Enterprises are increasingly moving towards cloud platforms to achieve business objectives and optimize their business operations including data management. Not only these services have transformed the game of managing data and applications, but many Cloud Services have also dished out brilliant user experience at an inexpensive cost with the addition of Data Analytics for research. It further leads to the simplification of processes, allowing organizations to focus more on business growth.
Several Data Engineering processes have come into the picture for the seamless management of Cloud Services. Names like Google Cloud, AWS, and Microsoft Azure have designed proper Cloud Infrastructure for organizations and individuals. To provide a seamless experience to users, these Cloud Platforms use several solutions such as Data Migration, Data Engineering, and Data Analytics.
AWS Data Engineering encompasses one of the core elements in the AWS data platforms which provide a complete solution to users. It also manages data pipelines, transfers, and storage.
For instance, to transform data into a uniform schema, AWS Engineering utilizes AWS Glue to give out all the functionalities. Moreover, it handles the Data Catalog that poses as a central repository of metadata. AWS Glue is capable of handling tasks completed in weeks rather than months.
On the other hand, Data Visualization, with an apt representation of data using interactive charts, graphs, and tables, plays an important role in AWS Data Engineering.
All the information from the Data Warehouse and Data Lakes poses as inputs for the tools to generate reports, charts, and insights from AWS data tools. Simply explained, data warehouse stores and uses structured data that is ready for strategic analysis while a data lake uses and stores both structured and unstructured data for use in the future.
Advanced BI tools powered by Machine Learning provide deeper insights from data and help users find relationships, compositions, and distribution in data.
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.
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.
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.
There has been a phenomenal increase in the volume of data that is being generated by businesses and consumers. Organizations are looking at solutions to help manage, process, and optimally utilize this data. As a result, AWS Data Engineering came into the picture which can package and handle all the requirements of the customers as per their needs.
An AWS Engineer is expected to analyze the customer requirements and propose an integrated package that can provide an optimal performance ecosystem to the organization.
AWS Data Engineering is also used to ensure that data presented to the end users are in an analysis-ready form and can deliver the right insights.
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:
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:
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.
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.
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.
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, 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.
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 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.
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 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 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 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.
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.
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:
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.
An organization comprises several components and people. As this article aimed to explain, AWS Data Engineering, the process of Data Engineering, and the best tools commonly used in the process, one needs to understand that enterprises need to select the best tools to reduce workload and costs.
AWS Data Engineering involves data collection from several data sources and creates pipelines that help data to travel. It is a job that requires skill and expertise and can solve the problem of a No-code Data Pipeline solution. Furthermore, it automates the process of loading data from multiple sources to the destination Data Warehouse.