![]() ![]() ![]() This has proven true as AI takes over repetitive tasks and amplifies areas where humans uniquely excel like creativity, relationships, and strategic thinking. He grasped that these technologies would make knowledge workers more productive versus displacing them. Tools like intelligent assistants, chatbots, recommendation engines, and contextual collaboration platforms now help automate and enhance workplace interactions.Įqually forward-thinking was Wang's perspective that AI would augment human capabilities rather than replace people. His vision that AI would handle nuances in communication and ensure the right information reaches the right people is very much the reality today. Though physical spaces may stay similar, he correctly predicted how AI would transform workflows, information access, and connections between people. Mutt Data: Future Past // In this 1987 interview, Fred Wang showed remarkable insight about the future impact of AI on offices and communication. Additionally, ethical concerns like plagiarism, bias, and the potential for misuse of AI-generated content must be addressed.Īs technology advances, the collaboration between AI and human creativity will likely shape new and innovative ways of designing, writing, creating videos, and developing chatbot experiences. It's important to note that while AI has revolutionized these areas, there are challenges to consider.įor instance, AI-generated content may sometimes lack the nuanced understanding that human creators possess. Here's how AI has impacted each of these areas: □□ □□□□□ □□□ □□□□ □□□ □□□□ĪI has significantly contributed to various creative and communication processes, including designing, writing, video creation, and chatbot development.Īfter the massive disruption of Chatgpt, many AI tools have emerged and gained a lot of attention. #softwareengineering #cloudcomputing #programming #data #technology #bigdata #dataanalytics #dataengineering □ If you like my posts, please follow me here Rocky Bhatia - and hit the □ on my profile to get notifications for all my new posts. I will cover each component and technical area in detail in future posts. Please let me know if i missed anything in comment In real-time visualization kibana, Prometheus, superset is also popular. □□□□ □□□□□□□□□: ingest massive quantities of event data and provide low-latency queries, Druid □□□□□ □□□□□□□□ : Cloud computing is an integral part of data engineering one should learn different tools offered by cloud based on how your company uses the cloud □□□□□ □□□□ : Delta Lake is becoming increasingly popular for building big data applications ,Delta from data bricks is widely popular □□□□□□□ : Massive amount of information gets to store, learn HDFS and at least one storage from cloud provider. □□□□□□□ □□□□□ - one must learn one message queue, we frequently use it, Kafka is the defacto. Hive is popular ,but based on what your company uses, you could at least learn 1 Data warehouse solution. □□□□ □□□□□□□□□: it centralizes and consolidates large amounts of data from multiple sources. ![]() □□□□□□□□□: SQL and NoSQL their schemas are predefined or dynamic, how they scale, the type of data they include and whether they are more fit for multi-row transactions or unstructured data. □□□□□□- processing of a continuous stream of data immediately as it generates.įlink for real-time processing, Spark for micro-batch streaming Spark is the de facto standard, Hadoop is still popular □□□□□ - processing a large volume of data all at once To start your career as a Data engineer, you should learn SQL and at least one more programming language out of python,scala and java. It makes it possible to take vast amounts of data, translate it into insights, and focus on the production readiness of data and things like formats, resilience, scaling, and security. □□□□ □□□□□□□□□□□ Involves a rich understanding of large distributed systems on which data solutions rely. □□□□ □□□□□□□□□□□ is a role that requires building systems to process data efficiently and to model the data to power analytics. ![]()
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