Table of contents
Chapter 1 Big Data Basics
1.1 Basic Linux Learning
1.2 Basic SQL Learning
1.3 Basic learning of Java and Scala
Chapter 2 Data Acquisition and Storage Technology
2.1 Hadoop Basics and Practical Battle
2.2 Hive and Hbase technology
2.3 ETL process and principle
Chapter 3 Data Management and Query Technology
3.1 Data warehouse system construction
3.2 Methodology of data governance system
3.3 OLAP query technology
Chapter 4 Big Data Development Tools and Platforms
4.1 Distributed Coordination Tool Zookeeper
4.2 Message Queue Kafka
4.3 Task Scheduling Tool Azkaban
4.4 Big Data Platform Tools CDH
Chapter 5 Data calculation and analysis technology
5.1 Spark system principle and practical combat
5.2 Flink's full system principle and practical combat
5.3 The practical principle of Kafka in real-time scenarios
Chapter 6 Data Visualization and Data Analysis Expansion
6.1 Introduction to data visualization tools
6.2 Python Data Analysis Basics
6.3 Basics and Applications of Machine Learning
Chapter 1 Big Data Basics
1.1 LinuxBasic learning
In the learning path of big data development,Linux BasicsLearning is a crucial cornerstone. As an open source, stable, secure and flexible operating system, Linux has a wide range of applications in the server field. For those who are engaged in big data development, proficient in mastering various basic commands and operating methods of the Linux operating system is a necessary core skill.
The Linux operating system provides a variety of basic commands, covering multiple fields such as file operation, directory operation and system management.
File operation commands:picturels(used to list directory contents),cd(Switch the working directory),pwd(Show the full path to the current working directory),touchCommands such as (create new files or update the timestamp of files) are tools that are frequently used in daily operations. passlsCommand, we can learn in detail about various information about files and directories, including permissions, sizes, modification time, etc. andcdThe command allows us to flexibly switch between different directories and quickly locate the required file location.pwdThe command allows us to clearly know the directory path we are currently in at any time.
Directory operation commands：mkdir(Create a new directory) andrmdirCommands such as (delete empty directories) play an important role in directory management. passmkdir, we can easily create new directory structures for better organization and classification of files.
System Management Commands：ps(View process status),top(Real-time display of the most resource-consuming process in the system)killCommands such as (terminate process) are crucial to monitoring and managing system processes.psCommands allow us to quickly understand the running process information in the system.topThis will show the use of system resources in real time to help us discover and deal with possible performance bottlenecks in a timely manner. When an exception occurs or is no longer needed, we can usekillcommand to terminate it.
In the process of learning the basics of Linux, developers must not only be familiar with the general usage of these basic commands, but also need to have a deep understanding of Linux's file system and permission management mechanism.
Linux's file system adopts a hierarchical structure, and the organization of files and directories is clear and orderly. We need to master how to create, delete, move and rename files and directories. For example, usemvCommands can move or rename files and directories, usingrmCommands can delete files and directories. However, when performing deletion operations, you need to be extra cautious to avoid accidentally deleting important data.
Permission management is also crucial in Linux. By setting different permissions, we can control user access rights to files and directories, including read, write and execute permissions. AvailablechmodCommands to modify permissions to files and directories to ensure data security and integrity.
In addition, the Linux operating system also provides powerful network management and security management functions.
In terms of network management, we need to understand how to configure network interfaces, such as setting IP addresses, subnet masks, gateways, etc. passifconfigCommands can view and configure basic information about network interfaces, androuteCommands are used to manage routing tables. At the same time, we also need to master how to set firewall rules to limit access to the system.iptablesIt is a commonly used firewall tool in Linux. By configuring its rules, the system can be effectively protected.
In terms of security management, in addition to setting firewall rules, you also need to pay attention to user authentication and authorization, file system encryption, etc. For example, by setting a user password, usesudoCommands grant users specific administrative permissions and other methods to enhance system security.
In the process of basic Linux learning, practice is the key to improving skills. By operating in an actual Linux environment, we can have a deeper understanding and mastering of commands and operation methods. At the same time, actively read Linux's official documents and community resources to obtain more learning materials and technical support. These resources not only contain detailed command descriptions and examples, but also provide the latest technical trends and best practices.
With the booming development of cloud computing and containerization technologies, Linux's position in these fields is becoming increasingly important. byDockerThe container technology represented is built based on the characteristics of the Linux kernel. For developers who are eager to explore in-depth in the fields of big data and cloud computing, solid basic Linux learning is the key to opening the door to success. By learning Linux, you can not only improve your own technical capabilities, but also open up a broader space for future career development.
1.2 Basic SQL Learning
SQL (Structured Query Language) is a standard language for database query and management, and occupies an important position in big data processing. Proficiency in SQL is an essential key skill for learners who want to dive into the big data field.
The core of the SQL language is its powerful query function. passSELECT、FROM、WHEREWith the clever combination of keywords such as this, users can flexibly and easily retrieve the required information from the database.
For example,"SELECT * FROM students WHERE age > 18;” This simple query statement can filter out all student records older than 18 years old from the “students” table.
SQL not only supports data query operations, but also covers functions such as insertion, update and deletion of data.
“INSERT INTO students (name, age) VALUES ('Alice', 20);” is used to insert a new student record into the “students” table.
“UPDATE students SET age = 21 WHERE name = 'Alice';” Then you can update the age information of a specific student.
“DELETE FROM students WHERE age < 18;” Ability to delete records of students under the age of 18.
These functions cooperate with each other to jointly build a solid foundation for database management.
In the process of learning SQL, it is crucial to have a deep understanding of these basic operations and their relationships.
Query optimization is a key link in SQL learning. In practical application scenarios, large databases often contain massive data, and how to efficiently query the required data from them has become a huge challenge.
