Big Data & Cloud Technologies
PySpark:
PySpark is the Python API for Apache Spark, a powerful open-source data processing engine. It allows users to write Spark applications using Python, enabling scalable data processing, machine learning, and data analytics. PySpark provides high-level APIs for working with structured and unstructured data, making it a popular choice for big data processing, data science, and data engineering tasks. It’s known for its ease of use, flexibility, and high performance.
Cloud AWS (Amazon Web Services):
AWS (Amazon Web Services) is a comprehensive cloud computing platform providing a wide range of services, including:
1. Compute (EC2)
2. Storage (S3)
3. Database (RDS, DynamoDB)
4. Analytics (Redshift, EMR)
5. Machine Learning (SageMaker)
6. Security (IAM)
AWS enables businesses to build, deploy, and manage applications and workloads in a scalable, secure, and cost-effective manner, fostering innovation and growth.
Data Lake:
A Data Lake is a centralized repository that stores raw, unprocessed data in its native format, allowing for flexible and scalable data analysis. It can handle large volumes of structured, semi-structured, and unstructured data from various sources. Data Lakes enable organizations to store, manage, and analyze diverse data types, providing a single source of truth for data-driven insights and decision-making. They often utilize big data technologies like Hadoop, Spark, and cloud storage.
