As enterprise data moves to the cloud, things can feel both simpler and more complex at the same time. AWS helps align the data, making it easier to move, organize, and process for productive operations in the backend. This blog explores how AWS data engineering works with growing data streams and helps you convert them into usable insights.
As organizations generate increasing volumes of data, many enterprises are moving to cloud platforms to modernize their data infrastructure. Cloud ecosystems such as AWS, Microsoft Azure, and Google Cloud enable businesses to store, process, and analyze large datasets more efficiently than traditional on-premise systems.
However, simply migrating data to the cloud does not automatically create value. Organizations need robust data engineering frameworks to build reliable data pipelines, manage data transformation, and prepare data for analytics.
This is where AWS Data Engineering plays a critical role. AWS provides a comprehensive ecosystem of services that help organizations ingest, transform, and manage data at scale. Tools such as AWS Glue simplify data integration and automate data pipeline development.
Once data is processed and stored in data lakes or data warehouses, organizations can use BI and analytics tools to generate dashboards, reports, and actionable insights that support data-driven decisions. Let’s discuss what data engineering is, how AWS supports it, and the key AWS tools used to build modern data pipelines.
What is Data Engineering?
For us to understand Data Engineering, we have to understand the “engineering” part better. What do Engineers do? They design and build things. Therefore, data engineers can be thought of as people who design and make pipelines that change and help transport data in a format. This format helps the data to reach the Data Scientist or other users in a highly usable state.
These solutions collect data from several sources and accumulate them in a single warehouse that holds all the data as a single source of truth. Because of this central role in managing and structuring data, many organizations need data engineering services to handle growing data complexity and support analytics-driven decisions.
Over the years, the definition of Data Engineering has not changed much even though the technology and the tools have changed drastically. In simple words, Data Engineering is the foundation that holds data science and analytics together with the use of technology and data processing.
Moreover, while conventional technologies like relational and transactional databases still have a place in big data architecture, fresh tools and technology have created innovation in the space.
What is AWS?
AWS, short for Amazon Web Services, is an on-demand cloud service provider that has various offerings under its umbrella. The organization is a subdivision of Amazon that can provide infrastructure, distributed computing facilities, and hardware to its customers. The various offerings from the organization are known as Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS).
AWS competes with names like Microsoft Azure, Alibaba Cloud, and Google Cloud. All these organizations are focused on improving the performance of an organization and reducing costs at the same time. Most of these platforms charge their users on a per-use basis. In comparison, an organization need not invest in setting up and maintaining complex IT infrastructure for its requirements at its premises.
AWS data centers are located in various parts of the world and the customer has the choice to select the data center that is closest to their target customer. The various services offered by AWS include Security, Data Warehouse, Data Analytics, Cloud Computing, Database Storage, etc.
AWS data management allows auto-scale with which a user can scale up or down the requirements for storage and computing capabilities based on the requirements of the business.
What is AWS Data Engineering?
There has been a phenomenal increase in the volume of data generated by businesses and consumers. Organizations are looking for solutions to help manage, process, and effectively utilize this data. As a result, AWS data engineering solutions have emerged to address these needs by packaging and managing various data requirements for organizations.
An AWS engineer is expected to analyze customer requirements and propose an integrated solution that provides an optimal data ecosystem for the organization. Many enterprises also partner with specialized data engineering service providers to design and implement these architectures.
AWS Data Engineering also ensures that the data presented to end users is in an analysis-ready format, enabling them to derive meaningful insights.
AWS Data Engineering Tools
In recent times, we have seen several changes because of different tools designed by AWS for specific needs. The various tools used in the AWS ecosystem can be explained as follows:
Data Ingestion Tools
These tools are used to extract various types of raw data like text from multiple sources, real-time data, logs, etc which are then used to store in a storage pool. The data ingestion tools provide solutions with which users can collect data from multiple sources. It is one of the most time-consuming processes in the AWS Data Engineering cycle. The data ingestion tools provided by AWS are as follows:
Amazon Kinesis Firehose
The Kinesis Firehose tool from Amazon can deliver real-time streaming data to the S3 tool. It also can configure the data transformation before it is stored on the S3. Kinesis Firehose supports encryption, compression, and data batching features
The scalability and volume depend on the data streaming yield. Kinesis Firehose is used in the AWS ecosystem to provide a seamless transfer of encrypted data.
AWS Snowball
Snowball from AWS is an amazing tool that can handle enterprise data from on-premise databases to the S3 tool. To avoid data and effort duplication, AWS used a snowball technique that can be used to ship data to the source location and then make a connection with the local network. The encryption service along with the ability to transfer data from local machines makes it an effective solution for data transfer.
AWS Storage Gateway
Many organizations use on-site machines for day-to-day tasks which need regular S3 backup. The storage gateway makes it seamless with the use of a Network File System. It uses the configuration of File Gateway on the Storage Gateway to perform this function.
