Big Data Technology Stack List

---

1、Fine

1.1 Overview

Flink is an open source streaming data processing and batch processing framework designed to process large-scale real-time data and offline data. It provides a unified system that efficiently processes continuous streams of data with fault tolerance and low latency.

Flink's design goal is to support both streaming data processing and batch processing in one system to meet different types of data processing needs. Its core concept is a directed acyclic graph (DAG), which realizes a flexible data processing flow by representing a data processing job as a directed graph of a series of operators and data flows.

Flink supports a variety of data sources and data sinks, including message queues (such as Apache Kafka), file systems (such as HDFS), databases, and sockets. It can receive data streams from data sources and send processing results to data sinks, while supporting various operations such as data transformation, aggregation, filtering, and joining.

Flink is highly scalable and can handle large-scale datasets and high-throughput data streams. It utilizes pipeline execution model and memory management technology to efficiently handle parallel computing tasks. In addition, Flink also provides event time-based processing, which can handle out-of-order data streams, and supports window operations and state management.
Flink is fault-tolerant and can recover from failures in the event of failures by dividing the data stream into replayable continuous data streams. It can guarantee the accuracy and consistency of data processing, and has high availability and reliability.

In addition to streaming data processing, Flink also provides batch processing capabilities, allowing users to process limited data sets in a batch manner. This enables users to process real-time and offline data in the same system, and simplifies the complexity of system architecture and development and maintenance.

In summary, Flink is a powerful, high-performance stream data processing and batch processing framework with unified stream processing and batch processing capabilities, fault tolerance, low latency and high availability, suitable for processing large-scale real-time data and offline Various application scenarios of data.

1.2 Features

As a streaming data processing and batch processing framework, Flink has the following characteristics:

1. High performance: Flink can achieve high throughput and low latency data processing through optimized execution engine and parallel computing model. It uses a pipelined execution model, memory-based computation, and tightly coupled task scheduling to maximize performance.

2. Fault tolerance: Flink provides a fault tolerance mechanism that can handle node failures and data loss. It divides the data stream into replayable continuous data streams, and implements fault recovery and data consistency through checkpoints and state backends.

3. Event-driven processing: Flink supports event-time-based processing and can handle out-of-order event streams. It provides mechanisms for window operations and handling out-of-order events, enabling users to group and aggregate data in the time dimension.

4. Unified stream processing and batch processing: Flink integrates stream processing and batch processing in one system, and users can use the same API and programming model to process real-time and offline data. This unification simplifies development and maintenance complexity and provides greater flexibility.

5. Multiple data sources and data receivers: Flink supports multiple data sources and data receivers, including message queues (such as Kafka), file systems (such as HDFS), databases, etc. It is able to integrate with existing data storage and messaging systems, and can flexibly handle different types of data streams.

6. Support for rich operations and function libraries: Flink provides a wealth of operators and function libraries, allowing users to perform various data conversion, aggregation, filtering and connection operations. It also supports custom functions and UDFs (User Defined Functions), enabling users to extend and customize according to their needs.

7. Scalability: Flink has good scalability and can handle large-scale data sets and highly concurrent data streams. It supports horizontal expansion and can increase the throughput and processing power of the system by adding computing nodes.

In general, Flink has the characteristics of high performance, fault tolerance, event-driven processing, unified stream processing and batch processing capabilities, multiple data source support, rich operation and function libraries, and scalability, making it an ideal tool for processing large-scale A powerful framework for real-time and offline data.

2. Introduction to Hadoop

2.1 Overview

Hadoop is an open source distributed computing framework for storing and processing large-scale data sets. It was originally developed by the Apache Software Foundation to solve the problem of processing massive amounts of data.

The core components of Hadoop include:

1. Hadoop Distributed File System (Hadoop Distributed File System, HDFS): HDFS is Hadoop's distributed file system for storing large-scale data sets. It divides data into chunks and distributes those chunks across multiple nodes of the cluster for high fault tolerance and reliability.

2. Hadoop YARN (Yet Another Resource Negotiator): YARN is a Hadoop resource manager for scheduling and managing computing resources in the cluster. It is responsible for allocating resources to jobs, monitoring the execution of tasks, and handling situations such as node failures.

3. Hadoop MapReduce: MapReduce is Hadoop's computing model and programming framework for distributed processing of large-scale data. It achieves efficient data processing by decomposing computing tasks into multiple Map and Reduce stages, and distributing these tasks to nodes in the cluster for parallel computing.

