This article is aimed at examining the role of a big data engineer, how data is collected, handled, stored, and analyzed, and giving you a better idea of whether or not this career is right for you.
What Is Big Data?
The term “big data” refers to extremely large amounts of operational, product, and customer data, typically in the terabyte and petabyte ranges. Additionally, big data analytics can be used to reduce compliance and regulatory risks, improve important company and operations use cases, and generate entirely new sources of income.
The following list of data sources:
- POS (point-of-sale) transactions and credit cards;
- digital transactions;
- engagements on social media;
- engagements with smartphones and mobile devices; and
- readings from sensors produced by the Internet of Things (IoT).
Big data can provide insights into things like:
- optimizing important operational and business use cases;
- reducing the risk of non-compliance with regulations;
- generating net new sources of income; and
- creating distinctive, compelling customer experiences.
What Is A Big Data Engineer?
A big data engineer is a specialist in charge of creating, maintaining, testing, evaluating, and maintaining the data for a company. Very large data sets are referred to as big data. Large amounts of data are frequently gathered by businesses in the economic system as they carry out their daily operations.
Additionally, big data can be incredibly helpful for businesses to increase productivity, profitability, and scalability when used properly. But without a big data engineer to create systems to gather, maintain, and extract data, a company’s big data is useless. Therefore, big data engineers are ultimately responsible for assisting businesses in managing their big data.
What Does a Big Data Engineer Do?
A big data engineer’s responsibility is to create, maintain, and guarantee a big data environment that is ready for production. The environment in which this role operates will include architecture, technological norms, open-source options, as well as procedures for data management and data preparation. Big data engineers typically perform all of the following duties:
- Design, build, and maintain systems for processing large amounts of data. This gathers information from various sources, whether structured or not.
- Data should be kept in a data lake or warehouse.
- Utilize data processing transformations and algorithms to handle raw data and produce predefined data structures. Additionally, they put the outcomes in a data lake or warehouse for later processing.
- Put various data through transformation and integration into a scalable data repository (such as a data warehouse, data lake, or cloud).
- Recognize the various tools, techniques, and algorithms used in data transformation.
- Implement business logic and technical processes to transform the gathered data into insightful and useful information. For operational and business use, this data must satisfy the requirements for quality, governance, and compliance to be trusted.
- Understand the distinctions between data repository structures, massively parallel processing (MPP) databases, and hybrid clouds, as well as operational and management options.
- Data pipelines should be analyzed, compared, and improved. Innovation in design patterns, data lifecycle design, data ontology alignment, annotated data sets, and elastic search techniques are some examples of this.
- To convert and feed the data into development, quality assurance, and production environments, prepare automated data pipelines.
- Creating and putting into use software systems.
- Establishing systems for data collection and processing.
- Extraction, Transformation, and Loading (the ETL process).
- Constructing data architectures that satisfy business needs.
- Examining novel approaches to gathering important data and enhancing its quality.
- Developing structured data solutions with a variety of tools and programming languages.
- Mining information from various sources to create effective business models.
- Cooperating with other teams, data scientists, and analysts.
How to Become a Big Data Engineer
In order to become a big data engineer, most people must go through a number of steps.
#1. Obtain a Degree:
A degree in computer science, statistics, or business data analytics is required to master the technical skills necessary to become a big data engineer. For these positions, which require a mastery of coding, statistics, and data, the majority of employers demand a bachelor’s degree.
#2. Gain Work Experience:
An important qualification for becoming a big data engineer is experience. Additionally, you can acquire experience through freelancing, internships, independent practice, or employment in related fields. Your chances of landing a job as a big data engineer increase with experience.
#3. Get Certifications:
To land a job as a big data engineer, professional certifications can also be very helpful. For those aspiring big data engineers, any of the following certifications can be useful:
- Cloudera Certified Professional (CCP) Data Engineer
- Certified Big Data Professional (CBDP)
- Google Cloud Certified Professional Data Engineer
- IBM’s Data Science Professional Certificate
The Best 10 Tools for Data Engineers
#1. Python:
Python is a popular programming language in the field of data engineering, and it is used for many different things like creating data pipelines, ETL frameworks, interacting with APIs, automating processes, and data munging.