Rational use of indexes is an important means to improve query performance. For example, creating indexes on fields that are often used for querying conditions can significantly speed up queries.
Avoiding full table scanning is also one of the key points of optimization. Through precise query conditions and reasonable index use, unnecessary data reading can be reduced.
Reducing nested queries can also improve query efficiency, because too much nesting can lead to complex execution plans and performance losses.
To achieve effective query optimization, learners not only need to have a solid SQL foundation, but also need to have an in-depth understanding of the internal mechanisms of the database, such as storage structure, indexing principles, etc.
After mastering the basic syntax and query optimization skills of SQL, it is also essential to be familiar with common database management systems.
MySQLIt is widely popular for its open source, ease of use and stable performance. It is suitable for applications of all sizes, from small websites to large enterprise systems.
OracleWith its powerful functions and enterprise-level support, it occupies an important position in the high-end market. It provides a wealth of features and tools to meet complex business needs.
These systems provide a wealth of SQL features and tools that can help users manage data more efficiently.
With the continuous evolution of big data technology, the SQL language is also constantly developing and changing.
For example, to adapt to the processing needs of large-scale datasets, new SQL variants such as HiveQL and SparkSQL came into being.
HiveQL is a query language used by Hive, a data warehouse tool based on Hadoop. It is optimized and extended for the big data environment based on the traditional SQL syntax.
SparkSQL is a SQL module in Spark's big data processing framework. It can be combined with Spark's powerful computing power to achieve efficient data query and analysis.
For big data practitioners, it is important to master traditional SQL, but paying close attention to the development and application of these new SQL variants is also indispensable.
Whether it is a data analyst, data scientist or software engineer, you need to be proficient in using SQL to deal with increasingly complex data processing needs in the field of big data.
By deeply learning the basic syntax of SQL, query optimization techniques, and how to use common database management systems, learners will be able to better understand and apply big data technology, thereby achieving more success in their future careers.
With the continuous advancement of technology, the integration of SQL and big data technology will be closer. For example, in big data processing frameworks such as Hadoop and Spark, SQL is widely used in data query, transformation and analysis.
These frameworks provide strong support for SQL, allowing developers to process large-scale data sets with familiar syntax, greatly reducing the threshold for big data processing.
Therefore, for big data practitioners, mastering SQL is not only a basic skill, but also the key to improving work efficiency and expanding their career development path.
1.3 Basic learning of Java and Scala
Java and Scala are two programming languages widely used in the field of big data. They each have unique characteristics and advantages, and jointly lay an important foundation for the construction of the big data ecosystem.
Java as a matureObject-Oriented ProgrammingSince its birth, language has won wide acclaim and application for its outstanding feature of "written at once, run everywhere".
In the field of big data, Java's position is as solid as a rock. Many well-known big data processing frameworks, such as Hadoop, Spark, etc., are developed based on Java.
This fully demonstrates Java's far-reaching influence in the field of big data.
Java has rich class libraries and strong community support, which allows developers to quickly find solutions when facing complex big data problems.
For example, Java's collection class library (e.g.ArrayList、HashMapetc.) provides a convenient way to store and operate data.
Compared to Java, Scala is a relatively young but dynamic programming language.
It combines the advantages of object-oriented programming and functional programming, making the code more concise, elegant and easy to understand.
Scala is able to interact seamlessly with Java class libraries, which means developers can make full use of the rich resources of the Java ecosystem while enjoying the programming convenience brought by Scala.
In the field of big data, Scala's functional programming features show significant advantages when dealing with large-scale data sets.
For example, in Apache Spark, Scala's functional programming style makes data processing flow clearer and more efficient.
In the process of learning Java and Scala, developers need to master the basic syntax structure, data types, control flow statements, and core concepts of object-oriented and functional programming.
For Java, you need to understand classes, objects, inheritance,PolymorphicImportant features of object-oriented programming.
For Scala, you need to master the concepts of advanced functions, currying, pattern matching, etc. in functional programming.
In addition, it is also crucial to understand how to optimize programs written in both languages.
When oriented towards data-intensive computing, you need to know how to adjust JavaVirtual Machine(JVM) performance parameters, such as heap memory size, garbage collection strategy, etc., to improve the operation efficiency of the program.
Similarly, when using Scala for big data processing, you must be familiar with how to fully utilize its functional programming characteristics, such as using immutable data structures, parallel set operations, etc. to optimize the data processing process.
Java and Scala are indispensable and important programming languages in big data development.
Mastering the basic knowledge and optimization skills of these two languages is crucial for big data developers.
By learning Java and Scala, developers will be able to respond more confidently and calmly to the challenges brought by big data, and provide enterprises with more efficient and smarter data solutions.
In the future big data development, the role of Java and Scala will continue to be enhanced.
With the continuous advancement of technology and the continuous expansion of application scenarios, these two programming languages will continue to evolve and develop.
Therefore, as big data developers, we need to maintain a keen perception of new technologies and new knowledge, and constantly learn and make progress to adapt to the rapidly changing big data era.
At the same time, we should make full use of the respective advantages of Java and Scala, and flexibly select technical solutions based on actual needs, so as to maximize the potential of big data and promote the development and innovation of enterprises.
Chapter 2Data collectionand storage technology
2.1 Hadoop Basics and Practical Battle
As the core architecture of the field of big data processing, Hadoop has become a key support for dealing with massive data challenges. With its unique distributed nature, it allows efficient processing of large-scale datasets on computer clusters using simple programming models.
Hadoop Basics
The core components of Hadoop include the Hadoop Distributed File System (HDFS) and the MapReduce framework.
