You're aiming for a promotion in data engineering. What's the one skill you need to develop?

1) Create a PoV for any data related problem. 2) Cross polinate with your peers and clients 3) Generate revenue from your idea. 4) Promotion will follow you :)

Observe the pain points that exist for your leader, try to solve them in your capacity and do little more. Pain points can be in platform, technical, functional or cross functional departmental. Not everything you will be able to solve by yourself, few things will be driving and collaboration and solving the issue. Promotions/good bonuses are given for such players.

Additionally, data pipeline orchestration involves coordinating the execution of various tasks within the pipeline, ensuring they are executed in the correct order and with the necessary dependencies satisfied. This orchestration process often includes scheduling, parallelization, and error handling to maintain the integrity and efficiency of the overall data workflow.

Penso que para se destacar é necessário conhecer plataformas em nuvem como #Azure, #AWS e #GCP..., #python e sem dúvida #SQL! Um diferencial pode ser dominar técnicas de modelagem de dados e otimiza??o de código SQL. Espero ter ajudado.

Data pipeline orchestration automates, monitors, and manages workflows and dependencies in data pipelines. These pipelines transform raw data into valuable insights for analysis, reporting, or machine learning. Orchestration ensures smooth, efficient, and error-free pipeline execution without manual intervention.

By scheduling and coordinating tasks, orchestration ensures that data processes are executed at optimal times. This includes considering dependencies, resource availability, and overall system load. This efficient time management helps meet deadlines and provides timely insights.

Data pipeline orchestration is crucial as it facilitates scaling up data engineering projects and managing diverse data sources and destinations. With rising data volume, variety, and velocity, coordinating multiple pipelines becomes essential. Orchestration prevents bottlenecks, failures, and inconsistencies, ensuring timely and high-quality data products.

Would you rather: a. Run all data pipelines manually and in the correct order. b. Have a drink while the computer does this for you. Pick an answer.

Data pipeline orchestration encompasses a blend of technical and conceptual skills that can be honed through learning and practice. Key components include data modeling, integration, quality, and governance. Data modeling entails designing and implementing the structure and schema of data sources and destinations like databases or warehouses. Integration involves ETL or ELT processes, extracting, transforming, and loading data using tools such as Apache Airflow or AWS Glue. Ensuring data quality is vital for accuracy and consistency, achievable with tools like Apache Spark or AWS DataBrew. Lastly, data governance entails establishing policies for pipeline security, privacy, compliance, and documentation.

Data pipeline orchestration offers numerous benefits to data engineering projects and careers. These include: 1) Increased efficiency by automating repetitive tasks. 2) Enhanced reliability with error handling, logging, alerting, and testing mechanisms. 3) Scalability through distributed processing, cloud computing, and containerization. 4) Innovation by exploring new data sources, experimenting with transformations, and analyses. 5) Accelerated delivery of superior data products to stakeholders.

Nobody will ever ask you this question in an interview and if they do, walk away, they don't know what they are talking about.

Always consider the cost associated with orchestration. Most orchestrators require an environment and generally your pipelines will only be running throughout the night because the business needs the data readily available during the day. So how do you manage costs when your environment is up all the time but only in use 20-40% of the time?