The world’s most valuable currency is no longer oil. Data has replaced it. Consequently, the need for engineers to control and manage this data so that data scientists can analyze it has increased exponentially along with the rapid growth of the data volumes that the business organization must collect, load, and analyze to create meaningful information used for strategic decision-making. In other words, data engineers play a fundamental and vital role in the overall data analysis and management lifecycle.
The data engineer versus the data scientist
In his article titled “Data Scientist vs Data Engineer, What’s the difference,” Saeed Aghabozorgi describes data engineers as “data professionals who prepared the Big Data infrastructure to be analyzed by data scientists.”
In other words, data engineers build the infrastructure upon which all data science projects depend.
Data engineers are software engineers responsible for designing, building, integrating the voluminous data from multiple sources, and managing the Big Data. They also write the complex queries needed to make the data easily accessible to the data scientist. Finally, the data engineer is responsible for maintaining and optimizing the Big Data ecosystem.
Juxtapositionally, as described by Aghabozorgi, the data scientist is the “alchemist” of the twenty-first century and is responsible for transforming prepared data into information to build models that drive the strategic decision-making process.
While data science as a discipline is not new, it can be seen as an advanced level of data analysis driven by computer science and machine learning. Therefore, as with data engineers, data scientists need strong software development skills to design new data manipulation and analysis algorithms and handle the rigors of the Big Data ecosystem. In summary, the data engineer is responsible to prepare the data as a foundation for the data scientist to analyze this data.
Data science as a discipline is not new. It can be seen as an advanced level of data analysis driven by computer science and machine learning. As a result, data scientists are experts in mathematics and statistics. Therefore, they do not need strong software development skills.
The Most Popular Data Engineering Tools & Programming Languages for 2021
Even though we have described the role and function that the data scientist plays in the Big Data ecosystem, this discussion’s core focus is the data engineer and not the data scientist.
Because the data engineers are responsible for designing and managing data flows that integrate data from numerous sources into a shared pool such as an AWS data lake and the setting up data pipelines, they require specific tools as well as the knowledge of relevant programming languages to perform their core role.
Consequently, let’s consider several software tools and programming languages most commonly used in data engineering.
1. Amazon Athena
Amazon Athena is one of the most widely growing services in the Amazon Cloud, often used as part of a cloud data lake for ad-hoc querying, analytics, and data science on both structured and semi-structured data.As stated on the Amazon Athena website, “Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”
2. Amazon Redshift
Amazon Redshift is a cloud data warehouse that data engineers can use to combine (and query) exabytes of structured and unstructured data stored in the data warehouse, operational database, and data lake using standard SQL.
Additionally, Redshift saves the results of all queries run in the Amazon S3 data lake using open-source formats. Once the results of these queries have been saved, additional analytics operations can be run on these results using other analytic services like Amazon Athena.
3. Apache Spark
The Apache Spark website defines Spark as an open source “unified analytics engine for large-scale data processing.” It runs on multiple platforms, including Hadoop, Apache Mesos, Amazon EC2 (Amazon Elastic Compute Cloud), Kubernetes, as well as hundreds of other data sources.
Spark runs workloads faster, using a DAG (Directed Acyclic Graph) scheduler, a query optimizer, and a physical execution engine. Lastly, data engineers can use programming languages like Java, Scala, Python, R, and SQL to write parallel applications querying batch and streaming data.
4. Apache Hadoop
Hadoop is another open-source software platform that manages data storage and data processing for Big Data. It is based on a distributed architecture, distributing large datasets and analytics workloads across multiple nodes in a computing cluster. These nodes are run in parallel. Thus, Hadoop can store and process both structured and unstructured data. Finally, it has the capacity to scale up from one to thousands of servers reliably and robustly.
5. Apache Kafka
Real-time data streaming is now an integral part of the Big Data ecosystem. The world generates massive volumes of data every minute of the day. Streaming data is the continuous flow of data generated by sources like computer server event logs, networks, banking transactions, and IoT data. In order to analyze this data in near real-time, this data is aggregated in a single pool so that it can be analyzed, generating real-time information.
Apache Kafka is an “open-source, distributed event or data streaming platform used for high-performance data pipelines, streaming analytics, and data integration.” It is written in Scala and Java, making integrating Kafka with other analytics platforms simpler and more efficient.
Python is one of the world’s most popular programming languages. It is known as the “lingua franca” of data science and is widely used for statistical analysis tasks. It is perhaps worth noting that Python and SQL are a requirement for over 67% of all data engineering jobs listed across the world.
Python is widely used in the data engineering community because it is easy to learn and read. And it has the capacity to interface with algorithms written in languages like C. Lastly, with the rapid advancement in AI (Artificial Intelligence), predictive analytics, and machine learning; there is a rising demand for data engineers with advanced Python skills and experience.
7. Structured Query Language (SQL)
Infoworld.com describes SQL as the “lingua franca of data analysis.” Even though it is not the most elegant of the fastest way to communicate with databases, it is the industry standard when it comes to creating, manipulating, and querying data in relational databases. However, the question is, why is SQL the industry standard when it is relatively slow and memory intensive?
The straightforward answer to this question is the ease of use and portability.
SQL, that language can be subdivided into three sub-languages.
Data Definition Language (DDL) is used to define data schemas, such as database tables.
Data Manipulation Language (DML) is used to modify the data in the database.
Declares queries such as the SELECT statement and relational joins.
The data engineering landscape is evolving rapidly, increasing the number of tools used to create data pipelines and integrating multiple data sources into a single data warehouse or data lake. This article provides a list of the 11 most widely used programming languages and data engineering tools that are useful to the data engineer, making the job of managing the masses of data that must be aggregated, stored, analyzed, and managed quite a bit easier than it would be without these tools. Suffice to say, these tools and programming languages are imperative to the data engineer’s operational success.