Welche Tools und Frameworks verwenden Data Engineers, um Daten-Workflows zu automatisieren und zu orchestrieren?

Data engineers utilizing Azure employ various tools and frameworks to streamline data workflows: 1. Azure Data Factory (ADF): This fully managed integration service enables creation, scheduling, and orchestration of data pipelines. 2. Azure Databricks: Unified analytics platform offering Apache Spark-based big data processing and ML capabilities. Data engineers can construct, train, and deploy pipelines using Spark and other open-source frameworks. 3. Azure Synapse Analytics: Unites big data and data warehousing capabilities, allowing data engineers to ingest, prepare, manage, and serve data for analytics and reporting purposes.

ETL est l'acronyme d'Extract, Transform et Load. L'ETL est un processus fondamental de l'ingénierie des données. Les ingénieurs de données conçoivent des pipelines ETL pour transformer les données brutes en un format adapté à l'analyse. Cela implique le nettoyage, l'agrégation et l'enrichissement des données, afin de s'assurer que les données sont utilisables par les Data Scientists et les Data Analysts.

1. Apache Airflow Purpose: Workflow orchestration and scheduling. Why Essential: Uses DAGs (Directed Acyclic Graphs) to define and manage complex workflows. 2. Apache Spark Purpose: Large-scale data processing. Why Essential: Handles both batch and stream processing, with support for SQL, machine learning, and graph processing. 3. Apache Kafka Purpose:Real-time data streaming. Why Essential:Enables high-throughput, low-latency data pipelines for streaming applications. 4. AWS Glue Purpose:Managed ETL service. Why Essential: Fully managed, serverless, and integrates seamlessly with AWS services for data preparation and loading. 5.Great Expectations Purpose: Data validation and quality. Why Essential: Ensures data reliability.

Data engineers often use tools like Apache Airflow, Azure Data Factory, or Prefect for orchestration. These help schedule and manage workflows easily. For automation, we use Python scripts, SQL, and sometimes Spark for handling big data. Cloud platforms like Azure, or GCP also have built-in tools to automate and monitor pipelines. The main goal is to make sure data flows smoothly with minimal manual work.

Experiment with version-controlled ETL/ELT configurations to enable rollback and disaster recovery. Tools like dbt or custom scripts can be paired with Git for maintaining trackable configurations for your pipelines, allowing you to iterate workflows safely without losing prior setups.

Data engineers leverage a variety of tools and frameworks to automate and orchestrate data workflows. Popular containerization tools like Docker and Kubernetes ensure consistency and scalability.For workflow management, Apache Airflow and Argo Workflows are widely used.Infrastructure as Code (IaC) tools such as Terraform and Ansible streamline cloud infrastructure deployment.Data orchestration tools like Apache NiFi and Prefect automate data integration and processing. These tools collectively enhance efficiency, reliability, and scalability in data engineering, enabling seamless data flow and real-time analytics. By mastering these technologies, data engineers can drive innovation and deliver impactful insights.

for on-premise, you can use widely used tools like Airflow, mage, prefect, etc. For AWS, there are Step Functions, Glue Jobs, and Custom orchestration using lambda and event bridge.

Databricks DLTs provide an excellent way to manage streaming flows. If you combine them with Databricks Asset Bundles, you can achieve a highly declarative approach. This setup eliminates the need to manually manage checkpointing, refreshes, and full refreshes when using Spark Streaming, while also ensuring CI/CD workflows for your deployments.(High Vendor Lock-in)

On GCP, Dataflow (powered by Apache Beam) and Dataproc (for Spark/Hadoop) enable scalable data processing, while BigQuery Data Transfer Service simplifies ELT workflows. AWS provides Glue (serverless ETL with Spark) and AWS Data Pipeline for data movement and transformation. Both platforms integrate with dbt for modular ELT workflows.

Workflow management tools are essential for orchestrating and automating complex data workflows. Here it goes some key topics about it: Task Scheduling Dependency Tracking Error Handling & Logging Alerting & Monitoring Integration Capabilities

Workflow tools (Airflow, Luigi, Prefect, Dagster) automate data pipelines. Airflow: robust, complex. Luigi: simple, batch-focused. Prefect: modern, failure-resistant. Dagster: data-centric, lineage. Choice depends on needs: scale, batch vs. real-time, cloud, data quality.