2.2 Features

Hadoop has the following characteristics:

1. Distributed storage and processing: Hadoop adopts a distributed storage and processing method, which can divide large-scale data sets into multiple blocks and store these blocks on multiple nodes in the cluster. In this way, parallel processing and calculation of data can be realized, and processing efficiency can be improved.

2. Scalability: Hadoop has good scalability and can expand processing power by increasing the number of nodes in the cluster. This enables Hadoop to cope with growing data volumes and computing demands, providing elastic resource management.

3. Fault Tolerance: Hadoop is highly fault tolerant and can handle node failures and data loss. Through data replication and backup mechanisms, Hadoop ensures redundant storage of data and can automatically restore data when a node fails.

4. High Throughput: Hadoop has the advantage of high throughput when processing large-scale datasets. By storing data on multiple nodes in a cluster and performing parallel computations, Hadoop enables efficient data processing and analysis.

5. Adapt to multiple data types: Hadoop can handle not only structured data, but also semi-structured and unstructured data. It is capable of processing various types of data, including text, images, audio, video, etc., enabling users to perform diverse data analysis and processing.

6. Elastic data model: Hadoop adopts a flexible data model that enables users to store and process data in its original form without having to define the structure and schema of the data in advance. This makes Hadoop suitable for data exploration and experimentation in big data scenarios.

7. Big Data Ecosystem: Hadoop has a huge ecosystem including various tools and components such as Hive, Pig, Spark, HBase, etc. These components provide rich functions and tools for data processing, data management, data warehouse, data analysis, etc., enabling users to build complete big data solutions.

Overall, Hadoop is a distributed storage and processing framework that is scalable, fault-tolerant, high-throughput, and adaptable to multiple data types. It provides users with an elastic data model and a rich ecosystem, and is an important tool for processing large-scale data sets.

3. Introduction to Hive

3.1 Overview

Hive is a Hadoop-based data warehouse infrastructure designed to provide query and analysis functions similar to relational databases. It was originally developed by Facebook and open-sourced in 2008.

Hive's design goal is to provide simple, scalable and high-performance data query and analysis capabilities. It maps structured data to tables on the Hadoop Distributed File System (HDFS) and provides a SQL-like query language HiveQL, enabling users to query and analyze large-scale data sets using SQL-like syntax.

The core components of Hive include:

1. Metadata storage: Hive uses a metadata storage to manage metadata such as table schema, partition information, and relationship between tables. By default, it uses a relational database such as MySQL to store metadata, but can also be configured to use other storage backends.

2. Query Engine: Hive's query engine converts HiveQL queries into tasks suitable for execution engines such as Hadoop MapReduce or Apache Tez. It is responsible for optimizing query plans, scheduling tasks, and returning results to the user.

3. Data storage and format: Hive supports storing data in tables on HDFS, and provides different storage format options, such as text files, sequence files, Parquet, etc. This enables users to choose the most suitable storage format according to the characteristics of the data.

4. User Interface: Hive provides a command-line interface and a web interface that enable users to execute queries and manage tables interactively. Additionally, it is possible to integrate with other applications using Hive's Java API or ODBC/JDBC drivers.

3.2 Features

Hive has the following characteristics:

1. SQL-like query language: Hive uses the SQL-like query language HiveQL, enabling users to write queries and analysis operations in familiar SQL syntax. This lowers the threshold for learning and using Hive, enabling developers to get started quickly.

2. Handling large-scale data: Hive is built on top of Hadoop and can handle large-scale data sets. It utilizes Hadoop's distributed computing capabilities to execute query tasks in parallel in the cluster to achieve high performance and high throughput.

3. Scalability: Hive has good scalability and can increase or decrease the size and computing power of the cluster according to demand. It can adapt to growing data volumes and computing needs, providing elastic resource management.

4. Multiple data storage formats: Hive supports multiple data storage formats, including text files, sequence files, Parquet, ORC, etc. Users can choose the most suitable storage format according to the characteristics of the data to improve query performance and data compression ratio.

5. Powerful data processing capability: Hive can process different types of data, including structured data and semi-structured data. It supports complex data types such as arrays, maps, and structures, enabling users to flexibly process and analyze various data.

6. Metadata management: Hive uses metadata storage to manage metadata such as table schemas, partition information, and relationships between tables. It provides flexible configuration of the metadata storage backend, which can use a relational database (such as MySQL) or other storage backends to store metadata.

7. Ecosystem integration: Hive is tightly integrated with other tools and components in the Hadoop ecosystem. It can seamlessly interact with Hadoop Distributed File System (HDFS), HBase, Spark, etc. to form a complete big data processing and analysis solution.