Additionally, Python is an essential option for more than two-thirds of job listings for data engineers due to its straightforward syntax and abundance of third-party libraries, which cut down on development time and costs.
#2. SQL:
SQL is essential for data engineers because it makes it possible to create reusable data structures, run complex queries, and model business logic. Additionally, it makes it easier to access, insert, update, manipulate, and modify data using a variety of methods.
#3. PostgreSQL:
The most widely used open-source relational database in the world is PostgreSQL, which has a vibrant community and a compact, adaptable, and powerful design. Additionally, it is perfect for data engineering workflows because it has built-in features, a large data capacity, and reliable integrity.
#4. MongoDB:
MongoDB is a popular NoSQL database that handles structured and unstructured data at a high scale. It is easy to use, highly flexible and offers features like distributed key-value stores, document-oriented NoSQL, and MapReduce calculation. Additionally, MongoDB is ideal for processing large data volumes and preserving functionality while allowing horizontal scale.
#5. Apache Spark:
Businesses need to capture and make data available quickly. Apache Spark is a popular implementation of Stream Processing, allowing real-time querying of continuous data streams. Additionally, it supports multiple programming languages, uses in-memory caching, and optimizes query execution.
#6. Apache Kafka:
Apache Kafka is an open-source event streaming platform with various applications, including data synchronization, messaging, and real-time streaming, popular for ELT pipelines and data collection.
#7. Amazon Redshift:
A prime example of how modern data infrastructures have advanced beyond storage functions is Amazon Redshift. Additionally, it makes using standard SQL to query and combine structured and semi-structured data from data lakes, operational databases, and data warehouses easier.
#8. Snowflake:
Snowflake is a cloud-based data warehousing platform offering storage, computing, third-party tools, and data cloning. Additionally, it streamlines data engineering activities by ingesting, transforming, and delivering data for deeper insights, allowing data engineers to focus on other valuable tasks.
#9. Amazon Athena:
Amazon Athena is an interactive query tool for analyzing unstructured, semi-structured, and structured data stored in Amazon S3 using standard SQL. Additionally, data engineers and SQL-skilled individuals can quickly analyze large datasets thanks to their serverless nature, which eliminates the need for infrastructure management and complex ETL tasks.
#10. Apache Airflow:
Data management between teams is a challenge for contemporary data workflows. Workflows are streamlined, repetitive tasks are automated, and job orchestration and scheduling tools like Apache Airflow help eliminate data silos. This tool is a favorite among data engineers because it provides a rich interface for visualization, progress monitoring, and problem-solving.
How Hard Is Big Data Engineering?
Being a data engineer can be challenging, to be honest. But once you’ve mastered the essential abilities and secured your first position, you’ll enjoy considerable freedom to craft your ideal position. Rarely will you be told what tools to use, and you’ll get to decide what you’ll be working on and when.
Is Working As A Big Data Engineer A Good Career?
Data engineering is a lucrative profession. According to Glassdoor, the average salary in the US is about $115,000, but some data engineers make up to $170,000 annually.
Is Big Data Difficult to Learn?
Data science is a broad field that may initially seem overwhelming. The skills needed for big data can be learned more quickly and effectively with perseverance, focus, and a solid learning roadmap.
Does Data Engineering Require a Lot of Math?
Math is a big part of data science. Data engineers, on the other hand, focus primarily on the technical aspects of creating data pipelines. The fact that both of these roles deal with big data is what unites them. It frequently takes a large team to work with big data.
Do Big Data Engineers Code?
Coding is a necessary skill for data engineers, just like it is for other data science positions. Other programming languages are used by data engineers in addition to SQL for a variety of tasks. Python is undoubtedly one of the best programming languages for data engineering, though there are many others.
Does Big Data Require Coding?
