Data Engineering Skills: 8 Skills Required for Big Data Engineers
Data engineers build and maintain database systems. They have a firm foundation in programming languages, skills in finding warehousing solutions and hands-on exposure to big data tools.
Data Engineers play a very important role of ensuring that the data scientists and other stakeholders have the data that is accurate, reliable and accessible at whatever cadence is required for them to their analysis or to implement solutions. This requires advanced skills in ETL (extract, transform, and load) systems, and basic knowledge of machine learning and algorithms.
Data engineering is closely tied to data science and software engineering, and you will work in close collaboration with data architects, data scientists, machine learning engineers, and CTOs.
Demonstrating that you have the right set of skills is how you get hired. In this article, we will take a closer look at the technical skills needed to transition into data engineering.
Essential Technical Skills for Data Engineering Jobs
As an aspiring data engineer, you’ll want to show a few key skills to employers. Here we list the critical skills that one should possess to build a successful career in Data Engineering.
High-level programming languages are the skills needed to grasp data engineering and pipelines. Java and Scala are used to write MapReduce jobs on Hadoop; Python is suitable for data analysis and pipelines, and Ruby is also a popular application glue across the board.
Data engineering heavily relies on programming languages, especially for statistical modeling and analysis, data warehousing, and building pipelines. Approximately 70% of the data engineering roles have Python as the required skill, followed by SQL, Java, Scala and other programming skills like R, Perl, Shell Scripting, etc.
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Relational and non-relational databases
Companies depend on database-centric data engineers to carry out tasks distributed across several databases. Data Engineers focus on analytics databases and work closely with data solutions architects, data scientists, working on data warehouses, and developing table schemas.
Data engineers should have a deep knowledge of database management and should be conversant with both relational and non-relational databases.
SQL databases are relational databases and NoSQL databases can store non-relational data.
- SQL–Structured query language (SQL) is used for carrying out ETL system tasks. It is the database programming language for querying relational databases, and a valuable part of data engineering.
- NoSQL—can store non-relational data such as document stores (JSON format) in MongoDB, and graph databases that use nodes to store data entities in Neo4j.
SQL and NoSQL skills are required for most data engineering roles, followed by AWS Redshift, MongoDB, AWS S3, Cassandra, GCP BigQuery, etc.
ETL (extract, transform, and load) systems
ETL—Extract, Transfer, Load is the process through which you’ll raw data from databases and other sources into a single repository, like a data warehouse or business intelligence platforms.
Data engineering makes life a lot easier for data science teams through ETL. It is the process that helps extract the right data to analyze and solve problems, making it a must-have skill for data engineers.
Some of the popular ETL tools used by Data engineers are Xplenty, Informatica, Stitch, PowerCenter, AWS Glue, Alooma, and Talend.
Data Warehousing Solutions
Data warehouse and ETL help organizations leverage big data in a purposeful manner. Big data projects can be enormously complex and very time-consuming. They require extensive contributions from data engineers to work on the huge amounts of data and skills in finding data warehousing solutions.
Data warehouses store enormous volumes of current and historical data and since this data is ported from extensive sources like CRM system, accounting software, and ERP software, most companies require data engineers to be familiar with the data storage tools. So that stakeholders can use the data for reporting, analytics, interactive visualizations and data mining.
Most employers expect entry-level engineers to be familiar with Amazon Web Services (AWS), a cloud services platform with an entire ecosystem of data storage tools like MarkLogic, Oracle, Amazon RedShift, etc.
Big data tools
Data engineers don’t just work with the regular data. Big data engineers work with a massive amount of both structured and unstructured data. Since big data is a crucial tool for data science and Machine learning teams, it becomes important for data engineers to know how to store, process, clean, and extract information from the data.
Big Data technologies, such as Apache Spark, Apache Kafka, Hadoop, Hive, ELK Stack, great_expectations, Segment, Snowflake, Cassandra, are in high demand and data engineers know how to work with these tools to handle huge data sets and identify patterns and trends within them.
Real-time processing frameworks: Some frameworks to know are Apache Spark, Hadoop, Apache Storm, Flink, and more.
- Data ingestion: It is one of the crucial parts of data engineering skills. Data ingestion is the process of moving data from one or more sources to a destination source where it can be analyzed adequately. Commonly used tools for data ingestion are Apache Kafka, Apache Storm, Apache Flume, Apache Wavefront, Sqoop, and more.
- Data buffering tools: Data buffering is important where streaming data is increasingly spawned from thousands of data sources. Commonly used tools for data buffering are Redis Cache, Kinesis, GCP Pub/Sub, and so forth.