In general, Hive provides a SQL-like query language, the ability to process large-scale data, scalability, support for multiple data storage formats, powerful data processing capabilities, metadata management, and tight integration with the Hadoop ecosystem, enabling It has become one of the important data warehouse infrastructures in the field of big data.

4、Spark

4.1 Overview

Spark is a fast, general-purpose, and scalable big data processing and analysis engine designed to provide efficient large-scale data processing capabilities. It was originally developed by AMPLab at UC Berkeley and open-sourced in 2010.

The design goal of Spark is to solve some limitations of Hadoop MapReduce, such as high latency, frequent disk read and write, etc., to provide higher processing speed and flexibility. Different from the traditional disk-based MapReduce, Spark implements memory computing by storing data in memory and using Resilient Distributed Datasets (RDD) as the basic data structure, thus providing faster data processing than MapReduce. Data processing speed.

Spark provides a variety of data structures for representing and manipulating data in distributed computing. The following are commonly used data structures in Spark:

1. Resilient Distributed Datasets (RDD): RDD is the most basic abstract data structure of Spark, which represents an immutable collection of data distributed on multiple nodes in the cluster. RDDs can operate in parallel with fault tolerance, support data transformation and persistence, and automatically recover lost data when needed. RDDs can be stored in memory for fast data processing.

2. DataFrame (DataFrame): DataFrame is a data structure similar to a table in a relational database. It organizes data in columns and has schema information. DataFrame can read data from a variety of data sources, such as text files, JSON, CSV, etc., and can also be converted from RDD. DataFrame provides SQL-like query syntax and rich data manipulation functions, enabling users to process and analyze data in a concise manner.

3. Dataset: Dataset is a new data structure introduced by Spark 1.6. It is an extension of RDD and DataFrame, combining the advantages of both. Dataset is type-safe, that is, type checking is performed at compile time to avoid some runtime errors. It provides powerful data manipulation and query capabilities, and has a similar API to DataFrame.

4. Streaming Data: Spark provides streaming processing functions, through which real-time data streams can be processed and analyzed. Streaming data is split into small batches and processed as RDDs. Spark Streaming provides a wealth of window operations, aggregation and transformation functions, enabling users to process and analyze streaming data in real time.

In addition to the commonly used data structures mentioned above, Spark also provides some other data structures and libraries, such as graph data structure (GraphX) for graph computing, machine learning library (MLlib) for machine learning tasks, graph processing library (Spark SQL) for For processing graphics data, etc. These data structures and libraries enable Spark to adapt to various data processing and analysis needs.

4.2 Features

Spark has the following characteristics:

1. Speed: Spark is based on memory computing and stores data in memory for high-speed processing. Compared with traditional disk storage data processing frameworks, such as Hadoop MapReduce, Spark has faster processing speed. In addition, Spark also improves processing efficiency by supporting parallel computing and task scheduling optimization.

2. Multiple task support: Spark supports multiple data processing tasks, including batch processing, interactive query, stream processing, and machine learning. Users can use the same set of tools and code libraries to process different types of data and tasks, reducing learning and maintenance costs.

3. Flexibility: Spark provides a rich API and programming model, and supports multiple programming languages, such as Scala, Java, Python, and R. This allows developers to use their familiar programming language for development, and can choose the most appropriate API and model according to task requirements.

4. Fault tolerance: Spark has fault tolerance, and through the backup and recovery mechanism of elastic distributed data set (RDD), it can ensure the reliability of data and the correctness of calculation when a node fails. Spark is able to automatically recover lost data and recalculate lost parts when needed.

5. Distributed computing: Spark is a distributed computing framework that can distribute data and computing tasks to multiple nodes in the cluster for parallel processing. It provides a task scheduling and data distribution mechanism, which can efficiently utilize the computing resources of the cluster and realize large-scale data processing and analysis.

6. Powerful ecosystem: The Spark ecosystem is very rich and tightly integrated with other tools and components in the Hadoop ecosystem. It can directly read and write Hadoop Distributed File System (HDFS), and seamlessly interact with Hive, HBase, Kafka, etc. to form a complete big data processing and analysis solution.

7. Scalability: Spark has good scalability, and the scale and computing power of the cluster can be increased or decreased according to demand. It can adapt to growing data volumes and computing needs, providing elastic resource management.

In general, Spark has the characteristics of high-speed processing capability, multi-task support, flexibility, fault tolerance, distributed computing capability, powerful ecosystem and scalability, making it an ideal choice for processing large-scale data and complex computing tasks. Ideal choice.