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Table of Contents
Who is a Data Engineer?
Every data-driven business requires a framework for data science and data analytics pipeline. The person responsible for building and maintaining this framework is known as Data Engineer. These engineers are responsible for an uninterrupted flow of data between servers and applications.
Therefore, a data engineer builds, tests, maintains data structures and architectures for data ingestion, processing, and deployment of large-scale data-intensive applications.
Data engineers work in tandem with data architects, data analysts, and data scientists through data visualization and storytelling. The most crucial role of the data engineer is to design, develop, construct, install, test, and maintain the complete data management and processing systems.
So what do they exactly do? They create the framework to make data consumable for data scientists and analysts so they can use the data to derive insights from it. So, data engineers are the builders of data systems.
Responsibilities of a Data Engineer
Data engineer manages this position by creating optimal databases, implementing changes in schema, and maintaining data architecture standards across all the business’s databases. Data Engineer is also responsible for enabling migration of data amongst different servers and different databases, for example, data migration between SQL servers to MySQL. He also defines and implements data stores based on system requirements and user requirements.
Data engineers should always build a system that is scalable, robust, and fault-tolerant hence, the system can be scaled up without increasing the number of data sources and can handle a massive amount of data without any failure. For instance, imagine a situation where a source of data is doubled or tripled, but the system fails to scale up, so it would cost a lot more time and resources to build up a system to intake this extensive data. Big Data Engineers have a role here: they handle the extract transform and load process, which is a blueprint for how the collected raw data is processed and transformed into data ready for analysis.
The Data Engineer performs ad-hoc analyses of data stored in the business’s databases and writes SQL scripts, stored procedures, functions, and views. He is responsible for troubleshooting data issues within the business and across the business and presents solutions to these issues. Data engineer proactively analyzes and evaluates the business’s databases in order to identify and recommend improvements and optimization. He prepares activity and progress reports regarding the business database status and health, which is later presented to senior data engineers for review and evaluation. In addition, the Data Engineer analyzes complex data system and elements, dependencies, data flow, and relationships so as to contribute to conceptual physical and logical data models.
Some of the other responsibilities also include improving foundational data procedures and integrating new data management technologies and the software into existing systems and building data collection pipelines and finally include performance tuning and make the whole system more efficient.
Data Engineers are considered the “librarians” of data warehouse and cataloging and organizing metadata, defining the processes by which one files or extracts data from the warehouse. Nowadays, metadata management and tooling have become a vital component of the modern-day platform.
Goals of a Data Engineer
Developing Data Pipelines
This skill set involves transferring data from one point to another. In other words, taking data from the operating system and then moving it into something that can be analyzed by the analyst or data scientist hence, leading to the next goal of managing tables and data sets.
Managing tables and Data Sets
The transferred data through pipelines populates some sorts of sets of tables that are then used by the analysts or data scientists to extract all of their insights from data. Analyzing information of any product, for example, a blog site with questions like what people are reading? How are they reading it? How long they are staying on particular articles.
Designing the product
Data Engineers end up playing an important role to understand what users want to gain from large datasets. Considering questions at the time of development, that users might have while using the product. E.g., developing a dashboard, how are people going to use the dashboard? What other features can be added and how far fetched they are.
Conceptual Skills Required to be a Data Engineer
The most required skill in data engineering is the ability to design and build data warehouses, where all the raw data is collected, stored, and retrieved. Without a data warehouse, all the tasks that data scientists do would become obsolete. It is either going to get too expensive or very very large to scale. However, other skills required are:
1. Data Modelling
The data model is an essential part of the data science pipeline. It is the process of converting a document of sophisticated software system design to a diagram that can comprehend, using text and symbols to represent the flow of data. Data models are built during the analysis and design phase of a project to ensure the requirements of a new application are fully understood. These models can also be invoked later in the data lifecycle to rationalize data designs that were initially created by the programmers on an ad hoc basis.
Stages in Data Modelling
- Conceptual: This is the first step in data model processing, which imposes a logical order on data as it exists in relationship to the entities.
- Logical: The logical modeling process attempts to impose order by establishing discrete entities, fundamental values, and relationships into logical structures.
- Physical: This step breaks the data down into the actual tables, clusters, and indexes required for the data storage.