HDFS is a distributed file system designed for large-scale data storage. It divides large files into multiple data blocks and stores these data blocks on different nodes to achieve distributed storage and fault tolerance of data. Through replica policies, HDFS ensures data reliability and availability. For example, a 1GB file may be divided into 128MB data blocks and stored on multiple nodes, while each data block will have multiple copies to prevent node failures from causing data loss.
MapReduce is a programming model for large-scale data computing. The core idea is to break down complex data processing tasks into Map stage and Reduce stage. In the Map stage, the input data is split into key-value pairs for preliminary processing; in the Reduce stage, the results of the Map stage are summarized and integrated. For example, calculate the number of occurrences of each word in a document, the Map stage will split the document into words and count, and the Reduce stage will summarize the total count of each word.
Hadoop practical application
In practical applications, Hadoop has demonstrated its powerful capabilities, covering many fields such as big data storage and computing, log analysis, and data mining.
In the e-commerce field, Hadoop can be used to store and process massive user behavior data, thereby providing accurate user portraits and marketing recommendations. By analyzing users' browsing history, purchase records and other data, companies can understand users' interests and needs and recommend products to them more in line with their preferences.
In the financial field, Hadoop can play an important role in risk control and customer credit assessment scenarios. It can integrate information from multiple data sources, such as transaction records, credit reports, etc., and use large-scale data analysis to identify potential risks and evaluate customers' credit status.
In addition, Hadoop can also integrate with other big data technologies and tools to build a more complete and efficient big data processing and analysis platform.
For example, integrating with Spark will enable faster data processing and analysis with Spark's in-memory computing benefits. At the same time, it integrates with NoSQL databases such as HBase to realize real-time query and analysis of massive data.
In short, Hadoop provides a solid foundation for big data processing and analysis with its powerful distributed storage and computing capabilities. With the continuous advancement and improvement of technology, Hadoop will continue to play an important role in the field of big data in the future, providing strong support for us to deal with the growing data challenges.
2.2 Hive and Hbase technology
In the Hadoop ecosystem, Hive and Hbase have become an indispensable and important component in big data processing and analysis with their unique functions and advantages.
How Hive works
As a Hadoop-based data warehouse tool, Hive can map structured data files into database tables and provide SQL-like query language HiveQL for data processing and analysis.
The syntax of HiveQL is very similar to that of traditional SQL, which makes it easy for data analysts familiar with SQL to get started. For example, bySELECT column1, column2 FROM table_name WHERE condition;” Such a query statement can obtain the required data from the mapped table.
At the same time, Hive also supports custom functions (UDF), where users can write functions to process data according to specific business needs, increasing the flexibility and scalability of data processing.
In practical applications, Hive is often used to process massive structured data, such as analysis of website logs, summary of user behavior, etc.
Hbase Technical Features
As a distributed, versioned, non-relational database, Hbase has significant characteristics such as high reliability, high performance and scalability.
It uses Hadoop HDFS as a file storage system to provide applications with efficient and stable data storage services.
Hbase's data model is a sparse, distributed, persistent multi-dimensional sorting map, which allows it to quickly locate and read and write required data.
For example, when storing user's real-time transaction records, Hbase can quickly respond to query requests and provide the latest transaction information in a timely manner.
In addition, Hbase supports transaction processing and version control functions, further enhancing data reliability and consistency and ensuring data accuracy and completeness.
Practical application and integration
In practical applications, Hive and Hbase often complement each other and work together.
For example, on e-commerce platforms, Hive can be used to batch process and analyze massive user behavior data to explore users' purchasing preferences and potential needs. Hbase is used to store users' real-time browsing and purchase records so that they can quickly provide personalized recommendation services when the user makes the next visit.
At the same time, optimizing the performance of Hive and Hbase is also a key link in big data development.
For Hive, query performance and data processing efficiency can be improved by optimizing the structure of query statements, adjusting data skew strategy, and using indexes and partitions reasonably.
For Hbase, it can improve its performance and response speed by carefully designing the table structure, optimizing data read and write strategies, and rationally utilizing the cache mechanism.
Hive and Hbase, as important components in the Hadoop ecosystem, each play a unique role, providing strong support for big data processing and analysis. In-depth learning and mastering their technical characteristics and application methods is of great significance to big data developers.
2.3 ETL process and principle
In the vast field of data processing and analysis, the ETL (Extract-Transform-Load) process is like a bridge connecting the data source and the target system, providing a solid data foundation for subsequent data analysis and decision-making.
Extract
Data extraction is the starting part of the ETL process, and its mission is to accurately obtain data from a diverse source system.
These source systems are all-encompassing, and may be traditional relational databases, such as MySQL, Oracle, or non-relational databases, such as MongoDB, Cassandra, or file systems, such as CSV files, XML files, or even API interfaces.
During the extraction process, the integrity and accuracy of the data must be ensured. This means dealing with possible data missing, errors or inconsistencies.
At the same time, it is also necessary to consider the access rights of the source system to ensure that data is obtained legally and compliantly.
In addition, the conversion of data formats and the optimization of transmission efficiency are also factors that cannot be ignored.
In order to achieve efficient data extraction, specialized tools emerge, such as Sqoop.
Sqoop can efficiently transfer data between Hadoop and structured data storage, which can significantly improve the efficiency of data migration whether it is batch import or export.
Data conversion (Transform)
Data conversion is the core link of the ETL process, like a data processing plant.
Its main goal is to clean the extracted data, convert the format, merge the data and process the complex business logic, so that it meets the data requirements of the target system.
In this process, it may be necessary to deal with inconsistencies in data types, filling of missing values, removing duplicate data, and data calculation and integration based on specific business rules.
The complexity and importance of data transformation determine that it often needs to be accomplished with powerful data processing tools.
