AWS Data Engineering manages Data Pipelines, Data Transfer, and Data Storage. But before we get into the discussion, let’s talk a bit about cloud platforms and the inception of Data Engineering process to manage the cloud services.
Organizations are shifting to Cloud Platforms to achieve their daily tasks and manage all their data. Cloud Services change the game of managing data and applications. With the increase in Data Analytics for research, many Cloud Services emerged to provide the best user experience at a reasonable price. It reduced the complexity and time for businesses and helps them to focus more on business growth.
AWS Data Engineering contains one of the core elements of AWS in providing the complete solution to users. Now, let’s delve right into the article for more insight. You can also check our article for the best data migration tools.
Introduction to AWS
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AWS or Amazon Web Services is an on-demand cloud service provider to organizations and individuals. It’s a subdivision of Amazon that provides various Infrastructure, Hardware, and Distributed Computing facilities. AWS support includes Infrastructure as a Service (Iaas), Platform as a Service (PaaS), and Software as a Service (SaaS), and other enterprise-level storage and computing services.
Cloud computing services like AWS, Microsoft Azure, Google Cloud, and Alibaba Cloud offer cost-saving options by charging on a pay-per-use basis, unlike complex and costly on-premise solutions.
Amazon Web Services provides various services such as networking, monitoring tools, database storage, data analytics, and security. AWS uses virtual machines for diverse applications and offers auto-scaling to adjust resources as needed. Multiple Availability Zones ensure data redundancy and minimize the risk of data loss.
Introduction to AWS Data Engineering
Many services like AWS, Google Cloud, Microsoft Azure provide ready-to-use infrastructures. Engineers with expertise in Big Data, Data Analytics manage all the services, optimization, and deliver requests to users. Let us first understand what is Data Engineering?
Data Engineering is the process of analyzing user requirements and designing programs that focus on storing, moving, transforming, and structuring data for Analytics and Reporting purposes.
AWS Data Engineering focuses on managing different AWS services to provide an integrated package to customers according to their requirements. It analyzes the customer’s needs, the amount and type of data they have, and the result of their operations. They also decide the best tools and services so that customers can use them to have optimal performance.
Extracting data from multiple sources to store it in the Storage Pool (Data Lake, Data Warehouse) is managed through Data Pipelines. Data Engineering with AWS also ensures that data present in Data Warehouse is in the analysis-ready form to their users.
AWS Data Engineering goes through many processes that use many different tools designed by AWS for specific requirements.
In this section, you will learn about working with the AWS Data Engineering Tools and the process followed to achieve a final result.
1) Data Ingestion Tools
Data Ingestion Tools extract different types of raw data such as Logs, Real-time Data Streams, text from multiple sources like Mobile devices, Sensors, Databases, APIs, etc. This heterogeneous data need to be collected from sources to store in a Storage Pool. AWS provides various Data Ingestion Tools to collect data from all data sources. The Data Ingestion process is the most time-consuming task in AWS Data Engineering.
1a) Amazon Kinesis Firehose
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- Kinesis Firehose is used to deliver fully managed real-time streaming data to Amazon S3. Kineses Firehose can also configure the transformation of data before storing it in Amazon S3. Kinesis Firehose supports Encryption, Compression, Lambda Functions, and Data Batching features.
- It is auto-scalable and depends on the volume and yield of the streaming data. Lambda Functions can transform incoming data from the source to the desired structure before loading it to Amazon S3.
- AWS Data Engineering leverages Kinesis Firehose to provide seamless Data Transfer following Data Encryption.
1b) AWS Snowball
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- Snowball is the ultimate tool to deliver your enterprise data from on-premise databases to Amazon S3. To solve the problem of replicating data from on-site data sources to Cloud Storage, AWS uses a Snowball device to ship to the data source location and then connects it to the Local Network.
- You can transfer data from local machines to the Snowball device. It also supports AES-256-bit Encryption. Organizations can ship back the device to AWS and transfer data to Amazon S3.
1c) AWS Storage Gateway
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- Many companies have running on-site machines that are essential for daily tasks, but they may need regular data backup on Amazon S3. Fortunately, AWS Data Engineering features a Storage Gateway that allows organizations to transfer data from on-site data sources to Amazon S3 using the File Gateway configuration of Storage Gateway. It uses an NFS (Network File System) connection to share data to Amazon S3.
- NFS is a Distributed File System Protocol that allows you to share files over the network to the Amazon S3.
- You can configure file-sharing settings from the AWS Storage Gateway Console and initiate file-sharing between Amazon S3 and on-premise machines.
2) Data Storage Tools
After the Data Extraction process, all the data get stored in Data Lakes or Storage Pools. AWS provides different storage services based on the requirement and mode of Data Transfer. With the right knowledge of AWS Data Engineering, one can select the best-suited Data Storage service for that specific task.
Data Storage Tools are essential for users to deliver High Power Computation (HPC) solutions. Amazon Web Services provides various storage solutions based on the requirements. These Data Storage solutions are cost-efficient and easily integrable with other applications for processing. It can collect data from many sources and transform it into specified Schema.
Amazon S3
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Amazon S3 stands for Amazon Simple Storage Service. Amazon S3 is a Data Lake that can store any amount of data from anywhere over the internet. Amazon S3 is commonly used in AWS Data Engineering for Data Storage from multiple sources because it’s a highly scalable, fast, cost-effective solution.