Hierarchical Data Model: This data model array in a tree-like structure, one-to-many arrangements marked these efforts and have replaced file-based systems. E.g., IBM’s Information Management System (IMS), which found extensive use in business like banking.
Relational Data Model: They replaced hierarchical models, as it reduced program complexity versus file-based systems and also didn’t require developers to define data paths.
Entity-Relationship Model: Closely relatable to the relationship model, these models use diagrams and flowcharts to graphically illustrate the elements of the database to ease the understanding of underlying models.
Graph Data Model: It is a much-advanced version of the hierarchical model, which, together with graph databases, is used for describing the complicated relationship within the data sets.
Industries use automation to increase productivity, improve quality and consistency, reduce costs, and speed delivery. It provides benefits in greater magnitude to every team player in an organization including Testers, Quality Analysts, Developers, or even Business Users.
Automation can provide the following benefits:
- Speed: It is fast, hence, dramatically reduces team development time.
- Flexibility: Respond to changing business requirements quickly and easily.
- Quality: Automation tools produce tested high performance, complete, and readable code.
- Consistency: It is easy for a developer to understand another’s code.
In data science, designing a data warehouse and data warehouse architecture requires a long time to complete as well as semi-automated steps result in a data warehouse that was limited and inflexible. So, data engineers came up with a solution to automate data warehouse involving every step involved in its life cycle, thus reducing the effort required to manage it. The need for data engineers to implement data warehouse automation (DWA) tools is growing as these tools eliminate hand-coding and custom design for planning, design, building, and documenting decision support infrastructure.
3. Extraction, Transformation, And Load (ETL)
ETL is defined as the procedure of copying data from one or more source into the destination system, which represents the data differently from the source or in a different context than the source. ETL is often used in data warehousing.
Data extraction is the concept of extracting data from heterogeneous or homogenous sources; data transformation processes data by cleansing data and transforming them to proper storage structure for the purpose of querying and analysis, finally data loading describes the insertion of data into the final target database such as operational data store, a data mart, data lake or data warehouse.
In data science, ETL involves pulling out data from operational systems like MySQL or Oracle and moving it into a data warehouse like SQL server or modern-day data warehouses like Hadoop or RedShift and then format it in such a way that analyst can get it. Eventually, the ETL process starts at the analytical data layer that does more than extracting data, it performs things like aggregating data, running metrics and algorithms on the data so that it can be easily fed into future dashboards.
4. Product Understanding
Data engineers look at the data as their product, so it is made in such a way that users can use it. If we are building datasets for machine learning engineers or data scientists, we need to understand how they are going to use it what are the models that they want to build is enough information is being provided at the customer level. This is required because the data engineer looks at the things at the granularity and aggregate things themselves.
Becoming a Data Engineer
- Programming Language: Start with learning Programming Language, like Python, as it has clear and readable syntax, versatility, and widely available resources and a very supportive community.
- Operating System: Mastery in at least one OS like Linux and UNIX OS is recommended, RHEL is a prevalent OS adopted by the industry which can also be learned.
- DBMS: Enhance your DMBS skills and get your hands-on experience at least one relational database, preferably MySQL or Oracle DB. Thorough with database administrator skills as well as skills like capacity planning, installation, configuration, database design, monitoring security, troubleshooting such as backup and recovery of data.
- NoSQL: This is the next skill to focus as it would help you understand how to handle semi and unstructured data.
- ETL: Understand to extract data using ETL and data warehousing tools from various sources. Transform and clean data according to the user and then load your data into the data warehouse. This is an important skill which data engineers must possess. Since we are at the age of revolution where the data is the fuel of the 21st century, various data sources and numerous technologies have evolved over the last two decades major ones being NoSQL databases and big data frameworks.
- Big Data Frameworks: Big data engineers are required to learn multiple big data frameworks to create and design processing systems.
- Real-time Processing Frameworks: Concentrate on learning frameworks like Apache Spark, which is an open-source cluster computing framework for real-time processing, and when it comes to real-time data analytics spark stands as go-to-tool across all solutions.
- Cloud: Next in the career path, one must learn cloud which will serve as a big plus. A good understanding of cloud technology will provide the option of stable significant amounts of data and allowing big data to be further available, scalable and fault-tolerant.
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