For example, Kettle (now renamed Pentaho Data Integration) provides a wealth of components and flexible configuration options that can handle a variety of complex data conversion needs.
Data loading (Load)
Data loading is the final work of the ETL process, responsible for loading the converted data into the target system accurately.
During the loading process, it is necessary to carefully consider the storage structure, indexing strategies and performance optimization of the data to ensure that the data can be queried and analyzed quickly and accurately.
Depending on the differences in the target system, the data loading methods vary.
For example, when loading data into Hive or Hbase in the Hadoop ecosystem, it needs to be done using Hadoop's distributed file system (HDFS) and the corresponding data import tool.
Optimization and Challenges of ETL Processes
Although ETL processes play an important role in data processing, they also face many challenges and optimization needs in practical applications.
With the explosion of data volume and frequent changes in business demand, ensuring the efficiency and accuracy of ETL processes has become a difficult problem that needs to be solved.
Data format and semantic differences between different source systems can lead to difficulties and errors in data conversion.
To address these challenges, ETL processes can be optimized from multiple dimensions.
For example, parallel processing technology is adopted to make full use of multi-core CPUs and distributed computing resources to improve data processing speed.
Use incremental loading strategies to load only newly generated or modified data to reduce unnecessary data migration.
With advanced technologies such as machine learning, outliers and errors in data are automatically identified and processed.
Mastering ETL processes and principles For big data developers, it is like holding the key to open the door to efficient data processing.
By deeply understanding all aspects of the ETL process and commonly used tools and technologies, big data developers can process and analyze data more easily and create more value for the enterprise.
Chapter 3 Data Management and Query Technology
3.1 Data warehouse system construction
In the vast world of big data, the construction of a data warehouse system is like building a magnificent treasure house of knowledge, which provides a solid architecture for data storage, management and analysis.
Basic methods for building data warehouses
The construction of a data warehouse is a systematic and vast project, covering the entire process from data source filtering to data storage and query.
First of all, in the choice of data sources, we must closely focus on business needs, clarify which data sources are core and indispensable, and formulate corresponding fine collection strategies. This may involve getting data from multiple business systems, external data sources, and even real-time data streams.
Data cleaning and integration are key steps to ensure data quality. This includes eliminating duplicate, wrong or incomplete data, unifying data formats, and resolving conflicts and inconsistencies between data, thereby providing an accurate and consistent data basis for subsequent analysis.
In terms of data storage, it is necessary to carefully select appropriate databases and storage architectures based on data volume, access mode and performance requirements. This may involve the selection and configuration of relational databases, NoSQL databases, data warehouse products, or distributed storage systems.
Application of Dimensional Modeling Theory
Dimensional modeling plays a crucial role in data warehouse design.
It cleverly divides business data into two categories: fact table and dimension table. The fact table focuses on recording specific details of business events, such as key indicators such as the amount and quantity of sales orders; while the dimension table focuses on describing the background information of business events, such as time, place, product category, etc.
Through this clear division, the relationship between data can be more intuitively displayed, greatly improving the efficiency of query and analysis.
When implementing dimension modeling, rigorous steps and specifications must be followed.
First, we must accurately identify the business process and determine the relevant fact tables and dimension tables based on this. This requires a deep understanding and insight into the business process.
Then, clarify the fields and properties of each table, carefully construct the relationship between tables, and ensure the consistency and integrity of the data.
Finally, the built data model is strictly verified and optimized to ensure that it can meet business needs efficiently and perform excellently in performance.
Continuous evolution and upgrading of technology
With the rapid development of big data technology, the data warehouse system also needs to keep pace with the times and continue to evolve and improve.
The introduction of distributed database technology can greatly improve the performance of data storage and query and meet the challenges of massive data.
The application of data mining and machine learning algorithms can mine potential value and models from massive data, providing more forward-looking insights into business decisions.
Advanced data visualization tools can present complex data in an intuitive and easy-to-understand form, helping decision makers grasp key information more quickly.
Successfully building an efficient, stable and easy to expand data warehouse system requires a deep understanding of dimensional modeling theory, practical application of actual business needs, and a keen insight and active adoption of new technology trends.
This will provide strong support for enterprises' data-driven decision-making and help enterprises stand out in a highly competitive market.
3.2 Methodology of data governance system
In today's digital business environment, the methodology of the data governance system is like a master key, opening the door to efficient enterprise data management.
Build a reasonable organizational structure
Building an organizational structure for data governance is the cornerstone of ensuring the orderly development of data governance work.
This architecture should clearly define the responsibilities of each department and role to avoid work chaos and inefficiency caused by unclear responsibilities.
For example, a data governance committee is established to formulate macro-data governance strategies, standards and guidelines, supervise the overall progress of data governance work, and coordinate the solution of cross-departmental data governance problems.
At the same time, a dedicated data administrator is set up to be responsible for daily data maintenance, quality monitoring, data security management and other specific tasks to ensure the accuracy, completeness and availability of data.
Develop unified data standards
Unified data standards are the key bridge to realize data circulation and sharing within enterprises.
When formulating data standards, the company's business needs and industry specifications must be fully considered to ensure that the standards are practical and forward-looking.
For example, a unified data format, encoding rules, data dictionary, etc. are defined so that data in different departments and systems can be understood and compatible with each other.
At the same time, in order to ensure the effective implementation of data standards, strict supervision and implementation mechanisms are needed.
This includes regular data quality inspections, training and publicity of data standards, and correction and punishment of violations of standards.
Strengthen data quality monitoring
Data quality monitoring is the key line of defense in the data governance system.
By establishing a comprehensive data quality monitoring mechanism, errors, missing and inconsistencies in the data can be discovered and corrected in a timely manner to ensure the accuracy and availability of the data.
