13 Useful AWS Analytic Services: Quick High-Level Overvie

The AWS platform provides many useful analytic services to help perform different types of analysis and insights using all types of data such as big data, unstructured, semi-structured, and structured data.

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Amazon Athena Service

Let’s start with the Amazon Athena service. Athena is a serverless analytic service.  Serverless means you don’t need to set up or manage any infrastructure to use the service. Furthermore, Athena is an interactive query service to analyze data directly on S3 by using standard SQL.

So, what would be a typical use case of Athena? If your use case is to query data on S3 for ad-hoc analysis, use the Amazon Athena service.

Amazon RedShift Service

The next one is the Amazon Redshift service. Amazon Redshift is an AWS data warehouse solution where you can query and load exabytes of structured and semi-structured data. The Redshift service is fast and scalable. Furthermore, It is simple to use as you use standard SQL.  Additionally, It is cost-effective as well. 

So, if your use case requires running simple and cost-effective analytic across massive structured and semi-structure data, the Amazon RedShift service could be a good solution choice.

Amazon EMR Service

Another one is the Amazon EMR service. You can think of EMR as Hadoop and Spark cluster solution on the cloud. So, If you have a use case that requires Hadoop or Spark cluster on AWS, this is your service.

Amazon CloudSearch Service

The next service is the Amazon CloudSearch service. Amazon CloudSearch is an AWS solution to search documents. For example, if you have a use case where you would like to have your documents searchable, this service could be a good solution choice.

Amazon Elasticsearch Service

Another service is Amazon Elasticsearch. The Elasticsearch service is also a solution to search documents similar to CloudSearch. There are some differences, though.

The main highlighted difference is that CloudSearch is a managed service. In other words, in CloudSearch, you don’t need to set up and manage servers. On the other hand, for the Elasticsearch service, you will have to set up and manage servers to use the service.

Amazon Kinesis Service

The next analytic category service is the Amazon Kinesis service. The Kinesis service essentially collects, process, and analyze data streams in real-time. Moreover, it is a collection of three services: Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.

So, If you have a use case of real-time data ingestion and consumption on AWS, you will look for Kinesis.

Amazon QuickSight Service

The next is QuickSight service. Amazon QuickSight is a fast business analytic service. It makes it easy for you to build visualizations, perform ad-hoc analysis on uploaded files.

Additionally, you can also perform analysis on databases like SQL Server, MySql, and PostgreSQL. Furthermore, you can perform analysis by ingesting data on Amazon RDS, Redshift, or S3.

So, if you have a  use case that requires building and sharing analytic dashboard(s) using data sets from files, databases, RDS, Redshift, or S3,  Quicksight is your solution choice.

Amazon Data Pipeline Service

Another analytic category service is the Data Pipeline service. It helps you move, integrate, and process data across AWS compute and storage resources, including on-premises resources.

As a result, this service can be used to build an ETL data pipeline. So, if your use case is to build an ETL data pipeline, the AWS Data Pipeline service could be a good solution choice.

Amazon Data Exchange Service

The next service is AWS Data Exchange. AWS Data Exchange is a compelling analytic service to build analytic or ML solutions. It does it by finding, subscribing, and using data sets from more than 80 qualified data providers. 

For example, the service uses data from Reuters, TransUnion, IMDb, Pitney Bowes, etc. So, if you have a use case to build an analytic or ML solution by using data from multiple sources, this is your solution service.

Amazon Glue Service

The next service is AWS Glue. AWS Glue is a serverless data integration service that you can leverage to build analytic or ML solutions.

The best part of the AWS Glue — in my opinion — is its flexibility, features, and connectivity to various AWS services. 

For example, it allows you to develop Spark jobs using Scala and Python by connecting to different data sources. In addition to this, you can also configure jobs to schedule to run as ETL data pipelines. Essentially, you can build and also schedule  ETL jobs using AWS Glue.

Amazon Lake Formation Service

The next AWS analytic category service is AWS Lake Formation. AWS Lake Formation makes it much easier to build data lakes. Instead of building data lakes in months, you can build in days from a dashboard with just a few clicks.

You point to data lake formation at the data sources that you want to move into lake formation. And AWS does the heavy lifting of crawling the schemas and setting up the right metadata tags.

Additionally, it can also perform cleaning, partitioning, indexing, and deduping the data. As a result, storing and accessing data becomes cost-effective and quick.

Finally, Lake Formation helps data analysts and data scientists as it actually puts in a catalog in a much easier to manage way.

Amazon Managed Streaming for Apache Kafka

Another AWS  service is Amazon Managed Streaming for Apache Kafka, also called Amazon MSK.  Essentially MSK is a fully managed, highly available, and secure Kafka Service. 

When using MSK, you will not have the operational overhead of managing Kafka's environment.  Additionally, MSK manages the provisioning, configuration, and maintenance of resources within MSK clusters.

So, If you have a use case where you need to build Kafka applications on AWS, you can use MSK.

AWS Glue DataBrew

And the final analytics type service on the list — as of this recording  — is AWS Glue DataBrew. AWS Glue DataBrew is a visual data preparation tool that enables users to clean and normalize data without writing any code. It has around 250 pre-built transformations to automate data preparation tasks.

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