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Data engineering is an important area of work that designs and builds systems for the collection, management, and processing of large volumes of data. Data engineers work with data scientists and business analysts on data quality and optimization activities. This would include the acquisition of appropriate datasets and the development of transformation algorithms besides maintaining database pipeline architectures. Data engineering plays a critical role in data science and machine learning as it underlies data-driven decisions by building reliable data pipelines and warehouses.
Apache Spark is a powerful open-source data processing engine and currently is considered as most preferred computing engine by data engineers. They attribute its use to the fact that it is fast and can comfortably handle large volumes of data, is highly scalable as well as incredibly easy to use which makes it a perfect tool for creating great data pipelines. In this article, the author intends to look at what Apache Spark entails and how data engineers can exploit this feature.
Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload.
You can write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker, and Kubernetes.
Apache Spark can help optimize your data pipelines and teach you to craft modular and testable Spark applications. You can create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your path to production.
In this article, we will discuss how it is possible to do Data Engineering with Apache Spark.
Spark mainly focuses on processing large datasets and is thus best suitable for distributed computing. Although it was first designed and implemented at the University of California, Berkeley it has expanded to become one of the most used big data processing systems today. The platform where Spark can operate has flexibility thus; it can work with Amazon S3, Apache Cassandra, Apache Hadoop Common, HDFS, and Apache HBase.
Speed: Whenever data cannot be stored locally, Spark employs techniques of data science partitioning to retrieve information from databases as well as in-memory processing for analyzing great volumes in record time.
Scalability: Spark can perfectly be written in such a manner that it can horizontally expand across many nodes thus making it possible to manage very large data sets without compromising on performance.
Ease of Use: From experience, let me confirm that Spark has a friendly front end that makes it easy to develop intricate analytical sequences.
Versatility: The availability of various data sources and data transformation capabilities in Spark allows the developers to create customized data processing pipelines that address the requirements of specific use cases.
Spark's architecture is built on four primary components: Spark Core, this spark component is the basic API for Spark data processing, Spark SQL is another component of Apache Spark used for SQL and structured data processing, Spark Streaming this component is used for stream data processing in Apache Spark, Finally, MLlib is the component of Apache Spark that is used for big data mining.
Spark Core: This forms the core of the Spark technology. It contains the basic capabilities for distributed data processing. Some of the components are The API: Resilient Distributed Dataset, in short, known as RDD, is used for carrying out distributed data processing; Hadoop streaming is a type of execution layer; Task tracker is involved in the scheduling of jobs.
Spark SQL: Spark SQL is a package that helps in running a SQL-like interface used to work with structured and semi-structured data. Developers are allowed to work with Spark SQL for executing SQL queries in data that is stored on sources like HDFS, Apache Cassandra, and Apache HBase.
Spark Streaming: This is another module used for processing streaming data in real-time. It allows you to process real-time data streams in small portions, a feature most applicable when you want to deal with sensor data, social media, or any other source of information in real-time.
MLlib: This stands for machine learning library. This is a package responsible for the processing of data that harbors machine learning algorithms. It supports a range of major algorithms for machine learning, such as clustering, classification, regression, and collaborative filtering.
Batch Processing: Spark finds high utility in batch processing, mainly when we deal with huge data, read data from various sources, transform the data, and write the processed data to some target data storage. The batch processing capabilities of Spark best fit into the execution of tasks like ETL, data warehousing, and data analytics.
Real-time Data Streaming: Using Spark, real-time data streaming can be performed that deals with ingesting data from real-time sources.
Apache Spark is a blessing for data engineers to enable strong data pipelines and solve big data problems based on speed, scalability, and ease of use. In this ever-changing era of big data needs, Spark is surely going to be the topmost framework for a data engineer, and hence, it's an essential skill for anybody who operates in the data science domain.
Spark supports other programming languages like Scala, Java, Python, and R if youwant an alternative to write your application logic inside the code base for these languages.
Spark will not replace Hadoop, but Spark can work within the Hadoop ecosystem, managing clusters with the YARN tool, or work independently.
Spark has a more user-friendly interface compared to most distributed processing frameworks.
Spark works very well with the big data tool ecosystem. Some of the most popular tools are Apache Kafka for real-time data ingestion, Apache Hive for data warehousing, and Apache Zeppelin for interactive data analytics.
The huge demand for professional data engineers who can handle big data has increased with its growth. Spark is the most powerful weapon in a data engineer's tool kit. Proficiency in Spark can have many career opportunities, whether in the data engineering domain, data science, or machine learning.
