You're venturing into data engineering. Which programming languages are crucial for your success?

For python: 1. Rich Ecosystem: Python has a vast ecosystem of libraries and frameworks specifically designed for data engineering tasks. Some essential libraries include: - pandas - NumPy - scikit-learn - Apache Spark - SQLAlchemy 2. Data Processing and Manipulation: Python provides a wide range of tools and libraries for efficient data processing and manipulation. Python's simplicity and expressive syntax make it easy to write readable and maintainable code. 3. Scalability and high-performance data pipelines. Tools like Apache Airflow and Luigi allow you to create and schedule complex data workflows. Parallel processing and distributed computing frameworks like Dask and Ray.

Python is an excellent companion, simple yet extremely powerful for managing powerful data manipulation tasks. I have created complete data ingestion tools for data lakes in a matter of few days. For me, it is indispensable, the primary tool for a data engineer.

Python is really just the beginning. It is a wonderful place to start because of the overwhelming size of the community and educational tools that already exist. Same can be said of the robust tooling options. There is no shortage of any of these things and learning Python can be fun, with quick and constant "wins". Where amateur data engineers evolve though, is in where they go next, whether its deep dives on some of Python's best packages, like pandas and PyTorch, or adding specialized languages, like Go and Rust, both of which are likely better options for building the myriad APIs you will inevitably surround yourself with as your career takes off!

In my experience, Python has become a game-changer for data engineering. Its readability and ease of use allow us to focus on complex tasks. Powerful libraries like Pandas and NumPy streamline data manipulation and analysis, saving us tons of time. Visualization with Matplotlib brings data to life for clear communication. Python's integration with popular frameworks like Apache Spark further extends its reach, making it an essential tool for handling large-scale data processing. Simply put, Python empowers us to be more efficient and effective data engineers.

Key programming languages for data engineering include Python for its versatility and extensive libraries, SQL for database management and querying, and Java or Scala for working with big data frameworks like Apache Hadoop and Spark. Additionally, familiarity with languages like R for statistical analysis and Bash or Shell scripting for automation can be beneficial.

I have hardly seen a data engineer who lives without SQL!. SQL is the primary language used for interacting with databases, querying data, and performing data manipulation tasks. As a data engineer, having a strong command of SQL enables you to retrieve, filter, and analyze data efficiently. Understanding SQL functions, joins, aggregations, and complex queries empowers you to extract valuable insights from databases and optimize data workflows. Whether working with relational databases or data warehouses, proficiency in SQL is a foundational skill that is indispensable for data engineering success.

SQL é essencial para interagir com bancos de dados relacionais, permitindo consultas eficientes e manipula??o de dados. Proficiência em SQL é crucial para qualquer engenheiro de dados que precise trabalhar com dados estruturados.

Not just data engineering, SQL intersects with many profiles like business analytics, data analytics and science and machine learning profiles, it’s great if you’re planning to switch profile, and easy for also non-tech peeps to get hang off.

SQL is the cornerstone of data engineering. It unlocks the power of relational databases, allowing us to not only retrieve data but also manipulate it for analysis. From updating records and creating tables to crafting intricate queries, SQL grants us the ability to shape the data to our needs. And because it's ubiquitous across various database systems, proficiency in SQL translates to flexibility and efficiency across different projects. It's a fundamental tool that empowers us to interact with and leverage the vast potential of data.

In today's market, many other languages, especially python is emerging as one huge giant, fit-for-all language but not in even in the next 20 years, SQL is going to go away, not anytime soon. SQL is and will be the most demanded language in this aspect, let's say you're working at a new state-of-the-art setup with python & R, the day you'd need to perform any integration with vendor/source anything, you'd need to have SQL's knowledge.

Java is indeed a powerful and versatile language, especially in the context of data engineering and backend development. Mastering Java involves understanding its syntax, object-oriented principles, and common design patterns.

Java is a versatile and powerful programming language known for its platform independence. In the field of data engineering and data science, Java is widely used for big data processing, data integration, and real-time data processing. Frameworks such as Apache Hadoop and Apache Spark are built using Java, making it ideal for handling large-scale data processing jobs. Additionally, Java's support for multithreading and scalability is beneficial for building responsive and high-performance data applications. Java also excels in database connectivity and enterprise data applications. It supports robust ETL (Extract, Transform, Load) pipelines and integrates well with various databases through JDBC.

Scala is particularly effective in real-time information processing. Its performance and real-time processing capabilities make it ideal for handling high-velocity data, crucial in domains like ad-serving systems and real-time analytics. Furthermore, Scala’s compatibility with Apache Spark enhances its suitability for real-time data processing tasks. Therefore, for data engineers dealing with real-time data processing, Scala emerges as a compelling choice due to its performance, real-time processing capabilities, and seamless integration with Apache Spark.

Bash scripting is one of the most undervalued talents in the game. Those engineers that I've worked with that do not utilize it and rely more on tooling and GUIs seem to do just fine googling commands when they need them, but the really special engineers that I've worked with are able to go beyond the tools, interact with the data directly and swiftly and accomplish so much more in such a shorter amount of time. Spending less time doing the mundane leaves lots of time to play and test new theories and strategies, which is the work that makes those special engineers so special.

Conhecimentos em Bash s?o úteis para automatizar tarefas de linha de comando e gerenciar fluxos de trabalho em sistemas baseados em Linux, essenciais para a gest?o eficiente de dados.

The knoweledge of additional tools and technologies is crucial. - Apache Hadoop Ecosystem: Knowledge of tools like HDFS, MapReduce, Hive, and Pig. - Apache Spark: For large-scale data processing. - Kafka: For real-time data streaming and integration. - Airflow or/and Data Factory: For workflow orchestration and scheduling. - NoSQL Databases: Such as MongoDB, Cassandra, and Redis for non-relational data storage. - Cloud Platforms: Familiarity with AWS, Google Cloud, and Azure services for data storage, processing, and analytics.

Cloud: Il faut noter que la data se déplace de plus en plus vers le cloud, connaitre l'architecture et le fonctionnement du cloud ne peut être que bénéfique pour la mise en place de la donnée. Les clouds les plus cités sont AWS et Azure, mais d'autres existent !