Ten years ago, scripting meant writing in the Linux Bash shell or Perl, using a Linux cron job to schedule the script run, and collecting information from logs to see if there were any issues.
Today, there are many Data Engineer skills and techniques that a Data Engineer needs to know and languages around this, beyond just shell scripting.
Data Engineering with Python
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Java and C++ have been integral languages in the Data Engineering field for over a decade, serving as an important interface with the disparate data in the organizations systems. Many newer systems today require additional integration and utilizing programming languages such as Python, C#, Scala and Go is more prevalent.
Knowing these languages is a must have in your Data Engineering Skills set to work with real-time data like social media, email, controls, or cloud-based systems.
Additionally, ELT (Extract, Load and Transform) methods need to be in line for other data sources like CSV and databases. Programming means using repositories like Git for source control. Data Engineers should also know about Software Development Life Cycle (SDLC) and Continuous Development (CD) and Continuous Integration (CI) techniques and tools like Jenkins and GitLab in DevOps.
Why You Should Learn Go
Watch now and see for yourself why Go is the next-generation language
for today’s modern computer environment.
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Structured Query Language is 25 years young in 2021, and still a must have for Data Engineer skills.
Knowledge of Relational Database Management Systems (RDBMS) is key in this role still.
Not only SQL is the acronym for working with Data Stores that store data in unstructured or semi-structured (lacking a schema) ways. NoSQL invokes data in a hierarchical way, using clustered environments; many machines working in parallel. Open-source systems Apache Hadoop, HBase, Redis, MongoDB and Cassandra are all the rage in 2021.
Knowing how to manipulate key value pairs and object formats like JSON, AVRO or Parquet is necessary in your data engineer skillset for these.
6. Data Pipelines.
Processing data and ensuring the efficient moving of that desperate “Data Lake” data for future analysis and visualization is another key knowledge area. Operating with real-time streams, data warehouse queries, JSON, CSV, raw data is a daily occurrence.
Understanding which tools to use like Apache Kafka, Storm, Flume for ingesting data or Amazon Web Services (AWS) Cloud Development Kit (CDK) for on-premises to cloud is a must have data engineer skill.
Scripting and Data Pipelines need to run on their own jobs, either scheduled or invoked, to perform the tasks required to successful move data. Beyond cron jobs the Data Engineer must know about the integrated tools in many server environments to achieve this.
Exploratory Data Analysis (EDA) has been used in the realm of the Data Scientist in the past. Today, Data Engineers must also acquire these data engineer skills to be able to ensure ETL work mentioned earlier is successful.
Knowledge of terminology and data manipulation is key here as is utilization of Apache Spark engine with PySpark or Scala.
PySpark for Data Engineering and Machine Learning
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Understanding visualization techniques is a key success factor for Data Engineers now.
Data Engineers need to ensure data integrity throughout the ETL process and how to visualize the resultant data.
2. Machine Learning and AI.
Knowledge of terminology and familiarity with algorithms is becoming a more important part of the Data Engineer skillset.
Today knowing and utilizing Python’s libraries numpy, pandas, and sci-kit learn and even cloud based tools like AWS Sagemaker, Microsoft’s HDInsight, or Google’s DataLab should be part of the known data engineer skill sets.
1. Cloud computing.
As mentioned at the top of this article, the growth in cloud computing today is astronomical. Herein lies an issue though, which cloud technology to choose. According to Flexera, 76% of public cloud adoption in 2020 was AWS based with Microsoft slightly behind at 69% and Google a distant 34%.
Does that mean recommending only the top 3? Absolutely not!
A Data Engineer needs to have a good understanding of the underlying technologies that make up cloud computing and in particular, knowledge around IaaS, PaaS, and SaaS implementations.
Add this to your Data Engineer skills.
How can you, the data engineer, be successful with all these areas since “studies show that 73% of digital transformation efforts fail.” Gaining knowledge generally takes a long time, especially trying to do it all on your own.
A proper data engineer certification training program that plans out your schedule, is adaptable, uses real-world labs, and allows you to study with an experienced instructor is key to your success.
Now It’s Your Turn!
Get started with Data Engineer Skills Training today!
Learn to design data pipelines and APIs in the cloud, perform analytics in the cloud and automate this complete process flow.
At the end of the program, demonstrate your mastery by finishing a capstone project that combines all the critical concepts learnt.
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