For orchestration, GCP’s Cloud Composer (managed Apache Airflow) allows complex workflow automation across services. AWS Step Functions provides a visual workflow orchestrator, while Managed Workflows for Apache Airflow (MWAA) brings Airflow’s flexibility to AWS.

Workflow management is vital in any pipeline, navigating the complexity of processing and the errors than can appear sometimes it takes more that a VM with a Python script. Tools like Airflow, Dagster, Prefect or Luigi gives you the possibility of linking different task in different services (or not-it's how you prefer!) to make your pipelines more controlled, more resistant to failure and manageable while gaining complexity due to the modularization of the processes.

Workflow management tools are essential for orchestrating complex tasks in data pipelines, offering automation and control through features such as scheduling and error handling. The ability to code workflows in Python gives engineers the flexibility to adapt and expand data operations.

In modern data engineering, workflow management tools are essential for automating and orchestrating data pipelines. They enable scheduling, handling task dependencies, and monitoring in real time, reducing manual intervention and errors. Tools such as Apache Airflow, Luigi, and Prefect offer modular design, dynamic pipeline creation, and scalability across distributed systems. Their integration capabilities enhance operational efficiency and ensure consistent data quality, making them integral to effective data processing.

No meu dia a dia como engenheiro de dados, ferramentas como Apache Airflow e Prefect são indispensáveis para orquestrar pipelines complexos. Gosto especialmente da abordagem "code-first", que me dá total controle sobre dependências, agendamento e tratamento de falhas. Ter logs detalhados e alertas integrados facilita a identificação rápida de gargalos e falhas. Além disso, a integração com outras plataformas (clouds, bancos, APIs) é um diferencial essencial para manter os fluxos de dados consistentes e confiáveis.

Los ingenieros de datos utilizan una variedad de herramientas y marcos para automatizar y orquestar los flujos de trabajo de datos. Herramientas de orquestación 1. Apache Airflow: Un marco de código abierto para programar y monitorear flujos de trabajo. 2. Apache NiFi: Un sistema de gestión de flujos de datos que permite diseñar y automatizar flujos de trabajo. 3. AWS Step Functions: Un servicio de AWS que permite crear y gestionar flujos de trabajo.

GCP offers Dataplex, which includes built-in data quality monitoring, while AWS Glue DataBrew provides no-code transformations and profiling. Open-source tools like Great Expectations can be deployed on both platforms for advanced data validation.

Data quality tools have evolved to integrate validation directly into pipelines, automating tests and reducing operational risks and costs. The growing sophistication of data quality tools reflects a shift in data engineering, making quality a fundamental component integrated into the workflow.

Garantir a qualidade dos dados é tão importante quanto movimentá-los. Utilizo ferramentas como Great Expectations e Soda para definir regras claras de validação e detectar problemas como valores nulos inesperados, duplicações e anomalias. Isso me permite "quebrar" o pipeline antes que dados problemáticos avancem para análises ou produtos. Gosto de integrá-las diretamente aos pipelines no Airflow ou dbt, automatizando as checagens e gerando relatórios de qualidade contínuos que apoiam tanto a área técnica quanto o negócio.

Borrowing From Software Engineering—But Not Blindly Data engineers often adopt software engineering testing principles, but data testing is fundamentally different. Unlike deterministic software tests, data tests deal with ever-changing inputs, unknown edge cases, and scale variations. Instead of unit tests, we need data assertions that evolve with the data. Instead of CI/CD, we need CI/CD/CT (Continuous Testing). Tools like pytest and dbt are great, but they must be combined with monitoring and sampling techniques to catch silent failures. Are we copying software testing strategies without adapting them to the messy reality of data?

On GCP, BigQuery’s Assertions (Preview) allow SQL-based testing, and Cloud Functions can automate validation tasks. AWS Deequ, built on Spark, enables scalable data quality checks within Glue or EMR. Both platforms integrate with dbt tests for transformation-level validation.

