What skills do you need to work with cloud data platforms?

If we are looking from a Data Engineering perspective Azure or AWS should be the preferred choice. One should know EMR, Redshift, Athena, Glue, S3, Lambda in AWS cloud. Talking about Azure cloud, I feel 2 most important services are Databricks and Azure Datafactory. Synapse is also good to know.

Below are some skills that you need: 1. Computer Networking. Any deployment that you do is bound to some level of network access. 2. Administration. This includes creating and managing users/groups/service principles, Identity and Access Management(IAM) to your data services etc. 3. Data warehouses, DataLakes and Lakehouses. Data collection, modeling, and preprocessing operations. 4. Compute frameworks for processing and transforming the data. 5. Data Analytics and Information Visualization skills. 6. Automation using pipelines, CI/CD, Git knowledge, Infrastructure As Code (IAC). 7. SQL, Python skills. 8. Monitoring and Log analysis. 9. Data security and governance.

Knowledge about any one cloud platform is must. Let's say if you're familiar with azure you should know: 1. Azure databricks 2. Delta lake and delta engine 3. Azure data factory. 4. Azure synapse analytics. 5. Microsoft fabric (new trend)

We can divide skills required to work on cloud into two different sections, First primary skills :primary skills can be dependent on exact role ,for data engineers it could be databases,compute resources ,data orchestration tools,storage tools ,data lifecycle management,cloud native Data professing techniques . For network engineer it could be some networking skills . Secondary skills : We can say it secondary skills but this is must know for all cloud roles , It’s optimisation and security .one should know how to utilise cloud computing resources by paying least possible amount and without leaking data by doing some weak configurations .

According to me in Azure Cloud 1. Azure Data Lake Service 2. Azure Data Factory 3. Data Bricks 4. SQL 5. Azure Synapse Analytics

Knowing Cloud Data Architecture is highly important to become an expert in cloud computing. It forms the backbone for designing efficient and scalable data solutions in the cloud. Understanding how data is stored, processed, and managed in cloud environments is crucial for optimizing performance and ensuring data integrity. Cloud Data Architecture encompasses various components like databases, data lakes, and data warehouses, along with considerations for data security and compliance. Understanding these principles enables experts to architect robust and cost-effective data solutions, make informed decisions, and implement best practices in handling data on cloud platforms.

?? In my experiences; the architecture is both art and technical disciple; to start, you must understand how tools work in specific case as activities as well know some example of solution architecture implementations: this way you get knowledge and criterio about how use right tool in specific architectural context.

Properly designed cloud data architecture can optimize costs by ensuring that you only pay for the resources you use. This includes the ability to automatically scale down resources during periods of lower demand, avoiding unnecessary expenses.

Cloud Data Architecture: a. Data Storage Services: Skill Requirement: Proficiency in utilizing cloud-based data storage services (e.g., Amazon S3, Azure Blob Storage). b. Database Services: Skill Requirement: Knowledge of cloud database services (e.g., Amazon RDS, Azure Cosmos DB) and their use cases. c. Data Warehousing: Skill Requirement: Understanding cloud data warehousing solutions (e.g., Amazon Redshift, Google BigQuery) for analytics.

La arquitectura de datos en la nube no es estática; está en constante evolución con la aparición de nuevas tecnologías y prácticas. Mantenerse actualizado a través de la formación continua y la experimentación práctica es crucial para tener éxito en este campo. Es fundamental para dise?ar, implementar y administrar eficientemente soluciones basadas en la nube. Aspectos clave a tener en cuenta para una buena arquitectura de datos: Integración de datos y ETL en la nube Gestión y almacenamiento de datos Seguridad de datos en la nube Optimización y Escalabilidad Automatización y Orquestación Dise?o de Sistemas Distribuidos Arquitecturas de Microservicios y Contenedores Continuidad del Negocio

Knowing Cloud Data Development is crucial to becoming a cloud expert. It involves coding, testing, and deploying data-centric applications. Proficiency in Python, SQL, and frameworks like Apache Spark is essential. Familiarity with cloud-native tools ensures efficient testing for data quality. Expertise in deploying on platforms like AWS, Azure is vital for scalability. These skills empower experts to leverage cloud technologies fully, implement data solutions effectively, and contribute to the seamless integration of data-driven applications in the ever-evolving cloud computing landscape.