Data quality monitoring should run through the entire life cycle of data collection, storage, processing and use.
For example, in the data collection process, the quality evaluation and verification of the data source is carried out; during the data processing process, the effects of data conversion and cleaning are monitored; during the data usage stage, the accuracy and consistency of the data are verified.
At the same time, with the help of advanced technical means, such as data mining and machine learning algorithms, it is possible to analyze and mine potential quality problems in the data more deeply, and early warnings and measures are taken to solve them.
Covering data security and asset management
In addition to the above core aspects, the data governance system should also include important contents such as data security management and data asset management.
Data security management is the key line of defense to ensure that data is not illegally acquired, tampered with or leaked.
This requires the establishment of a complete security protection mechanism, including access control, data encryption, security audit and other measures to ensure the confidentiality, integrity and availability of data.
Data asset management is a comprehensive inventory, evaluation and optimization utilization of enterprise data resources.
By clarifying the value, classification and distribution of data assets, we can better plan the development and utilization of data resources and improve the return on investment of data assets.
A complete methodology of data governance system is the cornerstone of building an efficient and stable big data environment.
Through comprehensive measures such as establishing a reasonable organizational structure, formulating unified data standards, strengthening data quality monitoring, strengthening data security management and data asset management, enterprises can better manage and utilize their big data resources and provide strong data support and decision-making basis for business development.
3.3 OLAP query technology
OLAP (Online Analytical Processing) technology shines brightly on the stage of big data analysis, providing users with efficient and flexible data analysis capabilities.
Mainstream OLAP query engine
Among the numerous OLAP query engines, Kylin, Clickhouse and Impala stand out with their own unique advantages.
Kylin is an open source distributed analytics engine, tailored to the Hadoop big data platform.
Its distinctive feature is that it adopts pre-computing technology, which greatly improves query performance by pre-calculating and storing multi-dimensional data cubes (Cuboids), and is especially suitable for complex data analysis scenarios that require rapid response, such as real-time monitoring and real-time analysis.
At the same time, Kylin supports SQL interface, reducing users' learning costs and allowing users who are familiar with SQL to get started quickly.
Clickhouse is a high-performance columnar database management system designed for real-time data analysis.
Its biggest advantage lies in its excellent query performance and extremely high data compression rates, which can demonstrate excellent efficiency when processing large-scale data sets.
Clickhouse supports a variety of data formats and rich SQL features, providing more flexibility and diversity for data analysis.
Impala is a high-performance SQL query engine developed by Cloudera that can directly provide fast and interactive SQL query capabilities for Hadoop data stored in HDFS or HBase.
Impala does not require data conversion or sampling, and can directly query large amounts of data, allowing users to obtain analysis results faster.
It also supports a variety of data formats and compression algorithms, further improving query efficiency.
Select the right query engine
When choosing an OLAP query engine, you need to comprehensively consider specific business needs and team technical capabilities.
If the business requires extremely high real-time requirements and needs to quickly obtain analysis results, then Kylin may be the first choice.
Clickhouse may have more advantages for scenarios where hyperscale data sets need to be processed and pursued extreme query performance.
If the team already has in-depth understanding and practical experience in the Hadoop ecosystem, then Impala, which is tightly integrated with Hadoop, may be a more suitable option.
At the same time, it is also necessary to consider the team's familiarity with specific technologies and the compatibility of the technology stack.
For example, if the team mainly uses columnar database technology, choosing Clickhouse may be easier to get started and maintain.
OLAP query technology plays an indispensable role in big data processing.
Deeply understanding and analyzing the characteristics and application scenarios of OLAP query engines such as Kylin, Clickhouse and Impala, and selecting the most suitable query engine based on actual business needs and team technical capabilities can significantly improve the efficiency and flexibility of data analysis and provide strong support for enterprise decision-making.
Chapter 4 Big Data Development Tools and Platforms
4.1 Distributed Coordination Tool Zookeeper
In the vast world of big data, the coordination and management of distributed systems are like a sophisticated symphony, and Zookeeper is the baton of precise command.
The basic principles of Zookeeper
Zookeeper adopts a file system-like data structure - ZNode. Each ZNode can not only store data, but also mount child nodes, forming a tree-shapedNamespace。
Through this structure, Zookeeper implements key distributed coordination services such as data publishing/subscription, load balancing, and distributed locking.
In the Zookeeper cluster, the election mechanism plays an important role. All write requests are processed by selecting a Leader node and ensuring data consistency, while other Follower nodes are responsible for processing read requests and synchronizing data from Leader nodes.
The core features of Zookeeper
- Data consistency:Zookeeper relies on the powerful Zab protocol to ensure the consistency of data in the cluster, allowing each node to obtain the latest and accurate data.
- Atomicity: All update operations are either successful or failed, and there is no intermediate state, ensuring the reliability of data operations.
- reliability: Even if some nodes fail, the Zookeeper cluster can still operate stably and continuously provide services.
- Real-time: Can respond quickly to client requests and perform well in scenarios with high real-time requirements.
Zookeeper usage scenarios
- Configuration Management: In complex distributed systems, Zookeeper can be used to centrally manage configuration information and realize dynamic updates and pushes.
- Distributed lock: Effectively control concurrent operations in distributed systems, ensure data consistency, and prevent data errors caused by multi-threading or multi-process competition.
- Service registration and discovery: In the microservice architecture, it helps services to achieve dynamic registration, discovery and load balancing, and improves system flexibility and scalability.
- Cluster management and monitoring: Monitor the status of each node in the cluster in real time, detect and deal with faulty nodes in a timely manner, and ensure the stable and efficient operation of the cluster.
Practical Application of Zookeeper
In actual scenarios, Zookeeper is often closely integrated with other big data components.
