In today's digital age, big data has become an essential and important resource in businesses and organizations. For developers who want to succeed in the big data space, it is crucial to master big data programming skills. Below we will provide you with some useful tips and advice to help you achieve better results in big data programming.

     Part 1: Understanding Big Data Programming Basics

1. Understand the basic concepts of Big Data: Understand the definition, sources, and characteristics of Big Data, including the type, volume, velocity, and diversity of data. This will help you better understand the challenges and demands of Big Data programming.

2. Learn about Big Data platforms and technologies: familiarize yourself with mainstream Big Data platforms and technologies, such as Hadoop, Spark and NoSQL databases. Understand their features, advantages and applicable scenarios in order to choose the right tools and technologies for programming.

     Part II: Mastering Big Data Programming Languages and Tools

1. Learn big data programming language: choose a programming language suitable for big data development, such as Java, Python or Scala. Master the basic syntax and features of the language and learn to use it to program on Big Data platforms.

2. Using Big Data development tools: Familiarize yourself with the development tools and frameworks provided by Big Data platforms, such as Hadoop MapReduce, Apache Spark, and Apache Hive. These tools and frameworks can help you program and analyze Big Data more efficiently.

     Part III: Optimizing Big Data Application Performance

1. Design a scalable architecture: In a big data environment, application scalability is critical. Design architectures with good scalability and load balancing capabilities to cope with growing data volumes and user demands.

2. Optimize data processing and storage: Adopt appropriate data processing and storage strategies to improve the performance and efficiency of big data applications. Use reasonable data partitioning and indexing techniques to speed up data queries and analysis.

     Part IV: Securing Big Data Applications

1. Implement data privacy and protection measures: Data privacy and protection are critical in Big Data applications. Adopt appropriate data encryption and access control measures to protect sensitive data from unauthorized access.

2. Establish monitoring and security auditing system: Establish a comprehensive monitoring and security auditing system to detect and respond to potential security threats in a timely manner. Use security tools and techniques to secure big data applications.

Big Data programming is a complex and challenging task, but by understanding the basics, choosing the right tools and techniques, and focusing on performance and security, you will be able to succeed in the Big Data space. We hope that the advice and tips we've provided will help you perform effective big data programming and achieve your personal and professional development goals.