S3 stores data in Objects, which are fundamental entities that consist of data and its metadata. Objects stores pairs where metadata is the description of respective data like Date has Date-Time description.
Amazon S3 is a cost-effective Data Storage solution to store data without any upfront hardware costs. It also gives you the freedom to replicate your S3 storage to multiple Availability Zones. You can set up Recovery Points Objectives and Recovery Time Objectives for robust Data Backup and Restore features.
You can efficiently run web-based cloud apps that can automatically scale with flexible configurations. With AWS Data Engineering, Amazon S3 allows you to run Big Data Analytics for better insights.
3) Data Integration Tools
Data Integration Tools combines data from multiple sources to a centralized view with the process of ETL (Extract Transform Load) or ELT (Extract Load Transform).
The process executed with Data Ingestion Tools is also a part of Data Integration. AWS Data Engineering looks to Data Integration as the most time-consuming task because it needs analysis of different sources and their Schema and takes time to move data.
AWS Glue
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AWS Glue is a serverless Data Integration Service that helps in collecting data from different sources, which is Data Ingestion. It is also responsible for handling the Data Transformation to the desired Schema before loading it to a Data Lake or Data Warehouse.
As mentioned earlier, Data Lakes are Storage Pools that can store data in the original structure, so it is optional to perform Data Transformation while loading data. But Data Warehouses requires a uniform Schema to run fast queries, Analytics, and Reporting.
AWS Data Engineering uses the power of AWS Glue to provide all the functionalities from extracting data to transforming it in a uniform Schema. It manages the Data Catalog that acts as a central repository of metadata. AWS Glue is capable of handling tasks to complete in weeks rather than months.
4) Data Warehouse Tools
A Data Warehouse is a storehouse or repository for structured and filtered data from multiple data sources. But if it also stores data from various sources, what makes it different from Data Lakes such as Amazon S3?
Data Lakes collect raw data from multiple data sources in original or transformed structures. The data stored in Data Lakes have no purpose defined yet whereas, Data Warehouses stores data with a specific purpose in a uniform Schema for query optimization.
Amazon Redshift
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Amazon Redshift is an ultimate Data Warehouse solution that provides Petabytes of data storage in a structured or semi-structured format. AWS Data Engineering ensures fast querying to run Data Analytics on a massive volume of data and feed data to different Business Intelligence Tools, Dashboards, and other applications. For this reason, Amazon Redshift follows a uniform Schema across all data.
With the help of AWS Glue, Amazon Redshift loads data from Amazon S3 after the Data Transformation process. Amazon Redshift allows massively parallel processing (MPP) that provides high computational power for processing exabytes of data.
AWS Data Engineering makes it possible for Data Analysts, Data Scientists to run queries from Amazon S3 using Amazon Redshift Spectrum. It saves time to move data from S3 to Amazon Redshift. But this method is feasible if it’s required few times. It is better to transfer data from Amazon S3 to Amazon Redshift if it needs regular fast querying for Analytics and Reporting.
5) Data Visualization Tools
Finally, the last part of AWS Data Engineering is Data Visualization. It is the main reason for which an AWS Data Engineer works. The Data Visualization Tools contains a package of BI tools powered with Artificial Intelligence, Machine Learning, and other tools to explore data.
All the data from the Data Warehouse and Data Lakes act as inputs for the tools to generate reports, charts, and insights from data.
Advanced BI tools powered with Machine Learning provide deeper insights from data and help users find relationships, compositions, and distribution in data.
Amazon QuickSight
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Amazon QuickSight is Amazon’s best tool that can easily help you create BI Dashboards within a few clicks. It is capable of delivering Machine Learning insights. You can use Amazon QuickSight from a Web browser, Mobile device or embed QuickSight dashboard in websites, portals, or from various applications. AWS Data Engineering also focuses on integrating Amazon Redshift to many Business Intelligence and Business Analytics tools.
Skills Required to Become a Data Engineer
The need for experts in AWS Data Engineering and Data Analytics is increasing as the average generation of data increases. Many surveys and reports indicate a shortage of supply of Certified Data Analytics Engineers.
This field requires Certified AWS Data Analytics and AWS Data Engineer Certification with the practical hands-on cloud platform.
To gain AWS Certified Data Analytics skills, one should focus on the below-listed points:
- Know the core differences and use cases of different storage services by AWS to select the best-suited storage utility based on requirements.
- Have hands-on practice to manually migrate data between Amazon Redshift clusters and Amazon S3.
- Know to query data from multiple tables in Data Warehouse and Data Lake.
- Be familiar with the Data Integration process and AWS tools.
- AWS Glue for ETL, AWS Athena for querying in storage, and QuickSight for Analytics and BI dashboards.
Apart from the above points, one must go through the documentation, courses, and practice more to get more knowledge on AWS Data Engineering.
Conclusion
This article gave you information about AWS Data Engineering, the process of Data Engineering, and the best tools commonly used in AWS Data engineering.
Companies use various data sources and platforms for completing daily tasks. They require the best tools to reduce workload and costs.
Aditya Jadon is a data science enthusiast with a passion for decoding the complexities of data. He leverages his B. Tech degree, expertise in software architecture, and strong technical writing skills to craft informative and engaging content. Aditya has authored over 100 articles on data science, demonstrating his deep understanding of the field and his commitment to sharing knowledge with others.
