Top 5 AWS Serverless services for Data Engineering
From the well-known Elastic Compute Cloud (EC2) and Simple Storage Service (S3) to platform as a service (PaaS) offerings encompassing practically every facet of modern computing, Amazon Web Services (AWS) offers a bewildering number of cloud services.
AWS’ sophisticated significant data architecture includes services that cover the whole data processing pipeline, from intake to treatment and pre-processing, ETL, querying and analysis, visualization, and dashboarding. Without building up expensive infrastructure or deploying software solutions like Spark or Hadoop, AWS allows you to manage big data quickly and easily.
Top 5 AWS Serverless services for Data Engineering
1. Amazon EMR
The Amazon Elastic MapReduce (EMR) managed cluster platform removes most of the complexity associated with running big data frameworks such as Apache Hadoop and Spark. You may use it to process and analyze large amounts of data on AWS resources, such as EC2 instances and spot instances available at a low cost. You may also use Amazon EMR to alter and move large amounts of data between databases hosted on Amazon Web Services (such as DynamoDB) and other data stores (such as S3).
2. Amazon SageMaker
This fully managed MLOps solution enables you to create, train, and deploy machine learning (ML) models directly to a production environment. You may access data sources using a Jupyter notebook instance without having to manage servers.
SageMaker comes with built-in machine learning algorithms optimized for big data in distributed situations and the ability to add your custom algorithms. To deploy your model into a scalable, secure environment, use the SageMaker Console or SageMaker Studio. Costs for data training and hosting are computed based on actual consumption, and there are no upfront or minimum payments, as with most Amazon services.
3. Amazon Kinesis Video Streams
Organizations are shifting to video for most of their content creation and management, necessitating the processing and analysis of video content. Amazon Kinesis Video Streams is a fully managed service for streaming live video to the AWS Cloud, real-time video processing, and batch-oriented analytics.
You can use the service to store video data, watch live feeds, and access video information in real-time as it is uploaded to the cloud. You can use Kinesis Video Streams to capture massive amounts of live data from millions of devices.
This contains video and other types of data like thermal imagery and audio. This data may be accessed and processed quickly by your applications. You may also use Kinesis in conjunction with various video APIs to further process and treat video content. Kinesis can be set up to retain data for a specific time and encrypt it in transit.
4. AWS Glue
AWS Glue is a data management service that helps extract, transform, and load (ETL). It allows you to classify, clean, enrich, and transfer data fully managed and cost-effective. AWS Glue is a serverless platform with a Data Catalog, a scheduler, and an ETL engine that automatically generates Scala or Python code.
AWS Glue processes semi-structured data and generates dynamic frames for ETL scripts. You can use dynamic frames to organize your data because they are a type of data abstraction. They support Spark data frames and provide schema flexibility and powerful transformations. Discover data sources, transform data, and monitor ETL processes with the AWS Glue console. The AWS Glue API can also access Glue from AWS services or other applications.
You tell AWS Glue what ETL actions you want it to do to transport data from the source to the target. You can schedule jobs in response to a defined trigger or run them on demand. You can either submit a script via the console or API or use the script that AWS Glue generates for you. Crawlers can be defined to scan sources in a data repository and add metadata to the Data Catalog.
5. Amazon QuickSight
This cloud-based business intelligence (BI) service is completely managed. Amazon QuickSight compiles data from a variety of sources into a single dashboard. It offers high security, built-in redundancy, global availability, and administrative features for managing big groups of users. You may get started right away without having to deploy or manage any infrastructure.
QuickSight dashboards can be accessed securely from any mobile or network device. Amazon QuickSight allows you to obtain data, prepare it for analysis, and save it as a direct query or SPICE memory (QuickSight’s Super-fast Parallel, In-Memory Calculation Engine). Add current or new datasets, create charts, tables, or insights, use enhanced tools to add variables, and publish the study as a dashboard to users.
We have made an effort to present the readers of this post with a comprehensive summary of the serverless data engineering capabilities offered by AWS. We did not go over every service; instead, we concentrated on the most well-known and important ones. You may construct highly effective data processing solutions with only a few clicks, and these solutions can automatically handle massive data sets and thousands of requests simultaneously. Because AWS handles the laborious aspects of keeping your data, you are free to concentrate on what matters: finding technological solutions to challenges faced by businesses.
FAQ Related to Top 5 AWS Serverless services for Data Engineering
Which AWS serverless service allows you to prepare data for analytics?
AWS Glue, for example, is a serverless data integration service that simplifies data preparation for analytics, machine learning, and application development. AWS allows you to query data from many data sources such as databases, data lakes, and data warehouses.
What do data engineers do in AWS?
AWS Data Engineering is one of the main components of AWS in providing users with a complete solution. AWS Data Engineering is in charge of managing Data Pipelines, Data Transfer, and Data Storage. This tutorial will teach you about the AWS Data Engineering process and tools.
What is AWS enlist any five services provided by AWS?
Compute, storage, database, migration, network, and content delivery, management tools, and security and identity compliance are some of the most frequently used areas of Amazon Web Services.