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SQL For Data Engineering

SQL, or Structured Query Language, is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS). It is widely used in both industry and academia for managing and manipulating data in databases. SQL is standardized by the ANSI (American National Standards Institute) and ISO (International Organization for Standardization) organizations, although different database management systems (DBMS) may implement additional features beyond the standard SQL commands. SQL is a fundamental tool for anyone working with data stored in relational databases, offering powerful capabilities for data manipulation, retrieval, and management.

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Data Bricks

Databricks is a cloud-based unified analytics platform designed for data engineering, machine learning, and data science. It integrates Apache Spark with an easy-to-use collaborative workspace, enabling teams to build data pipelines and advanced analytics at scale.

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AWS (Analytical services only)

Amazon Web Services (AWS) offers a comprehensive suite of analytical services that empower data engineers to ingest, process, store, and analyze large volumes of data efficiently. These services are highly scalable, cost-effective, and deeply integrated within the AWS ecosystem, making them ideal for building modern data pipelines and analytics platforms.

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Python

Python is one of the most popular and versatile programming languages used in data engineering. Its simplicity, rich ecosystem of libraries, and ability to integrate with various data sources make it an ideal choice for building scalable and efficient data pipelines.

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Spark

Apache Spark is one of the most powerful and widely used big data processing frameworks in modern data engineering. Designed for speed, scalability, and ease of use, Spark helps data engineers build robust, distributed data pipelines that can handle large volumes of data efficiently.

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Kafka

Apache Kafka is a powerful, distributed event streaming platform that plays a critical role in modern data engineering workflows, especially for systems that require real-time data processing and high-throughput pipelines.

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Pyspark framework

In the era of big data, traditional data processing tools struggle to handle the volume, velocity, and variety of modern datasets. Apache Spark emerged as a powerful distributed computing framework to solve this problem, and PySpark is its Python API that allows data engineers to work with Spark using familiar Python syntax PySpark is a powerful tool for data engineers, offering scalable, efficient, and fault-tolerant processing of large datasets using Python.

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