Leveraging the power of the cloud, enterprises have adapted the cloud platforms for enhancing their business agility and for managing their organization in a better way. Cloud-based services have been offering an optimal user experience, data management, and data analytics. Organizations are now finding time to focus on their key working areas rather than spending time managing and analyzing data.
Many popular cloud platforms like AWS, Google Cloud, and Microsoft Azure have been building a comprehensive cloud-driven infrastructure, offering solutions like Data analytics, data engineering, data migration, etc.
One such popular technology is AWS which offers AWS Data Engineering as an end-to-end solution for proper management, storage, and transfer of data channels. It also offers data visualization through insightful dashboards and reports that have significant value.
Before we go into details of what is AWS Data Engineering, let us glance through the fundamentals of data engineering, AWS, and then move ahead.
What is Data Engineering?
Data Engineering is a specialized stream in the world of data that creates data pipelines to create valuable information that aligns well with company objectives. It includes the transportation of data in a format and the transformation of data bulks into valuable information that can offer insight into the futuristic world of business.
Data engineering is the procedure to design and create pipelines for transporting data into a utilizable state. All the data is stored in data warehouses as a unified source of information. Data engineering works as a base that gets data analytics and data science in a single frame using data processing techniques and modern-day technologies.
Data engineer perceives the intricacies of software engineering and database management, and they operate independently in collaboration with other team members.
What is AWS?
Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis.
AWS is a popular cloud service provider that offers distributed computing, infrastructure, and hardware facilities to users. It offers services categorized into Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). It is meant to enhance organizational performance with the least costs involved and maximum efficacy in infrastructure management.
AWS offers various services like data analytics, data warehousing, data engineering, cloud computing, database storage, etc. It emphasizes offering development tools and infrastructure involving database, storage, computing, mobility, and management services.
AWS has played an increasingly important role in the cloud services industry, bringing in a significant amount of revenue for Amazon. From being launched in 2006, revenues of AWS surpassed 62 billion U.S. dollars in 2021. AWS offers a wide range of cloud-based products, including databases, analytics, management tools, security, IoT, enterprise applications, and developer tools.
What is AWS Data Engineering?
Powered by the technicalities of AWS and the fundamental of data engineering practices, comes the concept of AWS Data Engineering. It is aimed at managing and packaging data-related needs of clients with data optimization, how bulky the data may be, in such a format that users can easily extract insightful information from it.
The skilled team of AWS resources harnesses cutting-edge technologies for getting effective results in all phases of data engineering. AWS Data Engineering manages various AWS services to offer an integrated package that suffices all user requirements, including the tools that must be availed. It ensures that the data got by users from different data stores and warehouses is all set to be analyzed promptly, without any further action.
AWS data engineers create data models from different sources, maintain data integrity through backup processes, enhance database performance, extract future trends and patterns, maintain current applications, design security measures, increase storage capacity and suggest changes in infrastructure, as needed. They must be skilled in SQL, cloud computing, data warehousing, machine learning, and business intelligence.
Offering a competitive, stable, and secure application that has a faster go-to-market time is what AWS data engineers aim at. They support a fast drive to production through the AWS Cloud and yet maintain the trustworthiness and security of the application.
Top AWS Services Essential for Data Engineers
The AWS product family offers a lot of tools especially meant to perform effective data engineering tasks. Here are the most used AWS Data Engineering tools that perform specific functions, depending upon requirements:
DATA INGESTION TOOLS
These tools are leveraged for extracting data from disparate sources and then storing them in requisite locations.
- Amazon Kinesis Firehose – Offers real-time data streaming, and configuration of data transformation prior to storage on the S3. Supports encryption, data batching, and compression. Utilized in the smooth transfer of data.
- AWS Snowball – Manages data from the on-premises database with the help of a snowball method for transferring data to the source location connecting with the network. Offers encryption service with the competence to transfer data from local devices.
- AWS Storage Gateway – This tool smoothens the usage of on-site devices for routine tasks with routine S3 backup and the use of a network file system. It makes use of configuring the File Gateway on the Storage Gateway for doing the functionality.
DATA STORAGE TOOLS
Once the data is extracted and transferred, it is stored in a data lake or a data warehouse. AWS offers the right kind of data storage tools based on user requirements.
- Amazon S3 – S3 stands for Simple Storage Service. This is a data lake that can consume and store voluminous data from different sources. As a part of Amazon Data Engineering, it offers fast, scalable, and cost-effective solutions.
DATA INTEGRATION TOOLS
These tools execute in the ETL or ELT mode and demand analysis from various sources for data to move in certain directions. Data integration is an activity that calls for all the data that has been accumulated till now.
- AWS Glue – This tool undergoes data integration from different sources and loads them in a certain schema prior to getting loaded onto a data warehouse or lake. It takes care of many functionalities and data extraction from different sources for a specified schema.
DATA WAREHOUSE TOOLS
Data warehousing tools maintain a storehouse of data – structured and filtered data from disparate sources.
- Amazon Redshift – It is one of the best data warehousing solutions that offer Petabytes of data storage in a semi-structured or structured form. It empowers users to undergo MPP with high computational capabilities for a huge amount of data.
DATA VISUALIZATION TOOLS
These tools use the data and offer them in a visually appealing manner such that users find it interactive and engaging. Data from different business units can be extracted with AI and ML methods and different reports and charts can be generated.
- Amazon QuickSight
These tools can create a dashboard in a fast manner with the help of AI and ML techniques. Data can be extracted from different websites, portals, and different applications.
As We Wind Up
Our skilled data engineers are well-versed with cloud services like AWS and can design and develop visually appealing dashboards and reports for better business decisions. Data engineering is a primary service area of ours, with multi-industry experience and we, as data-driven leaders, assist organizations in leveraging their data bulks for futuristic and insightful business, along with leading cloud-based platforms like AWS.
Are you looking for robust data engineering practices in your team, with the help of cloud computing? AWS Data Engineering is an apt option, and you are at the right place. Contact us for any data-related requirement of yours.
Note: This Post Was First Published On https://ridgeant.com/blogs/aws-data-engineering-guide/
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