From my perspective, you have to be familiar with some of the cloud-native services offered by the AWS Cloud. Such as AWS Glue, AWS Database Migration Service (AWS DMS), and Lambda for extracting, transforming, and loading data. For structured data processing, you need to be proficient in AWS RDS and RedShift databases. To schedule a pipeline using the CloudWatch service and to monitor pipelines in AWS, you need to know about CloudWatch and CloudTrail. For large-scale data processing, use a custom Spark cluster or AWS EMR. You should know how to use the S3 service for storing, retrieving, encrypting, and archiving datasets. Working on cloud-based or on-premises data processing requires good programming skills in Python, Spark, and Scala.

El desarrollo de datos en la nube es un campo dinámico que requiere una actualización constante de habilidades y conocimientos debido a la rápida evolución de las tecnologías y herramientas en la nube. Combina el desarrollo de software con habilidades específicas en manejo y análisis de datos. Los desarrolladores de datos necesitan ser competentes en lenguajes de programación como Python, familiarizarse con las APIs y los SDKs proporcionados por los servicios de nube, dise?ar y gestionar bases de datos en la nube, ya sean SQL o NoSQL, y entender las soluciones de almacenamiento de datos como Data Lakes y almacenamiento de objetos. Desarrollar habilidades en herramientas y plataformas para el manejo de big data, como Apache Hadoop, y Spark.

I will consider LOBs or department ETL where basic drag and drop is more than enough. For example, move every night data from an ERP to a Data Warehouse. Having these skills are important, but is not always required. Some of these tools, like OCI Data Integration generates the Apache Spark code for you. You have the flexibility of the easy to use of the GUI but adapt/improve/change the code.

Cloud Data Development: a. Programming Skills: Skill Requirement: Proficiency in programming languages commonly used in cloud environments (e.g., Python, Java, SQL). b. Data Integration Tools: Skill Requirement: Experience with cloud-based data integration tools (e.g., AWS Glue, Azure Data Factory) for ETL processes. c. Serverless Computing: Skill Requirement: Understanding server less computing concepts and using server less technologies for data processing (e.g., AWS Lambda).

In the realm of cloud data platforms, operational skills are paramount. This encompasses a nuanced understanding of language versioning, optimizing development for cost, resource efficiency, and speedier pipeline results. It extends to managing retention policies, delving into automation and orchestration tools, and ensuring the robust health of repositories. Additionally, a proficiency in DevOps skills, including continuous integration, deployment, and monitoring, plays a pivotal role in maintaining the agility and reliability of cloud data operations. Mastering these skills ensures a well-rounded approach to handling the intricacies of operational challenges in cloud data environments.

Las operaciones con datos en la nube ("DataOps" en la nube) implican prácticas, procesos y tecnologías para mejorar la calidad y reducir el ciclo de entrega de los datos. Este enfoque se centra en la automatización, la colaboración continua y la integración de los procesos de gestión de datos en entornos de nube. Algunas habilidades y conocimientos clave involucran la utilización de herramientas para automatizar la integración, transformación, y entrega de datos (ETL/ELT). Herramientas como Apache Airflow, Kubeflow, y otras son fundamentales en este aspecto Aplicar principios de CI/CD no solo al código, sino también a los conjuntos de datos y a los procesos de transformación de datos, con el fin de mejorar la eficiencia y reducir errores

**Essential Cloud Data Operations** Cloud data operations include tasks like data ingestion, storage, processing, querying, transformation, backup, security, and governance. These operations ensure efficient data management, accessibility, and security in cloud environments, enabling organizations to leverage their data assets effectively for insights and innovation.

Cloud Data Operations demand expertise in cloud platforms (AWS, Azure), data governance, and security. Proficiency in database management, big data technologies (Apache Spark), and scripting is vital. Docker and continuous learning ensure efficient, scalable operations. Effective communication facilitates collaboration in this dynamic landscape.

Cloud Data Operations: a. Monitoring and Optimization: Skill Requirement: Ability to monitor cloud data services, optimize configurations, and manage costs effectively. b. Data Governance: Skill Requirement: Implementing data governance practices, ensuring data quality, and compliance with privacy regulations. c. Scaling and Performance Tuning: Skill Requirement: Skills in scaling cloud data solutions and optimising performance for varying workloads.