Coding expertise has historically been necessary for data science positions, and the majority of current data scientists with experience still use it. But as the field of data science evolves, people are now able to accomplish large data projects without writing any code, thanks to new technologies.
What is the Job Description of a Big Data Engineer?
A big data engineer is needed to develop and manage a company’s Big Data solutions, including designing tools, implementing ELT processes, collaborating with development teams, building cloud platforms, and maintaining production systems.
Additionally, you need in-depth knowledge of Hadoop technologies, first-rate project management abilities, and advanced problem-solving abilities to succeed as a big data engineer. A top-notch big data engineer is aware of the company’s requirements and implements scalable data solutions to meet both its present and future needs.
What is the Salary Big Data Engineer?
Big data engineers make an average salary of over $130,000, according to ZipRecruiter. Big data engineers with extensive experience and in the later stages of their careers can earn significantly more. However, those who are new to the industry and lack significant experience can anticipate making less money.
Big Data Engineer Jobs
Here are a few big data job examples to think about:
#1. Big Data Tester:
Average salary: $33,000 per year
A quality assurance (QA) analyst and a big data tester are similar. They evaluate data plans to aid in the distribution of data-related goods. Additionally, they can create, run, and analyze test scripts as well as data execution scripts. Big data testers also specify and monitor QA metrics like test results and defect counts.
#2. Technical Recruiter:
Average salary: $54,000 per year
A technical recruiter aids businesses in determining their hiring requirements and locating aspirants for big data positions. Additionally, they look for candidates on the market to screen, interview, and hire. The hiring process may also benefit from the assistance of technical recruiters.
#3. Database Manager:
Average salary: $65,000 per year
Database managers are technically talented individuals with a broad understanding of database technology. They take care of project management duties and upkeep the database environment. Additionally, a database manager frequently handles a variety of common management responsibilities, including managing personnel issues, leading the data team, and adjusting budgets.
#4. Data Analyst:
Average salary: $74,000 per year
Data analysts are people who analyze data systems and solve problems. They frequently design automated tools that search databases for data. Data analysts may work alone or in groups, and they frequently compile reports.
#5. Big Data Developer:
Average salary: $83,668 per year
Like a software developer, a big data developer creates data. They finish programming and coding applications as well as creating and putting into use pipelines that extract, transform, and load data into a final product.
Additionally, a developer might also help with the development of scalable, high-performance web services for data tracking. To develop more efficient methods, a few big data developers also investigate and examine fresh approaches to issues like storing or processing data.
#6. Data Governance Consultant:
Average salary: $95,000 per year
A data governance consultant creates frameworks to safeguard and control the use of data. This includes having an impact on how data assets are gathered, managed, used, and archived. Additionally, they supervise practices and regulations and guarantee that data usage complies with set standards.
#7. Database Administrator:
Average salary: $96,000 per year
The daily operations of a database record are managed by database administrators. This entails preserving database backups and making sure the database is stable. Furthermore, updates and modifications to databases are also carried out by database administrators.
#8. Security Engineer:
Average salary: $107,000 per year
IT needs security engineers to lower corporate risk exposure. For computer networks, they develop multi-layered defense protocols, such as installing firewalls and keeping an eye out for and responding to intrusion attempts. Additionally, to find problems and develop and carry out test plans for software updates, security engineers evaluate security systems.
#9. Data Scientist:
Average salary: $122,000 per year
Data scientists collaborate closely with corporate business operations. Additionally, they gather, examine, and interpret data, then present their conclusions to business executives. Data scientists provide advice to businesses to aid in decision-making on the basis of their findings and trends.
#10. Data Architect:
Average salary: $130,000 per year
To develop business strategies and database solutions, data architects combine their inventiveness with a comprehensive understanding of database design. Additionally, to help the business achieve its goals, they work with data engineers to develop data workflows. New database prototypes are also created and evaluated by a data architect.
Related Articles:
DATA SCIENTIST SALARY: Average Data Scientists Pay 2023
Database and Data Warehouse: Whats the Difference?
DATA STANDARDIZATION: Definition, Process & Why It Matters
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