For example, in a Hadoop cluster, it is used to manage the high availability of NameNodes, ensuring rapid switching to the standby node in the event of a primary NameNode failure, ensuring continuity of data storage and processing.
In HBase, Zookeeper maintains the metadata information of the cluster, implements distributed locking and other functions, and provides support for efficient data access and management.
Deeply understanding and proficient in using Zookeeper's principles and usage methods can help developers build more stable and scalable distributed systems to meet the complex needs of big data processing.
4.2 Message Queue Kafka
In the torrent of big data, Kafka is like an efficient information highway, carrying massive data flow.
Performance and Application of Kafka
With its outstanding performance, Kafka has become the core hub for data acquisition and transmission in big data processing.
In the log collection system, each server node continuously sends log data to Kafka, and then consumes it by other services and performs in-depth processing and analysis.
This architecture not only ensures high availability data transmission, but also avoids the risk of data loss.
In addition to data acquisition and transmission, Kafka also performs well in building real-time data stream processing systems.
By connecting data streams to Kafka, combined with Kafka Streams or other stream processing frameworks, such as Apache Flink, Apache Storm, etc., real-time monitoring, real-time recommendation and other functions can be realized to provide timely support for business decisions.
Kafka deployment and management
In practical applications, deploying and managing Kafka clusters requires considering many factors thoroughly.
The planning of the number of nodes should be determined based on the amount of data and processing requirements to ensure sufficient computing and storage resources.
The selection of disk capacity should fully estimate the data growth to ensure data storage security.
The configuration of network bandwidth must meet the high-speed requirements for data transmission and avoid network bottlenecks.
In order to achieve stable and high performance of Kafka clusters, reasonable configuration and optimization are crucial.
Adjust parameters to improve throughput and reduce latency, and promptly discover and resolve potential problems through monitoring and alarm mechanisms to ensure that the cluster is always in a healthy state.
Kafka's security and scalability
Kafka supports a variety of security authentication mechanisms, such as SASL authentication and SSL encrypted communication, building a solid security line for data transmission.
Its good scalability makes it easy to scale the size and performance of the cluster by increasing the number of nodes, meeting the growing data processing needs.
When using Kafka, you also need to focus on integration and collaboration with other big data components.
Combining with frameworks such as Hadoop and Spark can build a more powerful and efficient data processing and analysis system to achieve seamless data flow and in-depth mining.
Kafka's wide application and practical value in the field of big data cannot be underestimated.
Mastering the principles, usage methods and collaboration with other components is a necessary skill for big data engineers, which can effectively improve the efficiency and quality of data processing.
4.3 Task Scheduling Tool Azkaban
In the busy work of big data development, Azkaban is like a rigorous dispatcher, carefully arranging the execution order of various tasks.
Key features of Azkaban
Azkaban has comprehensive and easy-to-use functions, covering important aspects such as workflow management, task scheduling, task dependency management, and execution history viewing.
Users can easily create, configure and monitor big data processing processes to ensure that every link can be executed accurately.
It supports a variety of task types, including Hadoop MapReduce jobs, Hive queries, Pig scripts, etc., which perfectly blends with components in the Hadoop ecosystem.
How to use Azkaban
When using Azkaban, first create a project to organize your workflows and tasks.
Through its web interface or API, users can easily define and configure workflows.
A workflow consists of a series of interrelated tasks, such as data extraction, conversion and loading.
Users can set the dependencies between tasks accurately to ensure that tasks are executed in the expected order.
Once the workflow configuration is complete, Azkaban automatically schedules and executes tasks based on the dependencies by triggering execution.
At the same time, real-time execution monitoring and detailed log viewing functions allow users to keep abreast of the execution of tasks at any time and discover and solve problems in a timely manner.
Azkaban's flexible scheduling
In addition to basic scheduling capabilities, Azkaban also supports flexible timing scheduling and triggering condition settings.
Users can accurately set the execution frequency and trigger conditions of the workflow according to business needs to achieve refined task management.
In general, Azkaban provides efficient and reliable workflow scheduling solutions for big data development with its powerful functions and convenient operations.
It not only improves the efficiency and accuracy of data processing, but also greatly reduces the complexity of task management and becomes a right-hand assistant for big data developers.
4.4 Big Data Platform Tools CDH
CDH (Cloudera's Distribution Including Apache Hadoop) is an outstanding representative of the big data platform, providing a comprehensive and powerful solution for enterprise data processing.
Analysis of the principle of CDH
CDH is based on Hadoop's distributed file system (HDFS) and MapReduce programming model, implementing a separate architecture between data storage and computing.
HDFS can store massive data safely and reliably with high fault tolerance and scalability.
MapReduce provides an efficient distributed computing framework for processing and analyzing large-scale data.
In addition, CDH integrates rich components such as Hive, Hbase, Spark, etc., further expanding the capabilities of big data processing and analysis.
Advantages of CDH
CDH provides an intuitive and easy-to-use management interface and rich API interfaces, lowering the application threshold of big data technology.
Users can easily deploy, configure and monitor big data clusters through the management interface to achieve effective management of cluster resources.
Its API interface supports integration with other systems and tools, promotes data circulation and sharing, and improves the flexibility and efficiency of data processing.
CDH's functional expansion
In addition to basic data storage and computing functions, CDH is also equipped with a variety of data import, export and conversion tools, such as Sqoop, Flume, etc., to facilitate users to collect and integrate data.
At the same time, it supports a variety of data analysis tools and programming languages, such as Impala, Python, etc., to meet users' diverse data analysis needs.
Practical Application and Optimization of CDH
In practical applications, CDH has become the first choice for many enterprises to build data warehouses, conduct data analysis and mining with its stable and reliable performance and rich functional characteristics.