Considero os testes automatizados uma etapa essencial para garantir a confiabilidade dos pipelines. Ferramentas como pytest e unittest são muito importantes para garantir a qualidade do código. Para testes de carga e performance, o Locust é útil quando preciso simular ingestões em larga escala. Incorporar esses testes em pipelines CI/CD ajuda a manter a integridade dos dados e a confiança nas entregas, principalmente em ambientes críticos e sensíveis a erros.

Uma ferramenta de visualização de dados que possui uma versão open source, é amigável para a experiência do usuário e conta com um query builder para apoiar usuários não técnicos é o Metabase. Ela facilita a conexão entre times de produto e times de dados, tornando mais fácil e acessível para os usuários tomarem decisões baseadas em dados no dia a dia.

The Engineer’s Blind Spot—Why Storytelling Matters Many data engineers stop at building pipelines, assuming visualization is an analyst’s job. But a well-structured pipeline anticipates visualization needs—creating metadata, meaningful joins, and performance-optimized datasets for tools like Power BI and Looker. Data engineers should think of visualization as the final mile of the pipeline, not an afterthought. If a dashboard isn’t answering the right questions, the pipeline design might be at fault. Do we treat data visualization as an endpoint, or as a feedback loop for better engineering?

Looker (part of Google Cloud) provides enterprise-grade BI with deep BigQuery integration. AWS QuickSight is a scalable alternative with ML-powered insights. Both platforms support Tableau, Power BI, and open-source tools like Apache Superset for interactive reporting.

Embora visualização de dados nem sempre seja o foco principal do engenheiro de dados, gosto de colaborar com analistas e cientistas de dados usando ferramentas como Looker ou Power BI para validar a consistência dos dados e garantir que as métricas estejam corretas no pipeline. Além disso, uso Matplotlib ou Seaborn em análises exploratórias durante o desenvolvimento, o que ajuda a entender rapidamente padrões ou detectar outliers que poderiam afetar a qualidade dos dados processados.

Add periodic documentation audits to your project schedule to ensure that schemas, transformations, and workflows match real-life implementations. Use tools such as Dataedo or Schemacrawler to build comparison reports that keep your documentation up to date.

GCP users can leverage Dataplex and Data Catalog for metadata management, while AWS Glue Data Catalog centralizes schema documentation. Open-source options like Amundsen and DataHub integrate well with both cloud ecosystems for improved discoverability.

Uma ferramenta que, entre outras coisas, permite documentação é o OpenMetadata. Ela é usada para centralizar catálogo, governança, observabilidade e lineage. Você pode por exemplo catalogar seus dados e criar o lineage de ponta a ponta com conectores que permitem unificar diversas fontes de dados e os jobs/DAGs de orquestradores como o Dagster.

✅ Apache Airflow → Plataforma de código abierto para la orquestación de flujos de trabajo, ideal para la automatización de tareas complejas. ✅ Docker y Kubernetes → Facilitan la contenedorización y gestión de aplicaciones en entornos distribuidos. ✅ n8n → Herramienta de automatización de flujo de trabajo de código abierto con integración flexible. ✅ Prefect → Alternativa moderna a Airflow con enfoque en la facilidad de uso y escalabilidad. ✅ Luigi → Marco desarrollado por Spotify para la gestión de dependencias en flujos de datos.

The Future of Data Engineering is Not Just Tools—It’s Mindset It’s easy to get caught up in tools, but the best data engineers don’t just master Airflow or dbt—they master how to think about data. The future of data engineering will require: • Thinking in events, not just tables (real-time over batch) • Designing for reuse, not just one-off pipelines (modular, metadata-driven approaches) • Optimizing for data consumers, not just engineers (building for accessibility) The tools will change, but the engineers who focus on adaptability, automation, and stakeholder collaboration will always be ahead. What are you doing today to future-proof your data engineering skills?

Beyond tooling, governance, scalability, and cost efficiency should guide platform choices. GCP’s strong AI/ML integration makes it ideal for advanced analytics, while AWS excels in flexibility and multi-cloud interoperability. Choosing the right combination of tools ensures seamless, efficient, and reliable data workflows.