Introduction

In the evolving landscape of cloud computing and big data, data engineers play a critical role in enabling organizations to transform raw data into actionable insights. This article explores key concepts, tools, and workflows essential to modern data engineering—from cost-effective cloud migration strategies to the intricacies of data ingestion, transformation, and real-time analytics.

You will gain insights into:

  • Cloud benefits like pay-as-you-go pricing and reduced operational overhead

  • ETL vs. ELT processes and their roles in handling structured, semi-structured, and unstructured data

  • Azure services such as Azure Data Factory, Synapse Analytics, Azure Blob Storage, Cosmos DB, and Event Hub

  • NoSQL database types including key-value stores, document databases, graph databases, and column stores

  • Streaming data processing and the use of APIs like Gremlin for graph models

Whether you’re working with traditional relational databases or implementing advanced streaming pipelines, this guide highlights the practical knowledge and cloud-native tools every data engineer needs to build scalable, secure, and intelligent data platforms.

Practical Tips

  • One benefit of Cloud environments requires no capital investment. T- You pay for a service or product as you use it i.e. pay-as-you-go pricing. Moving servers and services to the cloud also reduces operational costs.
  • Extract, Transform and Load (ETL) is a typical process for ingesting data from an on-premises database to an on-premises data warehouse.
  • Unstructured data differ from structured data in many features: Schema on read, Storage in Data Lakes, and Native Format are  features of unstructured data
  • Each data source has unique data formats one of which  can be structured, semi-structured, or unstructured.
  • A benefit of ELT is that you can store data in its original format, be it JSON, XML, PDF, or images.
  • Another benefit of ELT is that it reduces the time required to load the data into a destination system. 
  • ELT also limits resource contention on the data
  • A data engineer’s scope of work goes well beyond looking after a database and the server where it’s hosted. Data engineers must also get, ingest, transform, validate, and clean up data to meet business requirements.
  • ELT is a typical process for ingesting data from an on-premises database into the cloud.
  • During the extraction process, Data Engineers must define the Data Source after defining the data.
  • Azure Cosmos DB is a globally distributed, multimodal database,  and can be deployed using several API models such as Cassandra API, MongoDB API, and SQL API.
  • Structured data is typically stored in a relational database such as SQL Server or Azure SQL Database.
  • Azure SQL Database is a service that runs in the cloud.
  • Azure Cosmos DB can offer sub-second query performance.
  • Azure Cosmos DB will provide applications with guaranteed low latency and high availability anywhere, at any scale, or migrate Cassandra, MongoDB, and other NoSQL workloads to the cloud.
  • Azure Blob Storage is primarily for unstructured data but can also store semi-structured data.
  • Azure Synapse Analytics is an integrated analytics platform, which combines data warehousing, big data analytics, data integration, and visualization into a single environment.
  • Azure Data Catalog is the best choice to store documentation about a data source.
  • Key-Value store stores key-value pairs of data in a table structure. They are a type of NoSQL database.
  • Graph database finds relationships between data points by using a structure that’s composed of vertices and edges. They are a type of NoSQL database.
  • Data Engineers can create data-driven workflows in the cloud for orchestrating and automating data movement and data transformation.
  • Azure Data Factory (ADF) is a cloud integration service that orchestrates that movement of data between various data stores.
  • Processing data in real-time, is called streaming data.
  • Audio files are a type of unstructured data.
  • In stream processing, each new piece of data is processed when it arrives.
  • The Gremlin API, a Cosmos DB API, works with Graph Databases.
  • Stream Analytics Job and Synapse Analytics are used to process data.
  • The purpose of data ingestion is to capture data flowing into a data warehouse as quickly as possible.
  • Data engineers configure ingestion components of Azure Stream Analytics, by configuring  data inputs from  sources including Azure Event Hubs, Azure IoT Hub, or Azure Blob Storage.
  • Big data streaming service is a feature of  Azure Event Hub.
  • Graph database, Column database, Document databases, and  Key-value store are types of NoSQL databases.
  • Synaps SQL offers both serverless and dedicated resource models to work with both descriptive and diagnostic analytical scenarios.
  • For predictable performance and cost, you should  create dedicated SQL pools to reserve processing power for data stored in SQL tables.
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