In order to give full play to the advantages of CDH, users can adjust the configuration parameters of Hadoop cluster according to their own business needs and technical characteristics, optimize the data import and export process, and select appropriate data analysis tools and algorithms, thereby improving the efficiency and accuracy of big data processing and analysis.
With the continuous evolution of technology, CDH is also continuing to upgrade and improve, bringing more innovative functions and high-quality experiences to users.
Continuously focusing on the characteristics of new technologies, constantly learning and exploring best practices are the key to big data developers and data scientists making full use of CDH.
Chapter 5 Data calculation and analysis technology
5.1 Spark system principle and practical combat
In the vast ocean of big data, Spark is like a powerful flagship, leading the trend of data computing and analysis.
Spark's basic architecture and core components
Spark adopts a master-slave structure, where the Driver program is like a commander, responsible for the scheduling of tasks and the allocation of resources, ensuring that the entire computing process is orderly. The Executor program is like a soldier who charges, focusing on the execution of specific tasks and efficiently completing the important task of data processing.
In addition, Spark's rich APIs and operators provide a variety of tools for data processing. Whether it is simple data conversion or complex aggregation operations, you can find the corresponding method and easily implement it.
Spark's RDD model
RDD (Resilient Distributed Dataset) is the core concept of Spark. It represents an immutable, partitionable and distributed data set that can be computed in parallel.
Through RDD conversion operations, such as map, filter, etc., various preprocessing and filtering of data can be performed. Action operations, such as count, collect, etc., trigger the actual calculation and return the result.
At the same time, Spark also provides support for higher-level data structures such as DataFrame and Dataset, which are more convenient and efficient when processing structured data.
Spark's practical application
In practical applications, Spark SQL provides strong support for the query and analysis of structured data. It can seamlessly connect with traditional SQL syntax, allowing data analysts to easily get started and quickly obtain valuable information from massive data.
Spark Streaming focuses on the processing of real-time data flow, suitable for real-time monitoring, real-time recommendation and other scenarios. Ability to capture and process continuous data in real time, and make timely responses and decisions.
Spark MLlib provides convenience for the training and prediction of machine learning models. Whether it is common machine learning tasks such as classification, regression or clustering, they can be efficiently completed in Spark.
Spark performance tuning
Although Spark itself performs well, it still needs to be tuned according to the specific situation in actual projects.
Reasonable setting of parallelism is one of the keys. Adjust the degree of parallel execution of tasks based on the amount of data and computing resources to avoid wasting or insufficient resources.
Optimizing data skew is another important aspect. When the amount of data in some partitions is much larger than that in other partitions, it will lead to inefficient computing. The data skew problem is solved through appropriate technical means, such as data redistribution, the use of aggregate functions, etc.
Making full use of caching is also an effective strategy to improve performance. Cache frequently used data into memory, reduce duplicate calculations, and improve data access speed.
A deep understanding of Spark's entire system principles and performance tuning combined with practical experience can better utilize Spark's powerful power in big data computing and analysis.
5.2 Flink's full system principle and practical combat
As a shining star in the field of stream processing, Flink has brought new vitality to the real-time computing of big data.
Overview of Flink Principles
Flink builds its core stream data processing mechanism based on event time processing and state management.
Identify the order of events by timestamping to ensure the accuracy and consistency of data processing. Rich APIs and operators allow users to flexibly customize various complex stream processing logics.
In the execution of computing tasks, Flink divides the tasks into multiple parallel subtasks, and different nodes in the cluster work together to achieve efficient data processing.
The powerful state management mechanism enables the calculation state to be quickly restored when a failure occurs, ensuring the integrity and correctness of the data.
Flink practical application
In real-time log analysis, Flink can quickly process massive log data, timely detect abnormal behaviors of the system, and provide strong support for troubleshooting and performance optimization.
In terms of real-time data stream processing, finance, e-commerce and other fields have extremely high requirements for real-time response to market changes. Flink can realize key functions such as real-time transaction monitoring and user behavior analysis to help enterprises make timely decisions.
During the real-time ETL process, Flink can accelerate the synchronization of data from the source system to the target system, ensure the timeliness and accuracy of data, and provide a solid foundation for the construction of data warehouses.
Flink performance tuning
Rationally setting parallelism is the basis for improving Flink performance. According to the data volume and cluster resources, adjust the parallelism of tasks to achieve optimal computing efficiency.
Optimized state management can reduce memory usage and improve processing efficiency. By reasonably partitioning and compressing state data, we ensure that while ensuring calculation accuracy, resource consumption is reduced.
Carefully adjust the size and sliding interval of the time window to achieve the best balance between accuracy and performance of the calculation results.
Use the monitoring tools and performance indicators provided by Flink to deeply analyze the execution of tasks and promptly discover and resolve performance bottlenecks.
Deeply mastering Flink's principles, practical applications and performance tuning methods can allow us to be in the battlefield of real-time big data computing and respond to various complex business needs.
5.3 The practical principle of Kafka in real-time scenarios
On the battlefield of real-time data processing, Kafka is like a solid fortress, providing reliable guarantees for the rapid transmission and processing of data.
The core principles of Kafka
Kafka's distributed architecture and message queue design are key to its success.
High scalability and fault tolerance are achieved through data partitioning and multi-copy storage. Even if some nodes fail, it can ensure reliable storage and transmission of data.
Kafka's real-time application
As a transit station for data streams, Kafka receives real-time data from various data sources and passes it accurately to downstream processing systems.
In real-time data analysis, the Kafka Streams API allows developers to build complex stream processing applications on Kafka, real-time conversion, filtering, and aggregation of data.
Kafka's performance optimization
Understanding Kafka's partitioning mechanism is critical to optimizing performance. Rationally designing partitioning strategies can improve the parallel processing capabilities of data and improve processing efficiency.