What skills do you need to work with cloud data platforms? Exactly the same that you need to work with any other platform: desire to learn and effort. From here on, the prior knowledge of each one will determine the level of seniority. If you are going to start working in cloud environments for the first time, here is the only advice I would give you: always keep costs in mind. The main difference between a cloud and an on-premise environment is that costs can skyrocket. Before starting to work with large volumes, always learn the best practices from a process and cost optimization point of view of your chosen platform.

El análisis de datos en la nube implica la utilización de plataformas y servicios basados en la nube para recopilar, almacenar, procesar y analizar grandes conjuntos de datos. Esta modalidad ofrece ventajas significativas en términos de escalabilidad, flexibilidad y eficiencia en costos. Es importante la comprensión de herramientas y frameworks como Apache Hadoop, Spark, y Kafka, que son esenciales para el procesamiento y análisis de grandes volúmenes de datos en la nube. Conocimientos en lenguajes de consulta de bases de datos, como SQL y NoSQL. Habilidad para utilizar herramientas de análisis y visualización como Tableau, Power BI, Quicksight, o incluso bibliotecas de Python, para extraer insights y presentarlos de manera efectiva.

??Before use a data analytics resources; understand how these resources are aligned with data solutions: avoid to use a overcapacity or undercapacity tool in your proposal solutions. ?? In analytical case; as an operational case; want to be efficient cost while keep a good performance and available: get this criterio by understanding of analytical resources.

Cloud Data Analytics: a. Big Data Technologies: Skill Requirement: Familiarity with big data technologies on the cloud (e.g., Apache Spark, Hadoop). b. Machine Learning Integration: Skill Requirement: Understanding how to integrate machine learning models and analytics within cloud platforms. c. Data Visualization: Skill Requirement: Proficiency in data visualisation tools (e.g., Tableau, Power BI) for communicating insights.

1. Data Query Languages: SQL: Proficiently use SQL for querying and analyzing structured data stored in relational databases on the cloud. NoSQL Query Languages: Understand and use query languages associated with NoSQL databases (e.g., MongoDB query language, Cassandra Query Language) for analyzing non-relational data. 2. Data Exploration and Visualization: Data Exploration Tools: Utilize data exploration tools to interactively explore and understand the characteristics of your data. Data Visualization Tools: Use data visualization tools (e.g., Tableau, Power BI, Google Data Studio) to create compelling visualizations that communicate insights effectively. Data Analysis Frameworks: Apache Spark for Analytics

Here’s What Else to Consider: a. Continuous Learning: Consideration: Cloud platforms evolve rapidly, so a commitment to continuous learning and staying updated on new features and services is crucial. b. Collaboration and Communication: Consideration: Effective collaboration with cross-functional teams and clear communication of technical concepts to non-technical stakeholders. c. Problem-Solving Skills: Consideration: Strong problem-solving skills to address challenges related to data architecture, performance, and security. d. Certifications: Consideration: Pursuing relevant cloud certifications (e.g., AWS Certified Solutions Architect, Microsoft Certified: Azure Data Engineer) to validate skills and knowledge.

The cloud's a fast-moving data world, constantly evolving with new features and tools. Just like any field, staying ahead means embracing these advancements. Though major clouds offer similar core services like data lakes, warehouses, and orchestration, each shines in different areas. New versions unlock fresh integrations and capabilities. By being familiar with these ever-changing landscapes, you can choose the perfect cloud service for every data need, wielding those advantages like a data Ninja!

I recommend taking one of the cloud certifications. Because they take you through all the cloud concepts you need to apply while working with clouds. AWS solutions architect certification or the Google cloud engineer are relevant for this. I haven’t checked out Azure cert but I am sure it might cover the cloud concepts too. Coursera offers study materials and preparation for these certs so you can start learning there before applying for the exam.

In my experience I've found Google cloud very beginner friendly for all the use cases of data engineering. I see a very resolved mindset in the GCP. Everything is very intuitive. This probably is because Google itself has been using the products it offers itself for a long time now.