Hier erfahren Sie, wie Sie als Anfänger im Data Engineering häufige Herausforderungen meistern können.

📚Master foundational concepts like databases, SQL, ETL processes, and data pipelines to overcome beginner challenges in data engineering. ⚙️Learn SQL thoroughly since it’s a core tool for managing relational databases. 🔄Understand the ETL (Extract, Transform, Load) process as it’s critical for moving and transforming data. 🔧Get familiar with data warehousing and the difference between structured and unstructured data. 🚀Practice building data pipelines using tools like Apache Airflow or similar orchestration tools. 🌐Focus on cloud platforms like AWS, Azure or GCP

Spend 3-4 weeks mastering the basics, including Exploratory Data Analysis and ETL using Python. Start by selecting projects from Kaggle, focusing on understanding the problem statements and framing solutions before diving into the code. Aim to complete at least three projects in each topic to gain a solid grasp of problem-solving.

Overcoming challenges as a beginner in Data Engineering is like navigating a maze. Start by mastering basics like SQL and Python. Break down problems into smaller parts, embrace trial and error, and learn from each misstep. Seek guidance from mentors and communities, and with persistence, you'll find your way through to success.

As a beginner in this field, one should follow two broad set of plans approach - let’s say them A & B. During PLAN A - Understand the infrastructure of your pipeline, infrastructure includes - what all components, failure handling strategy, important code scripts like triggering scripts, processors of the pipeline. PLAN B - understand the data - flow of the data important tables, decrypting the sql, spark code. With these steps one can understand the end-to-end flow.

Begin with the core skills—SQL for querying databases and Python for data manipulation using libraries like Pandas and NumPy. Grasp the principles of ETL (Extract, Transform, Load), which is fundamental to data engineering. Learn how to build pipelines that move and transform data efficiently. As cloud technology is central to modern data engineering, pick one cloud platform—AWS, Azure, or GCP. While starting with SQL and Python is crucial, try to get hands-on experience with tools like Snowflake, PySpark, or any distributed data processing frameworks. Understanding data modeling is key to structuring and organizing data effectively. As you progress, you'll need to design efficient models to support analytics and reporting.

My suggestion for beginners is to use platforms like Kaggle, Google Cloud, and Azure to train your cloud skills. Kaggle offers a wide range of public datasets and competitions that are great for practicing data processing and analysis, as well as a notebook environment that comes preconfigured with various libraries and cloud resources ready for use. On Google Cloud, you can explore services like BigQuery for large-scale data queries and Dataflow for processing pipelines. On Azure, you can experiment with tools like Azure Data Factory for data orchestration and Azure Synapse Analytics for large-scale data analysis. These platforms provide real environments where you can learn and practice, building practical cloud experience.

🚀 Essential Tools to Master: Data Processing Frameworks: Get hands-on with Apache Hadoop and Apache Spark—the go-to frameworks for handling big data. Data Storage & Management: SQL Databases: Learn MySQL and PostgreSQL for structured data management. NoSQL Databases: Explore MongoDB for managing unstructured and semi-structured data. Cloud Platforms: Familiarize yourself with AWS, Microsoft Azure, or Google Cloud Platform (GCP)—key players in the cloud ecosystem, offering scalable data solutions. 🔍 Invest in Your Skills: The right tools can empower you to tackle any data challenge that comes your way! 🛠️

So basically, don’t try to learn too many things because mastering all trades is not possible. You need to be very specific in choosing a cloud provider to start your journey. I preferred Azure because it has a lot of content to explore and is used in most organizations, whether product-based or service-based. After choosing Azure, my interest grew as I learned Azure Data Factory (ADF), which helped me illustrate the flow of data. I always wanted to work with it, and honestly, it’s very easy to use. Later, I discovered many other Azure tools like ADB, and so on.

Absolutely, becoming proficient with the right tools is key in data engineering. Early in my career, I focused on learning Apache Spark, which significantly improved my ability to process large datasets efficiently. I also gained hands-on experience with MySQL and PostgreSQL, which deepened my understanding of relational databases. Later, I explored cloud platforms like AWS, mastering services such as S3 for storage and Redshift for data warehousing. By experimenting with these tools in real projects, I could handle increasingly complex data engineering tasks and stay relevant in a rapidly evolving field.

Instead of spreading yourself too thin across multiple tools, focus on mastering one cloud platform, such as AWS, Azure, or GCP, and one data processing framework like Apache Spark. This depth of knowledge will serve as a strong foundation as you expand your toolset.

Be patient with data modelling, especially when designing systems from scratch, a well made data model with comprehensive relationships will save you a lot of time in the long run. Also, pay attention to your indexes!!

Data modeling is indeed crucial. I remember a project where I had to redesign an existing data warehouse that was struggling with performance issues due to poor modeling. Initially, the schema was overly normalized, leading to complex queries and slow performance. By applying a star schema design, I simplified the structure, which significantly improved query speed and efficiency. Practicing with different scenarios, like this one, helped me understand when to apply specific modeling techniques, making my data solutions both scalable and performance-driven.