Familiar with the fault tolerance mechanism, it can quickly restore data transmission when a fault occurs, ensuring the continuity of data flow.
A deep understanding of the principles and applications of Kafka in real-time scenarios can allow us to better utilize its powerful functions to meet various needs of real-time data processing.
Chapter 6 Data Visualization and Data Analysis Expansion
6.1 Introduction to data visualization tools
In the vast ocean of big data, data visualization tools are like bright pearls, helping us transform complex data into clear and easy-to-understand graphs and charts.
PowerBI
PowerBI is launched by Microsoft and is a powerful business intelligence tool.
It provides a wide variety of visualization options, from common bar charts, line charts, pie charts, to more complex maps, funnel charts, etc. Users can choose the most appropriate chart type to present the data based on the characteristics of the data and analysis needs.
Its interactive data analysis function is a highlight. Users can explore the details of data in depth through clicks, filters, drilling and other operations and discover the rules and trends hidden behind the data.
In addition, PowerBI's cloud collaboration capabilities allow team members to share and discuss data anytime, anywhere, greatly improving the efficiency and accuracy of decision making.
Superset
Superset is an open source data visualization platform tailored for big data environments.
It supports a variety of data sources, whether it is a traditional relational database or an emerging big data storage system, and can be easily accessed.
Superset provides highly customized visualization options that allow users to create unique charts and dashboards based on their needs. Its powerful SQL editor allows data analysts to query and analyze data flexibly to meet complex business needs.
Tableau
Tableau is a leader in the field of data visualization and is widely used in the field of business intelligence.
It has an intuitive and friendly user interface that can be quickly used by even beginners. Rich visualization options and powerful data connectivity capabilities make it easy to deal with a variety of data sources and data types.
Tableau supports interactive data analysis and data mining, helping users discover associations and trends in data and providing strong support for decision-making.
In general, these data visualization tools have their own characteristics, and users should choose tools that suit them according to their actual needs and usage scenarios. Whether it is enterprise-level business intelligence analysis or in-depth exploration in the big data environment, you can find the right tools to show the beauty of data.
6.2 Python Data Analysis Basics
In the world of big data, Python data analysis is like a magical key, opening the door to insight into the mystery of data.
Python Language Basics
Mastering the basics of Python is the cornerstone of data analysis.
Concepts such as variables, data types, control flow statements and functions are basic elements of programming. Variables are used to store data. The data type determines the nature and operation mode of the data. Control flow statements such as conditional judgment and loops allow programs to perform different operations according to different situations, and functions are used to decompose complex tasks into reusable modules.
Pandas Library
Pandas is one of the core libraries for Python data analysis.
It provides efficient data structures such as DataFrame that can easily process tabular data. Operations such as data cleaning, conversion and filtering are made simple and intuitive in Pandas. Through functions such as indexing, merging, and grouping operations, data can be processed and analyzed flexibly.
Numpy library
Numpy is the basic library for scientific computing in Python.
It provides powerful N-dimensional array objects and rich mathematical functions that can efficiently perform numerical calculations. Numpy's performance advantages are particularly obvious when dealing with large multidimensional arrays and matrices, providing solid support for numerical calculations in data analysis.
Matplotlib library
Matplotlib is an important library in Python for creating visual charts.
It can draw various types of charts, such as line charts, bar charts, scatter charts, etc. By converting data into intuitive graphs, we can better understand the distribution and trends of data and discover patterns and anomalies in the data.
In the learning process, practicing through practical cases is the key to consolidating knowledge and improving skills. From reading data, cleaning processing to visual analysis, each step allows us to understand the charm of Python data analysis more deeply.
At the same time, reading other people's excellent code and participating in open source projects can broaden your horizons and learn more advanced data analysis methods and techniques.
The basis of Python data analysis is an important part of big data development and provides us with powerful equipment for navigating the ocean of data.
6.3 Basics and Applications of Machine Learning
In the vast world of data analytics, machine learning is leading change at an astonishing pace.
Types of machine learning
Supervised learning is the training of a model under the guidance of marked data to enable it to predict unknown data. For example, in image recognition, the training model to identify different objects by labeling a large amount of image data.
Unsupervised learning is about finding internal structures and patterns in unlabeled data. The clustering algorithm groupes similar data points into a group, and the dimensionality reduction algorithm is used to reduce the dimensions of the data in order to better understand and process the data.
Reinforcement learning allows the model to learn the optimal strategy through interaction with the environment and reward mechanism. It has a wide range of applications in the fields of gaming and robot control.
Application areas of machine learning
In the e-commerce field, by analyzing users' purchasing history and browsing behavior, using machine learning to personalize recommendations to improve users' purchasing conversion rate and satisfaction.
In the financial field, the credit scoring model uses machine learning to predict customers’ credit risks and provides a basis for loan decisions. Stock price prediction models try to capture market dynamics and provide reference for investment decisions.
In the medical field, machine learning can assist in disease diagnosis, drug development and medical image analysis, improving medical efficiency and accuracy.
Master machine learning technology
To master machine learning technology, you must not only understand the principles of the algorithm, but also be able to choose appropriate models and evaluation indicators.
Model selection requires consideration of factors such as the characteristics of the data, the nature of the problem, and computing resources. Common evaluation indicators such as accuracy, recall, F1 value, etc. are used to measure the performance of the model.
Programming capabilities are also essential skills for realizing and experimental machine learning algorithms. Python has become the mainstream programming language in the field of machine learning with its rich machine learning libraries, such as Scikit-learn, TensorFlow, PyTorch, etc.
With the development of big data technology, the integration of machine learning and data analysis will be closer. Continuous exploration and innovation, apply machine learning to more fields, solve practical problems, and create greater value.