Data Storage Tools
After the data extraction and transfer process are complete, the data extracted is usually stored in a data warehouse or data lake. The various storage solutions offered by AWS are based on the mode of data transfer and storage requirements. The right knowledge of the AWS ecosystem helps to identify the data storage tools as per requirements.
Identifying the right data storage tools is required to achieve high-power computation solutions. The data storage solutions from AWS can be integrated easily with other applications. At the same time, it is capable of collecting data from different applications and integrating it all into a specific schema.
The various data storage tools are as follows:
Amazon S3
Amazon S3, short for Simple Storage Service, is a data lake that can include any volume of data from anywhere on the internet. It is usually deployed as part of Amazon Data Engineering for data storage from multiple sources because of its speed, scale, and cost-effectiveness.
You do not need to invest in buying any hardware to use Amazon S3 for data storage. With AWS Data Engineering, you can run Amazon S3 and deploy AWS tools for data analytics.
Data Integration Tools
The data integration tools from AWS can work in the Extract Transform Load (ETL) or Extract Load Transport (ELT) model. The process completed in the Data Ingestion activity is also a part of the Data Integration exercise. AWS Data Engineering considered data integration as the most time-taking activity because it needs analysis from different sources and schema takes time to move data.
AWS Glue
AWS Glue integrates multiple source data and loads it to a particular Scheme before it is made part of a Data Warehouse or Data Lake. It is one of the fastest data integration solutions available in the market that can handle tasks in weeks and not months. The key advantage of using AWS Glue is the fact that it can provide all functionalities and extract data from multiple sources to put data in a specific Schema.
Data Warehouse Tools
A data warehouse is a repository of structured and filtered data that has been collected from various sources. It is different from a Data Lake because the latter collects raw data in original or transformed form. However, the former stores structured and filtered data. AWS tools list for the data warehouse is as follows:
Amazon Redshift
Amazon Redshift is among the best data warehousing solutions available in the market. It provides Petabytes of data storage in a structured or semi-structured format. AWS Data Engineering ensures that the use of other tools like S3 and Glue is done seamlessly to conduct big data analytics in an organization.
Amazon Redshift allows you to experience massively parallel processing (MPP) which provides high computational power for processing massive amounts of data.
Data Visualization Tools
Data visualization uses the stored data and presents them in an easy-to-understand and interactive format. With solutions like artificial intelligence and machine learning, all data from various business processes are used to generate charts, reports, and insights. The data visualization solution in the AWS suite are as follows:
Amazon QuickSight
Amazon QuickSight can create a BI dashboard in just a few clicks. It can deliver insights using machine learning and artificial intelligence. It can be used from a website, portal, or various applications.
What does Data Engineering with AWS mean?
Many case studies and research papers state the use cases of Data Engineering with AWS. One of the papers highlighted the use of the solution through a monthly report system with which a client was pushing data. However, even though the report gave the client the exact things that they needed, they could not move further with all the data they had accumulated. However, through this Data Engineering process, one could build a house of data with automated pipelines and built-in data checks for processing, where the data went before being sent to the reporting system.
Moreover, as the client added this feature to their already established data architecture, it also increased their capabilities and their access to the original data set which further allowed them to respond to ad hoc questions that center around cost-effectiveness and profits. From this, we can understand that while big corporations do use data and analytics as a part of their regular business, mixing the right technology, and integrating newer tools, can also permit you to leverage information for comprehensive results.
Several other companies across the world are harnessing the capabilities of AWS solutions by building with data engineering.
What are the skills required to become a Data Engineer?
As the average data generation increases, the need for specialists in the field of AWS Data Engineering and Data Analytics will grow further. As per several reports, there is a shortage of supply of Certified Amazon Data Analytics Engineers. This field requires Certified AWS Data Analytics and Certified Data Engineering with a practical hands-on cloud platform.
To gain AWS Certified Data Analytics skills, one should concentrate on the below-listed points:
- Understand the main differences and applications of dissimilar storage services by AWS to choose the best-suited storage utility based on requirements.
- One needs to have the ground experience to manually transfer data between Amazon Redshift clusters and Amazon S3.
- One needs to understand and query data from multiple tables in Data Warehouse and Data Lake.
- The individual needs to get accustomed to the Data Integration process and AWS tools. AWS Glue for ETL, AWS Athena for querying in storage, and QuickSight for Analytics and BI dashboards.
Along with the above-mentioned points, an individual needs to go through the documentation, courses, and practice more to get more knowledge on AWS Data Engineering.
Conclusion
An organization comprises several components and people. As this article aims to explain AWS data engineering, the data engineering process, and the tools commonly used, enterprises need to understand the importance of selecting the right tools to reduce workload and costs.
AWS data engineering involves collecting data from multiple sources and building pipelines that enable data to move efficiently across systems. It requires strong technical skills and expertise and can also address challenges related to no-code data pipeline solutions. Furthermore, it automates the process of loading data from multiple sources into a destination data warehouse.