Long-running queries, slow report refreshes, and sluggish ML model performance can all be attributed to inefficient data modeling (and possibly indexing). By mastering star and snowflake schemas and practicing data modeling, you can significantly improve the efficiency of your data analysis and reporting. Efficient data modeling not only speeds up queries and reports but also enables more accurate and insightful analysis. Taking a OBT concept and breaking it up into fact and dimension tables reduces duplication and speeds up queries and reports. Practice splitting it up into dimensions and facts, then measure efficiency between the two. This helps you to be able to explain advantages of different methods in your interviews!

Data Modeling, huh… It’s just a fancy term. I don’t find it very interesting because the learning curve is very linear. If I say there are only two things to learn, that might be true. One is schema creation, and the second is normalization. Schemas, like star and snowflake, are essential to learn, but beyond that, normalization is just a concept. You don’t really need much else from modeling.

I think the first step is to understand the concepts of relational and non-relational modeling. Know the benefits of each and when to use them. For the relational model, use tools like MySQL Workbench to create tables and relate them with primary and foreign keys, and use SQL for querying. For the non-relational model, I suggest starting with storing data in JSON documents, which facilitates flexibility and scalability, especially for unstructured data. Practice with a personal project using public data and ask for feedback from your colleagues. These steps will help develop skills in data modeling.

Debugging is essential. I used to think that deduping data and checking for null values were the only important tasks, but I figured out that everything from checking the database name to the schema name and verifying the table are all vital steps. Using ADB for data validation is also very useful and reliable.

Learning to debug code is essential for anyone who wants to master a programming language and improve their automation workflows. To enhance your ability to find errors, using automated tests is an excellent option. They help ensure software quality and save time on manual testing.

O que mais encontrará na analise de dados e a falta de uma padronização. Por isso inicio de um projeto do zero tem sua facilidade, conseguindo criar o padrão do inicio e seus relatórios terá máxima eficiência e assertividade. Agora quando vai iniciar analise em uma base de dados sem uma padronização prepare sua paciente, fique ciente que chegar na padronização levara um tempo. Importante e saber que seus colaboradores ou cliente quando a "roda começa a girar corretamente" percebera que organização dos dados fara que o tempo utilizado para tomada de decisão será rápida e facilitada devido a padronização.

Data Engineers are the main checkpoint for business and are essentially in the middle of the entire data program. So, collaborative work is our only option. Whether it’s gathering requirements from a Business Analyst or answering a Power BI developer’s silly question, you need to be on every call to provide answers. And I forgot to mention, it’s mandatory for the entire Data Engineering team to be on production calls, even though the ratio of DEs to other teams like Power BI developers and Analysts is just 4:1. So, collaboration is our only option.”

Certainly. Here's an updated response incorporating those important aspects: To overcome challenges as a beginner in Data Engineering, start by building a strong foundation in programming, databases, and data structures. Focus on learning SQL, a general-purpose language like Python, and familiarize yourself with big data technologies and cloud platforms. Gain hands-on experience through projects, applying your skills to real-world data problems. Understanding your source data is also very important to effectively process it. Develop strong debugging and data validation skills to ensure data quality and integrity throughout the pipeline. Cultivate soft skills like communication and effective collaboration.

The use of tools like Git and GitHub is crucial for effective collaboration and software development. Git, a distributed version control system, allows developers to efficiently track and manage changes in source code. GitHub, a platform based on Git, facilitates collaboration by providing a centralized space to store, share, and review code. With GitHub, teams can work simultaneously on different parts of a project, easily integrate changes, and resolve code conflicts, which improves productivity and software quality. Additionally, these tools offer features for code review, issue tracking, and continuous integration, making the development process more agile and organized.

Data Engineering is highly collaborative. Work closely with data scientists, analysts, and business stakeholders. Practice translating complex technical details into layman’s terms, as effective communication is key to project success.

Quando tem o setor apenas para lidar, processo de criação e finalização e extremamente rápido. Agora lidar com diversas equipes, sua comunicação precisa sem limpa, sem ruído, fazer que as equipes compreenda suas necessidades e que dependendo de cada ferramenta tem sua limitação e estrutura.

To overcome challenges as a beginner in Data Engineering, follow these key strategies: 1.Continuously learn and stay updated on new tools. 2.Master core concepts like database(SQL),Cloud Tech - be strong enough in at least one (Azure, AWS,GCP), algorithms, and databases. 3. Develop strong problem-solving skills and break down complex tasks. 4.Use Git for version control to manage your code. 5.Prioritize thorough documentation for easy maintenance. 6. Implement testing to ensure data quality. Optimize data pipelines for performance. 7. Network with the community for insights and support. Attend meetups, and participate in online discussions. 8. Finally, remain adaptable to new tools and changing business needs.

O primeiro passo é não tentar abraçar tudo de uma vez. Aprenda aos poucos, começando do básico e avançando para o intermediário. Priorize o que realmente importa, como SQL e computação em nuvem. Durante uma transição de carreira, a inteligência emocional é fundamental. Muitas coisas podem tentar te desanimar, mas manter essa inteligência e o foco é essencial.