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Section : 4 UMY : Design and Implement Data Storage

64 : Building a Fact Table :  We have connected to SQL Database and Azure Synapse Datawarehouse using the SSMS We are going to build a fact table based on the "SalesOrderDetail" Table & "SalesOrderHeader" Table.  once thing we can do is create a View based on the joint of both of these tables within my SQL database itself and then I can create a new table within my SQL database and copy that information using the pipeline tool in Azure synapse on to my SQL Datawarehouse  If you don't want to impact your Auze SQL database as it is a production based database what you can do is use the pipeline tool to copy the data that you want to Azure synapse we are going to look into that approach when we come to Azure Data Factory .
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ETL - Real time pipelines : Databricks & Pyspark: Real Time ETL Pipeline Azure SQL to ADLS

AWS : Real time data pipeline https://www.youtube.com/watch?v=P4kjorNGC1w Real-Time Spark Project |Real-Time Data Analysis|Architecture|Part 1| DM | DataMaking | Data Making https://www.youtube.com/watch?v=NFwNKkIkN6o&list=PLe1T0uBrDrfOYE8OwQvooPjmnP1zY3wFe   
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ETL -- Building Batch Job Pipeline

 ETL : https://www.youtube.com/watch?v=hK4kPvJawv8   Building a Batch Data Pipeline using Airflow, Spark, EMR & Snowflake  --  
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Data Engineer Interview Questions

  General Data Engineering Concepts: What is the role of a Data Engineer in an organization? The role of a Data Engineer in an organization is to design, develop, and manage the data architecture, infrastructure, and tools necessary for collecting, storing, processing, and analyzing large volumes of data. Here are some key responsibilities and aspects of the role: Data Architecture Design: Designing and implementing data architectures that support the organization's business goals and data requirements. Database Management: Creating and maintaining databases, whether they are traditional relational databases or NoSQL databases, ensuring optimal performance and scalability. ETL (Extract, Transform, Load) Processes: Developing and optimizing ETL processes to efficiently extract data from various sources, transform it to meet business needs, and load it into the data warehouse or other storage systems. Data Modeling: Designing and implementing data models that represent the structure ...
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DE Skills and Terms and Definition

  Apache Spark is an open-source, distributed computing system that provides a fast and general-purpose cluster-computing framework for big data processing and analytics. It was developed to address the limitations of the MapReduce computing model, which is widely used for processing large-scale data in distributed environments. Here are some key features and components of Apache Spark: Speed: Spark is designed to be fast and supports in-memory processing. It can perform data processing tasks up to 100 times faster than traditional MapReduce frameworks by caching intermediate data in memory. Ease of Use: Spark provides high-level APIs in languages such as Scala, Java, Python, and R, making it accessible to a wide range of users with different programming skill levels. It also includes a built-in set of higher-level libraries for various tasks, such as Spark SQL for SQL-based querying, Spark Streaming for processing real-time data, MLlib for machine learning, and GraphX for graph ...
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Data Engineering Skills

  To be a successful data engineer, you should possess a combination of technical, analytical, and communication skills. Here is a list of skills that are commonly needed for a data engineering role: Database Management: Proficiency in working with relational databases (e.g., MySQL, PostgreSQL, Oracle) and NoSQL databases (e.g., MongoDB, Cassandra). SQL (Structured Query Language): Strong command of SQL for querying, updating, and managing databases. ETL (Extract, Transform, Load): Experience with designing, implementing, and optimizing ETL processes to move and transform data between systems. Programming Languages: Proficiency in at least one programming language, such as Python, Java, Scala, or Ruby, for scripting and automation. Big Data Technologies: Familiarity with big data processing frameworks such as Apache Hadoop (HDFS, MapReduce) and Apache Spark. Data Modeling: Ability to design and implement effective data models, including understanding of dimensional modeling and nor...
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Data Engineer Stack -- Terms & Definition

A Data Engineer is responsible for designing, developing, and managing the data architecture, infrastructure, tools, and processes needed to collect, process, and analyze large sets of data. Here are some key skills typically required for a Data Engineer role: Programming Languages: SQL: Proficiency in SQL is essential for querying and manipulating data in relational databases. Python/Java/Scala: These languages are commonly used for developing data processing pipelines, ETL (Extract, Transform, Load) jobs, and other data engineering tasks. Big Data Technologies: Hadoop: Understanding the Hadoop ecosystem, including HDFS, MapReduce, and Hive, is valuable for handling large-scale distributed data processing. Spark: Apache Spark is widely used for big data processing and analytics, providing a faster and more flexible alternative to traditional MapReduce. Data Modeling and Database Design: Relational Databases: Knowledge of relational database concepts, schema design, and optimizati...
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