Top 10 Skills for Data Engineer

1. Python:

To become a Data Engineer, you need to learn a programming language called Python. At no surprise, over 70% of data engineering job descriptions mention Python as a must have skill. Python libraries have helped improve data workflows and Python is easy to learn with simpler syntax as compared to Java OOP.

2. SQL

SQL is a data centered language. To be able to work effectively with data in any data science position, you need a basic skill of Structure Query Language (SQL). With more than half of job postings listing SQL as a skill at 56%, it is an important skill for data engineering jobs in 2021. Aside from being a core data science language in general, SQL is especially useful from a business point of view, such as being able to model business logic and create reusable data structures.

3. Cloud

Cloud technology is the trending infrastructural platform that require less capital to use, and easy access to data from any location. Migration of data from on premise to cloud is the new trend because cloud technology provides scalability, accessibility, security and so many advantages. Most mid-level to advanced data science positions require Cloud experience and listed in about 50% of the job descriptions recently posted on job boards. AWS and Azure Cloud are the dominant cloud platforms followed by Google cloud. Most employers seem to treat cloud platform skills as interchangeable or at least expect expertise on one platform to translate to another.

4. Big Data

Big data is a buzz word for large data sets and increasing in demand as Data Engineers encounter daily usage of data on the job. With expertise or knowledge of big data technologies like Hadoop etc, most organizations require such skill from data engineers.

5. Apache Hadoop 

Apache Hadoop has seen tremendous development over the past few years. Its components like HDFS, Pig, MapReduce, HBase and Hive are currently in high demand by recruiters. At 24% of job listings, the Apache Hadoop framework is an ecosystem in itself, as it’s actually a collection of open-source tools. It allows for the distributed processing of large data sets across clusters of computers using simple programming models. Apache Hadoop uses the MapReduce programming model with sever clusters for big data.

6. Apache Hive is data warehouse software that “facilitates reading, writing, and managing large datasets residing in distributed storage using SQL”.

7. Kafka 

Kafka is an open-source processing software platform using Scala and Java. It handles real-time data feeds and can connect to outside processing libraries. Engineers should understand Kafka’s architecture, how to use it, and how to integrate it with other libraries.

8. Apache Spark: In addition to the Hadoop framework, Apache Spark is also extremely popular in roles involving big data analytics. A quicker and more straightforward alternative for complex frameworks like MapReduce, many organizations are now expanding their operations and looking for professionals with experience in Spark. Moreover, the increase of Spark’s in-memory stack has also made this skill extremely sought after by headhunters of prominent consulting firms. With about 40% of data engineer job posts include Spark as a needed skill, it’s a good to have skill for data engineers. Considering data pipelines are a huge part of what makes a data engineer special, it makes sense that Spark – a framework built for data pipelines – come up frequently as a needed skill.

9. NoSQL databases stand in opposition to SQL. NoSQL databases are non-relational, unstructured, and horizontally scalable. NoSQL is quite popular, but previous hype of it displacing SQL as the dominant storage paradigm seems to be overblown.

10. ETL

ETL, also known as Extract, Transform, Load – appeared in about 70% of data engineering job postings. Simply described as the extraction of data, transformation of data and loading into required data source or linked source. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.

11. Scala

Scala is a general-purpose programming language often used in data processing libraries like Kafka, which is why it is essential for data engineers to know. Acting somewhat as a counterpart to Java, it is more concise and relies on a static-type system. Scala is programming language popular with big data. Spark was built with Scala.

12. Database skills and tools

Databases are the core of data storage, organization, and searching. Therefore it is extremely important to be familiar with their structure and language. There are two primary types of databases; structure query language (SQL)-based, and NoSQL-based. NoSQL is becoming increasingly popular, which is why engineers should be familiar with the types.

NoSQL databases include key-value cache (Ignite, Coherence, Hazelcast), key-value store (Aerospike), Tuple store (Apache River), object database (Prest, ZopeDB), document store (BaseX, Clusterpoint, IBM Domino), wide column store (Amazon DynamoDB, Cassandra), and native multi-model database (CosmosDB, MarkLogic).

13. Machine Learning

Machine learning (ML) is a critical tool for big data engineers, since it allows them to sort and process large amounts of data in a short period of time. As well, big data is a part of building machine learning algorithms, since they “learn” by processing data sets. Engineers should be familiar with the machine learning algorithm building process. They must know how to write them, and how to use algorithms in the process of data ingestion. 

Conclusively, if you want to become a data engineer, I suggest you learn the technologies mentioned in this video, in order of priority. Also learn SQL, NoSQL, Python, data APIs, Java, basics of distributed systems and knowledge of algorithms and data structures.

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