The handling and processing of data between the data creation/capture and data science/analysis stages is known as data engineering. Data engineering employs technical methodologies and techniques to enhance internal operations and communication. An organization's data pipeline moves through the steps of ingestion, processing, storage, and access. Data engineers make ensuring that raw data are available for data scientists and other organizations to use practically. A subset of data science is frequently used to describe data engineering.
Top projects you may like
Programming & Tech
Data Engineering FAQs
What does a data engineer do?
In the past decade, data science has seen soaring demand, and with it comes an increasing need for data scientists. Today, businesses need something beyond the power of traditional database management to come out on top. It is evident that where there is data, there is a data engineering service.
Freelance data engineering experts will work on the execution of programs and data storage, design data pipelines, and be in charge of the operating system. They are also responsible for processing, formatting, and optimizing the data flows that meet user needs. A data engineer must ensure that all data received is safe and high-quality for firms to make use of.
Freelance data engineering developers have many responsibilities that reflect one or more of the above facets. In short, it is a highly specialized job, so you must have a firm understanding of programming languages, algorithms, and complex tools to achieve fruitful results.
What is the role of a big data engineer?
Data engineering services often focus on two main parts of the data platform: pipelines and platforms (infrastructure). An engineer with the full-stack title will do both. Below are some typical roles of big data engineers:
- ETL - Typically requested by the analytics team. For example, an analyst needed data from November to December 2022 for analysis but could not locate it in Data lake. The data engineer will find the source, which could be the Backend Database or a third-party API, and then sync it to the Data lake for the analyst to use.
- Data API. It functions similarly to a backend application. Engineers will create a Spark Job to retrieve data from the Data lake, process it as needed, and save the results in the database (Mysql, Postgres, Elastic Search). Finally, they create an additional Rest API to trigger Spark Jobs.
- Streaming data. The name is self-explanatory. Freelance data engineering developers will get real-time data from Kafka and feed it into the Data lake.
- Set up the operating system and keep Airflow running to load over 2000 tasks. Furthermore, because the system supports numerous teams, both technical and non-technical, engineers must configure CI/CD so that everyone can use Airflow easily.
- Build and maintain a data warehouse or "data lake". It is the most fundamental project that any data engineer must complete.
- People can handle a small quantity of data comfortably on their personal computers. But for an enterprise, especially those with strict security regulations and measuring data in Petabytes, a data engineer is needed to develop a separate system to handle this efficiently. Data engineering services also build other platforms such as Kafka, CI/CD, Hive, Presto, etc.
What should I learn to become a data engineer?
Data engineering is a subset of data science, an umbrella term for many types of data-related work. Freelance data engineering is the design and development of systems that assist customers in collecting, storing, and analyzing data. All businesses, whether large, small, or medium-sized, require a large data warehouse to function. At that point, data engineering services will assist them in working with data.
Data engineering is a highly sought-after job in the technology industry, ahead of computer scientists, web designers, and database architects. Experts believe the demand for freelance data engineering will continue to rise in the coming years. In addition to accumulated knowledge and experience, you will need the following skills and qualities to pursue a career as a data engineer:
- The first skill you must possess, of course, is programming. Python is a great programming language to start with.
- Tools for freelance data engineering: Numpy, Pandas, Matplotlib, and Scikit-learn are examples of Python libraries. Aside from them, there are SQL, Spark, R, and higher-level tools such as H2O and tableau.
- Machine learning applications: Tensorflow, Keras, and Pytorch are a few examples.
- Data processing frameworks competency.
- Soft skills are also essential besides technical skills. This profession necessitates clear, coherent thinking and obviously familiarity with data engineering.
What makes a good data engineer?
Freelance data engineering has specific requirements to climb the ladder. Before anything, you must fulfill two conditions to become a certified data engineer:
- Technical qualification: Certification in computer science or information technology.
- Technical requirements include a background in SQL and NoSQL. Data engineers understand data flow and can create and integrate APIs.
More specifically, you should be familiar with other programming languages that aid in statistical analysis and modeling, such as Python or R. Mastering Spark, Hadoop, and Kafka will also benefit your career.
A decent data engineer has other skills besides programming language proficiency, such as effectively using database architecture, finding data storage solutions, building data flow, using the cloud, and so on.
To advance in freelance data engineering, you should learn and obtain the necessary certifications to back your abilities and knowledge. Cloudera Certified Professional Data Engineer (CCP), Google Cloud Certified Professional Data Engineer, and Certificate in Engineering Excellence Big Data Analytics Optimization (CPEE) are some certifications to consider.
If possible, you should pursue a master's degree in computer science, computer engineering, or other related fields. You can improve your professional knowledge and skills by attending schools, which assists you in broadening your path to running a successful data engineering service.
Is data engineering a good career?
Data engineering falls into three sections: data creation and management, data analysis, and results evaluation. Every business relies heavily on data - they make quick and accurate decisions based on the data they sweep up. Besides, the world has seen a growing number of customers using products/services that results in a vast amount of data. For that reason, hiring freelance data engineering experts is more critical than ever.
Businesses today are looking for flexible, low-cost, and scalable data storage and management solutions. To accomplish this, they must create a Data lake to supplement or replace an existing data warehouse. As a result, freelance data engineering will have an adequate development path for you. As a data engineer, you will have the chance to interact and work with various companies or multinational corporations.
Data engineering is a brain-intensive job that requires less effort than manual labor. However, while this is a potentially lucrative job, the pressure of speed and accuracy necessitates a person who can manage a heavy workload.
In short, freelance data engineering will always be an industry that holds significant potential. In addition, it is a hot field that lacks talented persons with high levels of the needed skills.
What are the key fields and processes in data engineering?
Extracting, loading, and transforming (ELT) data are central to the two most common data engineering processes. First, data engineering services must always extract data in some way from a single source, but what happens next is not so straightforward.
ELTs are common in Data lake stores or architectures that require raw extracted data from multiple sources. It enables different processes and systems to process data from a single extraction. When combining data from various sources, it is advantageous to co-locate and store the data in a single location before converting it.
Meanwhile, the ETL (extract, transform, load) process heavily computes the conversion before loading the results into a file system, database, or data warehouse. Compared to the above process, it is not as efficient because each batch or stream frequently requests data from dependent systems. It means you have to re-query data from them, add load to those systems, and wait for data to become available on each execution.
However, in cases where simple transformations are applicable for a single data source, ETL may work better because it reduces system complexity at the expense of data exchangeability.