AWS offers several analytics services to help process and provide insights from data. These include Amazon Athena for interactive querying of data stored in S3 using SQL, Amazon EMR for processing large amounts of data using Hadoop and other open source tools, Amazon CloudSearch for setting up a search solution easily, and Amazon Kinesis for collecting, processing, and analyzing real-time data. Other services are Amazon Redshift for data warehousing, Amazon Quicksight for interactive dashboards, AWS Glue for ETL jobs, and Amazon Lake Formation for securing data lakes.

1. What are AWS Analytics
Services?
It is a service offered by Amazon that helps in processing and
providing insights to data by analyzing it. There are many
services in the analytics section which have been discussed
below:
https://forms.office.com/r/DKwT0JCEUZ

2. Amazon Athena
• An interactive query engine which helps in easy analysis of data stored in S3 with the help of
SQL queries. It is server-less (start query instantly), hence having no infrastructure that needs
to be managed. The user only pays for the queries they run ($5 per terabyte scanned by your
query).
• Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start
querying using standard SQL. Most results are delivered within seconds. With Athena, there’s
no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone
with SQL skills to quickly analyze large-scale datasets.
• Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a
unified metadata repository across various services, crawl data sources to discover schemas
and populate your Catalog with new and modified table and partition definitions, and
maintain schema versioning.

3. Amazon EMR
• An Industry-leading cloud big-data platform for processing huge amount of data using open
source tools such as Apache Hadoop, Spark, Hive, Hbase, and Presto etc.
• It makes it easy to setup, operate, and scale your big data environments by automating time
consuming tasks like provisioning capacity and tuning clusters.
• It provides a Hadoop managed framework, which helps in quick and cost-effective method of
processing large amounts of data across on-the-g- scalable EC2 instances. EMR notebooks are
similar to Jupiter notebooks, and provide an environment to develop and collaborate for
dynamic querying and exploratory analysis if data.
• It is highly-secure and offers reliability to handle a wide range of uses cases, which include,
but are not limited to machine learning, scientific simulation, log analysis, web indexing, data
transformations (ETL), and bioinformatics.

4. Amazon CloudSearch
• It is a service managed by AWS Cloud,
• It makes the process of setting-up, managing and scaling a search solution for a website or an
application an easy task.
• It supports 34 languages and comes with features such as highlighting, auto-complete, and
geospatial searchability.
• With ease of operation feature: features like auto-scaling, self-healing clusters, high
availability and data durability
• Easy administration: (security) built in integration with IAM and (auditing) built-in integration
with Cloudtrail.
• You don’t have to become a search expert or worry about h/w provisioning, setup and
maintenance.
• Offers pay-as-you-go pricing with no upfront expenses or long-term commitments.
• Search endpoint, document endpoint, Document ARN, Searchable documents

5. Amazon ElasticSearch service
• It helps ElasticSearch in the process of deploying, securing, operating and scaling data by
searching, analysing and visualizing this data in real-time.
• It comes with easy-to-use APIs which can be used for log analysis, full-text search, scaling
requirements, monitoring applications, clickstream analytics, and security.
• It can also be integrated with open-source tools such as Kibana, Logstash which help in
ingesting data and visualization of data respectively.
• It’s a open source search engine, writen in java language and is based on Apache Leucin.
• It is real-time distributed & analytics engine
• It supports full-text search instead of table/scheme.
• It is document oriented

6. Amazon Kinesis
• It makes the process of collecting, processing, and
analysing real-time data an easy task. This helps in
achieving real-time insights to data so that this data
can be analysed and actions can be taken based on the
insights quickly. Real-time data including video, audio,
application logs, website clickstreams, IoT telemetry
data (meant for machine learning) can be ingested and
this data can be responded to immediately in contrast
to waiting for all the data to arrive before beginning
pre-processing on it. It currently offers services like-
Kinesis Data Firehose, Kinesis Data Analytics, Kinesis
Data Streams, and Kinesis Video Streams.

7. Amazon redshift
• It is a quick, and scalable data warehouse which
helps in analysing user data in a cost-efficient and
simple manner. It delivers 10 times quicker
performance in comparison to other data
warehousing services, since it uses machine
learning, massively parallel query execution and
columnar storage on high-performance disks.
Petabytes of data can be queried upon, and a
new data warehouse can be setup and deployed
in a matter of minutes.

