serverless workloads fall under 2 categories connecting your platform or running services. Connecting your platform involves writing a piece of code that when invoked performs a function. ie a code that invokes a lambda function to change a file format on a file uploaded to an S3 bucket.
Running services benefit from the flexibility of custom server configuration and the ability to perform multiple tasks.AWS has serverless analytics services that make it easier to store and analyze any type or size of data.
with AWS serverless analytics services you are able to perform all the big data data analysis methods like descriptive, prescriptive, predictive & diagnostic analytics methods in a serverless. way with the below offerings.
With these serverless AWS services, you can query, warehouse, analyze integrates and even perform business intelligence on your data with absolute ease. Let’s look into the services.
- Amazon Athena– serverless interactive query service built on open-source frameworks supporting open table and file formats. Athena is able to analyze petabytes of data without it being transferred be it on-premises or in the cloud. it uses SQL and python and supports Trino and presto engines and Apache spark frameworks.
Amazon Athena has new features launched in Dec 2022 like amazon Athena for Apache spark which allows you to run interactive analytics for Apache spark all under 1 sec.
Its use cases are:} through its many integrations and APIs you can use Athena to analyze google analytics data.
Run federated SQL queries on data in relational, and non-relational, object both on-premises and in the cloud.
2. Amazon EMR big data platform that makes it easier for analysts and engineers to run petabyte scale open source big data analytic frameworks in a serverless way.
Some use cases include: performing big data analytics
Build scalable data pipelines
Process real-time data streams
3. Amazon Kinesis makes it easy to collect, process and analyze streaming video and data streams in real time.to get timely insights and be able to react quickly.
4. Amazon redshift-fully managed petabyte-scale cloud data warehouse. it uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes.
Redshift is used by tens of thousands of AWS customers to analyze exabytes of data every day.AWS says that it has 5X better performance than similar offerings made by its rivals like GCP or Azure.
During re-invent, 2022 amazon announced redshift integration for apache spark which will enable developers to run spark queries on redshift data from EMR glue and sage maker within seconds.
It supports popular language frameworks like(java,python,r &scala)
Some of its major customers include Nasdaq and GE.
5. AWS glue-modern serverless data integration platform that makes it easier to discover mover and integrate data from multiple sources for analytics, it also offers 1 million objects stored for free in its free tier. With AWS glue users can efficiently and cost-effectively build modern data pipelines for analytics, application development and machine learning.
With AWS glue, you only pay for the resources used while your job is running
Used widely by data scientists, engineers and analysts with flexible support for all types of workloads.
6. Amazon quick-sight-business business intelligence platform that provides virtualization and ad hoc analysis in a quick easy to understandable format.. quick sight connects to your data sources wherever they are be it was, spreadsheets, big data sources or 3rd party sources both on the cloud or on-premises and transforms the data into rich interactive dashboards.
7. Amazon OpenSearch -distributed open-source platform for search and analytics. OpenSearch is a successor of elastisearch and in reinvent 2022 it was announced that it had joined AWS serverless analytics offerings.
OpenSearch enables you to easily ingest, search, aggregate, view, and analyze data for a number of use cases such as log analytics, application search, enterprise search, and more.