Data engineering is an advanced profession and in-demand job among the big data domain. So, if you are planning to get into Data Engineering, equip yourself with the functional knowledge of important tools like Apache spark.
Data Engineers solve complex problems to provide accurate, high-quality, and complete data to all stakeholders, in a way that is easy to access.
Data Engineering benefits of automating tasks in six key areas:
- Data Collection: Reliability, accuracy, and coherence of data are indicators of effective data collection. Automation supports the process, ensuring event-driven data is delivered persistently and ETL pipelines are strong and run on a dependable schedule.
- Data Sanitization: Accuracy and consistency indicate successful data sanitization, with data engineers removing private or sensitive information. Automation ensures private data isn't supplied and only appropriate stakeholders have access.
- Data Cleansing: The most time-consuming process in data engineering, accuracy, and consistency are key indicators. Automation inspects records, makes updates, removes unnecessary data, and corrects bad data.
- Data Warehousing: Automation ensures consistency and accuracy in the process, delivering and storing data logically according to schedule.
- Data Integration: Used for activation to deliver data closer to real-time than warehousing, automation ensures connecting data between systems and all tools and teams working with the same data.
- Reverse-ETL: Extending data integration that relies on warehousing, automation ensures data sent via reverse-ETL contains desired insights and faults don't augment negative impacts.
Data engineers have long used automation in various tools to help with these tasks and application testing to meet the stringent requirements.
Machine Learning and Algorithms
Data engineers need ML skills and knowledge of algorithms to do their job. They focus on creating end-to-end pipelines, ETL tasks, and regression, classification, and clustering.
Faulty data can lead to models learning incorrect patterns, regardless of the modeling techniques used. Data Engineers create the necessary groundwork for scientific research, allowing stakeholders to gather and analyze raw data from multiple sources and formats.
Having a basic understanding of ML concepts is essential for comprehending the needs of data scientists and ML engineers. Data Engineering enables ML, data exploration, and other analytical projects with large datasets. It is important for data engineers to have knowledge of supervised/unsupervised learning and clustering, as well as using the k-means clustering algorithm with Spark MLlib and other big data tools.
Data engineering and cloud computing are intertwined, with Big Data's rise leading to organizations preferring cloud-based solutions like Hadoop on AWS, Dataproc on Google Cloud, or Azure Databricks for efficient data storage, processing, and handling.
Data is critical for every company, so companies often use hybrid cloud, with prominent cloud platforms for Data Engineering such as AWS, Azure, GCP, OpenStack, OpenShift, and more.
Data engineers must be highly skilled in building cloud computing solutions at scale and applying data engineering skills to develop distributed systems with software engineering best practices for Big Data solutions, as well as continuous deployment, code validation, logging, instrumentation techniques, and monitoring.
Learn Kubernetes; it's popular in Data Engineering and a highly sought-after skill by organizations worldwide.
FAQs about Data Engineering Skills
We've got answers to your most frequently asked questions.
How to prepare for a data engineering interview?
Data Engineers need math skills, but it's not the core. It can be useful in following steps to solve problems and coding regularly encourages logical thinking.
The more data engineering experience you gain, the more logical reasoning you'll apply to problem solving and developing solutions that don't exist, with the ability to think critically.
What security skills does a data engineer need?
Some companies have dedicated data security teams, but many data engineers are still responsible for securely managing and storing data to protect it from loss or theft.
Security is essential to data engineering due to applications often being available over networks and connected to the cloud, vulnerable to security threats and breaches.
Data Engineers learn to do basic security testing as part of the development process to ensure no security vulnerabilities in a new or updated version of an application, ensuring compliance with security criteria.
Is data engineering part of data science?
Data science is highly collaborative, with data engineers working with various stakeholders.
Data Engineers develop data collection processes, integrate new technologies into existing systems, and streamline systems for data collection and analysis.
- Data Science uses a scientific approach to extract actionable business insights from data for decision-making.
- Data Engineering involves designing, creating, building, and maintaining data pipelines to collect and combine raw data from various sources, ensuring optimization.
Can I get a job with data engineering certification?
Learning from the best is the best way to refresh your knowledge, and building specialist skills is a strong bet.
Quality Data engineering courses can teach you advanced cloud computing and DevOps, as a starting point for your data engineering career.
Data Engineers need more than engineering skills; like software engineers, they have a strong grasp on logic and how it works. They evaluate by breaking things down into pieces and then combining them to create a creative and effective solution.
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