8. Amazon Quicksight
• It is a cloud powered business intelligence service, which is fast, and helps in delivery insights
to people in an organization. It allows users to create and publish interactive visual
dashboards which can be accessed via mobile devices and browsers. These visual dashboards
can be integrated with other applications thereby providing customers with powerful self-
serving analysis service.
• Functions of Quicksight (Business Intelligence tool)
• Performance management: -
• Data wrangling – Converting the data into a structured format (acquising, combining,
cleansing)
• Data mining – ETL
• Data analytics
• Data warehouse -
• Predictive modelling

9. AWS data pipeline
• It helps process and move data between different
AWS resources (compute and storage devices).
Data can be regularly accessed from the place it is
stored, it can be transformed and processed at
scale. The result of this data processing can be
transferred to other AWS services such as S3,
RDS, DynamoDB, and EMR. It helps in the
creation of complex data processing workloads
which provide facilities such as high fault
tolerance, high availability and repeatability.

10. AWS Glue
• It is a completely managed ETL service (Extract,
Transform, Load) which helps users prepare and
load their data for the purpose of analysis. An ETL
job can be set up and run with a few mouse clicks
from the AWS Management Console itself. Glue
can be pointed to the location of data stored, and
it discovers the data and its metadata and stores
it in Glue Catalog. Once the data is in the catalog,
it can be searched, queried, and made available
for ETL process.

11. Amazon Lake Formation
• It is a service that helps in securing data lake. Data Lake
can be visualised as a centralized, customized and
secured data repository which stores this data in the
original form as well as a processed form meant for
data analysis. It helps combine various types of
analytics that help in gaining deeper insights to data
thereby helping make better business decisions. But
the process of setting up and managing a data lake
requires a lot of manual efforts. But Lake Formation
makes this process an easy one by collecting data and
cataloguing it. This data is then classified using ML
algorithms as well as providing security for sensitive
data.

12. Amazon Managed streaming
for Kafka (MSK)
• It is a service that helps in building and
running applications that use Apache Kafka. It
is fully managed and helps process streaming
data. Apache Kafka is open-source and is used
to build real-time streaming data pipelines
and application. With the help of MSK, Kafka
API can be used to populate data lakes, reflect
changes in the database and use machine
learning to power other applications.

13. Application Services

14. • Application Services (often
used instead of application
management services or
application services
management) are a pool of
services such as load
balancing, application
performance monitoring,
application acceleration,
autoscaling,
micro-segmentation, service
proxy and service discovery
needed to optimally deploy,
run and improve
applications.

15. What is Application Services
Management?
• The process of configuring, monitoring, optimizing and
orchestrating different app services is known as application
services management.
• Today, organizations with their own data centers or which
use the public cloud, handle applications services
management. In the early days of online
adoption, application service providers (or ASPs) were
companies which would deliver applications to end users
for a fixed cost. This single tenant, hosted model was
largely replaced by the advent of the Software-as-a-Service
(SaaS) delivery model which was multi-tenant and on-
demand.

16. What is Cloud Application Services?
• Cloud App Services are a wide range of
specific application services for applications
deployed in cloud-based resources. Services
such as load balancing, application firewalling
and service discovery can be achieved for
applications running in private, public, hybrid
or multi-cloud environments.

17. What are App Modernization
Services?
• Traditional applications were built as monolithic blocks of software. These
monolithic applications have long life cycles because any changes or updates to
one function, usually requires reconfiguring the entire application. This costly and
time consuming process delays advancements and updates in application
development.
• Application Modernization Services enable the migration of monolithic, legacy
application architectures to new application architectures that more closely match
the business needs of modern enterprises’ application portfolio. Application
modernization is often part of an organization’s digital transformation.
• An example of this is the use of a microservices architecture where all app services
are created individually and deployed separately from one another. This allows for
scaling services based on specific business needs. Services can also be rapidly
changed without affecting other parts of the application. Application-centric
enterprises are choosing microservices architectures to take advantage of flexible
container-based infrastructure models.

18. Thank You
Any Suggestions