Data Processing #
Introduction #
Data processing in the field of data engineering refers to the collection and manipulation of raw data to produce meaningful information. It involves the conversion of raw data into machine-readable form, flow of data through the CPU and memory, construction of output, and the interpretation of the output. Data processing functions include validation, sorting, summarization, aggregation, analysis, reporting, and classification.
Data processing frameworks, such as Apache Spark, Apache Kafka, Amazon Kinesis, Apache NiFi, and MapReduce, are designed to handle large volumes of data, perform complex transformations, and facilitate quick insights. These frameworks are capable of processing data in real-time (streaming), in batches, or a hybrid of both. They are designed to be highly scalable, reliable, and distributed, meaning they can process data across many machines in parallel, increasing the speed of data processing tasks.
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. It’s a key component of the Apache Hadoop software framework. In a MapReduce computation, input data is broken down into chunks for processing by different nodes in a distributed system. Intermediate results are then combined (or “reduced”) to solve the problem at hand.
These frameworks are platform-independent and can run on various operating systems, including but not limited to Linux, Windows, and MacOS. This makes them versatile tools for data processing in diverse environments.
The choice of data processing tool depends on the specific requirements of the task, such as the volume of data, the speed at which the data needs to be processed, and the complexity of the data processing operations. Each tool has its strengths and is suited to different types of tasks. For example, Apache Kafka is well-suited for real-time data streaming applications, while Apache Spark is excellent for complex data processing tasks that require in-memory computation.
Tools #
Data processing tools in data engineering can be categorized into three types: Batch, Hybrid, and Stream.
Batch #
Batch processing is a method where data is collected, processed in bulk, and then the results are produced. The tools used for batch processing include:
- Apache Arrow: A cross-language development platform for in-memory data.
- Data Build Tool (dbt): A command-line tool that enables data analysts and engineers to transform data in their warehouses more effectively.
Hybrid #
Hybrid processing combines both batch and stream processing methods. The tools used for hybrid processing include:
- Apache Spark: An open-source, distributed computing system used for big data processing and analytics.
- Apache Beam: A model and set of language-specific SDKs for defining and executing data processing workflows.
- Apache Flink: An open-source stream processing framework for high-performance, reliable, and real-time data streaming applications.
- Apache Nifi: A software project from the Apache Software Foundation designed to automate the flow of data between software systems.
Stream #
Stream processing is a method where data is continuously processed, with new data affecting the computation as it arrives. The tools used for stream processing include:
- Apache Kafka: A distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
- Apache Storm: A free and open-source distributed real-time computation system.
- Apache Samza: A distributed stream processing framework that uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.
- Amazon Kinesis: A fully managed service for real-time processing of streaming data at a massive scale provided by Amazon Web Services (AWS).