5/17/2023 0 Comments Metabase aws![]() ![]() ![]() I’ll share my curated findings with you in a convenient bullet-point format, so you can easily bookmark, enjoy, and learn over the weekend. So, I'm excited to announce my brand-new LinkedIn micro newsletter: Data Stream Weekly!Įvery Friday, I'll deliver the week's best data engineering content and forum discussions – all handpicked just for you. □□ Data engineers will transition to being advisors and enablers of automation, focusing on more strategic tasks as tools and practices evolve.Īny other present or future trends that are missing?ĭata professionals on LinkedIn, as someone who spends several hours a week reading and analyzing the latest developments in the industry because of my job, I thought, why not share my findings with you? □□ Titles and responsibilities may shift as software engineering and data engineering become more intertwined, with software engineers potentially needing to be well-versed in data engineering. □□ Improved data management will result from the adoption of #dataOps, a practice focused on improving communication, integration, and automation of data flows across an organization. □□ The gap between data producers and consumers will narrow, with #datacontracts fostering closer collaboration between stakeholders. ![]() □□ Specialization within data engineering will increase, giving rise to new roles such as analytics engineers and data reliability engineers. □□ Data engineering trends point to increased abstraction, simplification, and maturity of the field, with tools decreasing in complexity while adding more functionality and features. □□ The data engineer's role today encompasses the entire data engineering process, requiring familiarity with the data engineering lifecycle and evaluating data tools for optimal performance across various dimensions. ![]() □□ Concepts like the semantic layer help maintain a common understanding of data across systems. □□ #Python and #SQL are the essential languages for data engineers. □□ The shift towards data products and the data mesh architecture emphasizes the idea of data as a product. □□ Concepts like functional data engineering, idempotency, and declarative programming have been adopted in data engineering for better pipeline operability and observability. □□ Data engineers adopt software engineering best practices, including agile development, code testing, and version control. □□ Contemporary data engineers focus on higher-value tasks such as data modeling, quality, security, management, architecture, and orchestration due to data tools' abstraction of Big Data frameworks. □□ The title of "big data engineer" was mostly replaced by the broader term "data engineer." □□ The modern data stack emerged in the early 2010s, with cloud data warehouses like #Redshift, #BigQuery, and #Snowflake, along with tools for orchestration (Airflow), transformation (dbt), and BI (Looker, Metabase, Mode, etc.). □□ The advent of public cloud providers (AWS, Google Cloud, Microsoft Azure) revolutionized software and data applications, allowing companies to save on up-front costs and easily scale resources. □□ The early 2000s saw the rise of Big Data, driven by tech giants like Google, Yahoo, and Amazon, and the development of the Hadoop framework. □□ Data warehousing started in the 1980s, leading to scalable analytics through MPP databases. A concise exploration of #dataengineering: 18 bullet points unveiling its past, present, and future ✨ ![]()
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