are the primary responsibilities of a Data Engineer. However, specific job profiles may demand the following commitments:
- Create Databases by integrating various in-house tools and external sources.
- Cooperate with backend developers to build a high-quality Data Architecture.
- Work in collaboration with other teams in the company to understand data requirements and maintain accuracy while handling data.
- To enhance performance and stability, identify strategies to improve data dependability, efficiency, quality, and data governance processes.
- Work closely with the rest of the IT team to manage the company’s infrastructure.
- Facilitate a smooth communication channel between vendors and internal systems while outsourcing data-related tasks.
- Create and test scalable Big Data ecosystems for organizations so that data scientists may run their algorithms on reliable and well-optimized data platforms.
- Identify jobs where manual participation can be eliminated with automation.
- Enable and run Data Migrations across different Databases and Servers. For instance, Data Migration from SQL Servers to the Cloud.
- Replace outdated systems with newer or updated versions of current technologies to enhance Database efficiency.
- Conduct extensive testing and validation in order to support the accuracy of Data Transformations and Data Verification used in Machine Learning Models.
- Maintain the system using Kafka, Hadoop, Cassandra, and Elasticsearch.
- Employ disaster recovery techniques in case of mishaps.
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How to Become a Data Engineer?
Certifications alone will not get you a job in Data Engineering. To be considered for a position, you must also have prior experience. Other ways to get started in data engineering include:
- University degrees: Bachelor’s degrees in Applied Mathematics, Computer Science, Physics, or Engineering are all useful for aspiring Data Engineers. Additionally, Master’s degrees in Computer Science or Computer Engineering can help candidates stand out.
- Online courses: Online courses that are inexpensive or free are a good way to learn Data Engineering skills.
- Project-based learning: The first step in this more practical approach to learning data engineering skills is to set a project goal and then determine which skills are required to achieve it. The project-based approach is an effective method for maintaining motivation and structuring learning.
What Tools Do Data Engineers Use?
There are no one-size-fits-all tools data engineers use. Instead, each organization leverages tools based on business needs. However, below are some of the popular tools data engineers use.
- Data Processing
- Programming Languages
- Data Migration & Integration
- Distributed Systems
- Data Science & Machine Learning
SQL remains central to everything in our fast-paced world, where tools and technologies are constantly evolving, and is a foundational tool for data engineers.
NoSQL databases are non-tabular and, depending on their data model, can take the form of a graph or a document. MYSQL, PostgreSQL, and Oracle are examples of popular SQL databases. Popular NoSQL databases include MongoDB, Cassandra, and Redis.
2) Data Processing
Data Engineers are responsible for creating real-time data streaming and data processing pipelines. Apache Spark is a real-time stream processing analytics engine. Apache Kafka, a popular tool for building streaming pipelines, is used by more than 80% of Fortune 500 companies.
3) Programming Languages
To create software solutions to data challenges, Data Engineers must be fluent in at least one programming language. In the Data Engineering community, Python is widely regarded as the most popular and widely used programming language.
4) Data Migration & Integration
The processes involved in moving data from one system or systems to another without compromising its integrity are referred to as Data Migration and Integration. Data integration, in particular, is the process of combining data from various sources in a meaningful and valuable way.
Striim is a well-known real-time data integration platform that data engineers use for both Data Integration & Migration.
5) Distributed Systems
Distributed systems are those systems that collaborate to achieve a common goal while appearing to the end-user as a single system. Hadoop is a well-known data engineering framework for storing and processing large amounts of data across a network of computers.
6) Data Science & Machine Learning
Data Engineers must have a basic understanding of popular data science tools in order to better understand the needs of data scientists and other data consumers. PyTorch is a free and open-source Machine Learning library for deep learning applications that run on GPUs and CPUs. TensorFlow is a free and open-source Machine Learning platform that allows teams to build and deploy machine learning-powered applications.
What are the Key Skills for Data Engineers?
A Data Engineer must have a flair for problem-solving, attention to detail, critical thinking, and excellent communication. However, they should possess technical skills that will help them excel in this profession. Even if they lack the technical skills, with continuous practise and dedication, they can be mastered. The major areas of expertise must include:
1) Information Security
With the growing threat of hacking, Data Engineers must be aware of Cloud Security best practices and leverage them in their Data Management. This involves dealing with Data Privacy in a dynamic regulatory environment, e.g., GDPR.
This is a computer programming language that is used for a variety of purposes. Because of its ease of use and vast libraries for accessing Databases and storage systems, Python has become a popular tool for doing ETL operations. Many Data Engineers prefer it over an ETL tool since Python is comparatively more versatile and powerful for these tasks. For instance, Pandas, a Python library, helps Data Engineers in cleaning and manipulating data.
For further information on Python, check out the official website here.
3) Technical Tools
A Data Engineer must be proficient in the following tools:
- Open Frameworks: Apache Spark, Hadoop, perhaps Hive, MapReduce, and Kafka.
- ETL (Commercial Platforms): Hevo Data, Informatica, SSIS, and DataStage.
- ETL (Open-Source): Airflow, Luigi, and dbt.
- Business Intelligence Tools: Looker, Mode, Power BI, and Tableau.
- Any of the Relational Database Management System (RDBMS) and NoSQL.
- Any Cloud Platforms, preferably AWS.
- Knowledge of SaaS, IaaS, and PaaS.
- Data Visualization: Hue and D3.js.
- Data Warehouse: AWS Redshift, Google BigQuery, and Snowflake.
- Operating Systems: UNIX, Linux, Solaris, and Windows.
Why the Critical Need for Data Engineering now?
Most businesses have completed a digital transformation over the last decade. This has resulted in unimaginable volumes of new types of data, as well as much more complex data at a higher frequency. While it was previously obvious that Data Scientists were required to make sense of it all, it was less obvious that someone was required to organize and ensure the quality, security, and availability of this data in order for the Data Scientists to do their jobs.
As a result, in the early days of Big Data Analytics, Data Scientists were frequently expected to build vital infrastructure and data pipelines. This was not necessarily in their skill sets or job expectations. As a result, data modeling would be incorrect, resulting in redundancy and inconsistency. These kinds of issues prevented businesses from getting the most out of their data projects, and as a result, they failed. It also resulted in a high rate of Data Scientist turnover, which persists to this day.
With the advent of Corporate Digital Transformations, the Internet of Things, and the race to become AI-driven, it is evident that organizations require a large number of Data Engineers to lay the groundwork for successful data science initiatives.
The popularity of Data Engineer roles can be attributed to the growth of Open-Source platforms and increasing migration to the Cloud. With more data being generated at various endpoints, companies need Data Engineers to organize, assess, and maintain Big Data Architectures. It also includes creating Data Pipelines for other Data Professionals and this is where Hevo saves the day.
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