The Most Popular Data Engineering Tools
In this article, we’ll explore the role of the data engineer and the most popular data engineering tools available today.
The role of data engineer
As they’ve begun to realize how valuable the data housed in their computer systems can be, many companies are embarking on data science initiatives to develop innovative ways of leveraging that value. That’s why data engineering has become one of the most in-demand IT disciplines today.
Data engineers are the people who build the information infrastructure on which data science projects depend. These professionals are responsible for designing and managing data flows that integrate information from various sources into a common pool (a data warehouse, for example) from which it can be retrieved for analysis by data scientists and business intelligence analysts. This typically involves implementing data pipelines based on some form of the ETL (Extract, Transform, and Load) model.
Data engineering tools
In creating this information architecture, data engineers rely on a variety of programming and data management tools for implementing ETL, managing relational and non-relational databases, and building data warehouses. Let’s take a quick look at some of the most popular tools.
Data management tools
- Apache Hadoop is a foundational data engineering framework for storing and analyzing massive amounts of information in a distributed processing environment. Rather than being a single entity, Hadoop is a collection of open-source tools such as HDFS (Hadoop Distributed File System) and the MapReduce distributed processing engine. Precisely Connect is a highly scalable and easy-to-use data integration environment for implementing ETL with Hadoop.
- Apache Spark is a Hadoop-compatible data processing platform that, unlike MapReduce, can be used for real-time stream processing as well as batch processing. It is up to 100 times faster than MapReduce and seems to be in the process of displacing it in the Hadoop ecosystem. Spark features APIs for Python, Java, Scala, and R, and can run as a stand-alone platform independent of Hadoop.
- Apache Kafka is today’s most widely used data collection and ingestion tool. Easy to set up and use, Kafka is a high-performance platform that can stream large amounts of data into a target like Hadoop very quickly.
- Apache Cassandra is widely used to manage large amounts of data with lower latency for users and automatic replication to multiple nodes for fault-tolerance.
- SQL and NoSQL (relational and non-relational databases)are foundational tools for data engineering applications. Historically, relational databases such as Db2 or Oracle have been the standard. But with modern applications increasingly handling massive amounts of unstructured, semi-structured, and even polymorphic data in real time, non-relational databases are now coming into their own.
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- Python is a very popular general-purpose language. Widely used for statistical analysis tasks, it could be called the lingua franca of data science. Fluency in Python (along with SQL) appears as a requirement in over two-thirds of data engineer job listings.
- R is a unique language with features that other programming languages lack. This vector language is finding use cases across multiple data science categories, from financial applications to genetics and medicine.
- Java, because of its high execution speeds, is the language of choice for building large-scale data systems. It is the foundation for the data engineering efforts of companies such as Facebook and Twitter. Hadoop is written mostly in Java.
- Scala is an extension of Java that is particularly suited for use with Apache Spark. In fact, Spark is written in Scala. Although Scala runs on JVM (Java Virtual Machine), Scala code is cleaner and more concise than the Java equivalent.
- Julia is an up-and-coming general-purpose programming language that is very easy to learn. Its speed is on par with C or Fortran, which allows it to be used as the single language in data projects that formerly required two languages. For example, Python may have been used for prototyping, with re-implementation in Java or C++ to meet production performance requirements. Now, with its speed and ease of use, Julia can be used for both prototyping and production.
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