Recognize the promise of AWS’s fully managed services for code-free IT operations. Learn how to utilize these solutions to simplify IT administration, boost operational efficiency, and automate repetitive tasks without coding.
AWS is indeed a suite of fully managed services that can help IT organizations and companies create and run applications easily, with little to no infrastructural management necessary.
The following are core no-code services that simplify and minimize overhead for operations such as server and operating system deployment and management, database, analytics, etc.
Some of the biggest and most useful AWS fully managed no-code services are Auto Scaling for Amazon EC2, which lets you scale EC2 compute capacity, as discussed in this blog
Amazon EC2 Auto Scaling
go up or down automatically. Auto-scaling does not require creating instances ahead of time to be prepared for increased or decreased traffic and lets you set automated rules that will add or remove EC2 instances depending on such factors as CPU usage.
This is ideal for instances where the user is trained on an hourly, daily or seasonal basis. Auto-scaling provides users with the ability to scale resources up or down without the need to write any code or worry about the infrastructure.
Actually, IT teams only have to set the auto-scaling policies and AWS will easily scale the compute tier without much effort.
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Amazon Aurora Serverless
Amazon Aurora Serverless is a default option for the Amazon Aurora database, which automatically scales up or down based on the use of compute power, starts up, or shuts down if it is not required.
This relieves developers from capacity planning hassles and maintaining database servers for varied workload. The application traffic determines how the serverless model dynamically allocates resources for processing compute and memory.
The infrastructure is elastic, and when the application does not need the database, it is possible to deplete the capacity to zero. One of the concerns that developers do not have to deal with is the management of databases; AWS does this on their own.
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AWS Lambda
AWS Lambda offers the ability to perform automated computation by running application code that is designed to respond to a variety of events, such as changes in data, the state of the system, web events, errors, or other events generated by other AWS services.
Developers just upload the application code base and set up some triggers. AWS Lambda runs the code at scale while, at the same time, not requiring the user or organisation to manage any servers.
It can go from having no parallel executions defined to as many as thousands at any given time based on fluctuating traffic. This makes it suitable for applications that are not constantly busy and require a burst of processing power here and there, such as chatbots, Customer Relationship Management systems, real-time data processing, etc.
Amazon S3
Amazon S3 provides an easy-to-use storage infrastructure that automatically scales storage capacity and file downloads; it handles management of infrastructure components such as replication and data tiering, servicing out requests.
According to defined policies, S3 is capable of increasing or decreasing the storage, computing, and networking capacity in an effort to align itself with the set performance standards and cost containment goals.
That allows IT teams to avoid the situation when they have to provision storage infrastructure to start with and then constantly manage to scale on their own.
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AWS Glue
AWS Glue is a serverless ETL tool that helps simplify the integration of data between data storage applications. It has an auto-utility provisioning mechanism, which means that the service takes care of the volume of usage and there is no need for the client to scale resources on his/her own.
Auto-provision Apache Spark environments and manage storage resources for moving data between sources and destinations at the highest data throughput possible. Additional automation and optimisation features provided by the machine learning capabilities help to fine-tune the process of data integration.
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Amazon Athena
Amazon Athena is a server-less SQL-based interactive query service that lets you start an ad-hoc query over data and operates in S3. It operates on S3 files on the back end and can scale up or down based on the number of queries, thus freeing teams from maintenance of any servers and infrastructure.
To perform the queries, Athena employs presto engine while automatically scaling the instance depending on the query difficulty. By using the S3 engine, developers and analysts are able to run standard SQL queries on unstructured, structured, or semi-structured datasets without the need to set up servers or infrastructure.
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Amazon EMR
Amazon EMR, or Amazon Elastic MapReduce, is essentially a Hadoop framework service that disseminates computational work across multiple EC2 instances. EMR is an effective solution that eliminates the management overhead of deploying, sizing and optimizing open-source big data frameworks such as Apache Hadoop, Spark, HBase, etc.
With reference to the number of workloads, the EMR clusters can also scale out to meet the high volume requirements usually demanded by data sets for analysis use cases. Finally, after processing the information, such environments self-adjust to reduce the provisioned resources and minimize costs.
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To sum up:
In AWS full service is fully managed, so IT teams can work on objectives rather than engage in Menial activities. The ability to provision resources automatically and grow capacity without requiring additional intervention greatly cuts operational overhead and allows developers to create new solutions at a fast pace.
The use of such no-code automation services will, however, prove even more essential with AWS’ growing portfolio to manage IT flexibility and costs.
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