8 o’clock | Big Data & Cloud Computing
As one advances in data science, gaining an appreciation for big data engineering and cloud computing becomes increasingly valuable. Understanding tools like Hadoop, Spark, and cloud platforms such as AWS, Google Cloud, and Azure can be highly beneficial.
Hadoop is an open-source framework that enables distributed storage and processing of vast amounts of data across clusters of machines. It’s fundamental for big data because:
- It allows parallel processing of huge datasets.
- It stores data in a distributed manner using HDFS.
- It’s often used for batch processing, handling structured & unstructured data efficiently.
Apache Spark
Spark is a powerful big data processing tool that builds on Hadoop’s ecosystem but is much faster for certain workloads. Some key advantages:
- In-memory computing speeds up data processing significantly compared to Hadoop.
- Supports real-time and stream processing, making it ideal for fast analytics.
- Integrates well with machine learning frameworks like MLlib & TensorFlow.
Cloud Platforms (AWS, Google Cloud, Azure)
Cloud platforms have transformed how businesses and individuals interact with computing resources. Learning cloud-based services is crucial because:
- They offer scalability – you can increase or decrease computing power based on demand.
- Provide managed services for storage, databases, AI, and security.
- Offer tools like Amazon EMR (Elastic MapReduce) and Azure Synapse Analytics, which integrate well with Hadoop and Spark.
- Serverless computing options like AWS Lambda and Google Cloud Functions eliminate infrastructure management concerns.
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Yun.Bun 